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  • 1.
    Ambarki, K.
    et al.
    Umeå University, Sweden .
    Lindqvist, T.
    Umeå University, Sweden .
    Wahlin, A.
    Umeå University, Sweden .
    Petterson, E.
    SyntheticMR ABE, Linköping, Sweden .
    Warntjes, Marcel Jan Bertus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Birgander, R.
    Umeå University, Sweden .
    Malm, J.
    Umeå University, Sweden .
    Eklund, A.
    Umeå University, Sweden Umeå University, Sweden .
    Evaluation of Automatic Measurement of the Intracranial Volume Based on Quantitative MR Imaging2012In: American Journal of Neuroradiology, ISSN 0195-6108, E-ISSN 1936-959X, Vol. 33, no 10, p. 1951-1956Article in journal (Refereed)
    Abstract [en]

    BACKGROUND AND PURPOSE: Brain size is commonly described in relation to ICV, whereby accurate assessment of this quantity is fundamental. Recently, an optimized MR sequence (QRAPMASTER) was developed for simultaneous quantification of T1, T2, and proton density. ICV can be measured automatically within minutes from QRAPMASTER outputs and a dedicated software, SyMRI. Automatic estimations of ICV were evaluated against the manual segmentation. MATERIALS AND METHODS: In 19 healthy subjects, manual segmentation of ICV was performed by 2 neuroradiologists (Obs1, Obs2) by using QBrain software and conventional T2-weighted images. The automatic segmentation from the QRAPMASTER output was performed by using SyMRI. Manual corrections of the automatic segmentation were performed (corrected-automatic) by Obs1 and Obs2, who were blinded from each other. Finally, the repeatability of the automatic method was evaluated in 6 additional healthy subjects, each having 6 repeated QRAPMASTER scans. The time required to measure ICV was recorded. RESULTS: No significant difference was found between reference and automatic (and corrected-automatic) ICV (P greater than .25). The mean difference between the reference and automatic measurement was -4.84 +/- 19.57 mL (or 0.31 +/- 1.35%). Mean differences between the reference and the corrected-automatic measurements were -0.47 +/- 17.95 mL (-0.01 +/- 1.24%) and -1.26 +/- 17.68 mL (-0.06 +/- 1.22%) for Obs1 and Obs2, respectively. The repeatability errors of the automatic and the corrected-automatic method were less than1%. The automatic method required 1 minute 11 seconds (SD = 12 seconds) of processing. Adding manual corrections required another 1 minute 32 seconds (SD = 38 seconds). CONCLUSIONS: Automatic and corrected-automatic quantification of ICV showed good agreement with the reference method. SyMRI software provided a fast and reproducible measure of ICV.

  • 2.
    Bertus Warntjes, Marcel Jan
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Blystad, Ida
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Tisell, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Obtaining Double Inversion Recovery and Phase Sensitive Inversion Recovery Images without additional Scan Time2014Conference paper (Other academic)
  • 3.
    Blystad, Ida
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Warntjes, Jan Bertus Marcel
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Landtblom, Anne-Marie
    Linköping University, Department of Clinical and Experimental Medicine, Neurology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    SyntheticMRI compared with conventional MRI of the brain in a clinical setting: a pilot study, ESMRMB 2012, Lisbon, Portugal.2012Conference paper (Other academic)
  • 4.
    Blystad, Ida
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Warntjes, Jan Bertus Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Landtblom, Anne-Marie
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Neurology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Larsson, Elna-Marie
    Uppsala University, Sweden .
    Synthetic MRI of the brain in a clinical setting2012In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 53, no 10, p. 1158-1163Article in journal (Refereed)
    Abstract [en]

    BACKGROUND:

    Conventional magnetic resonance imaging (MRI) has relatively long scan times for routine examinations, and the signal intensity of the images is related to the specific MR scanner settings. Due to scanner imperfections and automatic optimizations, it is impossible to compare images in terms of absolute image intensity. Synthetic MRI, a method to generate conventional images based on MR quantification, potentially both decreases examination time and enables quantitative measurements.

    PURPOSE:

    To evaluate synthetic MRI of the brain in a clinical setting by assessment of the contrast, the contrast-to-noise ratio (CNR), and the diagnostic quality compared with conventional MR images.

    MATERIAL AND METHODS:

    Twenty-two patients had synthetic imaging added to their clinical MR examination. In each patient, 12 regions of interest were placed in the brain images to measure contrast and CNR. Furthermore, general image quality, probable diagnosis, and lesion conspicuity were investigated.

    RESULTS:

    Synthetic T1-weighted turbo spin echo and T2-weighted turbo spin echo images had higher contrast but also a higher level of noise, resulting in a similar CNR compared with conventional images. Synthetic T2-weighted FLAIR images had lower contrast and a higher level of noise, which led to a lower CNR. Synthetic images were generally assessed to be of inferior image quality, but agreed with the clinical diagnosis to the same extent as the conventional images. Lesion conspicuity was higher in the synthetic T1-weighted images, which also had a better agreement with the clinical diagnoses than the conventional T1-weighted images.

    CONCLUSION:

    Synthetic MR can potentially shorten the MR examination time. Even though the image quality is perceived to be inferior, synthetic images agreed with the clinical diagnosis to the same extent as the conventional images in this study.

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    Synthetic MRI of the brain in a clinical setting
  • 5.
    Blystad, Ida
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Department of Medical and Health Sciences, Radiology.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Helmersson, Teresa
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Contrast assessment of Synthetic Magnetic Resonance Imaging in clinical practice2011Conference paper (Refereed)
  • 6.
    Blystad, Ida
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). KTH Royal Institute Technology, Sweden.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Larsson, Elna-Marie
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Uppsala University, Sweden.
    Tisell, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Quantitative MRI for analysis of peritumoral edema in malignant gliomas2017In: PLOS ONE, E-ISSN 1932-6203, Vol. 12, no 5, article id e0177135Article in journal (Refereed)
    Abstract [en]

    Background and purpose Damage to the blood-brain barrier with subsequent contrast enhancement is a hallmark of glioblastoma. Non-enhancing tumor invasion into the peritumoral edema is, however, not usually visible on conventional magnetic resonance imaging. New quantitative techniques using relaxometry offer additional information about tissue properties. The aim of this study was to evaluate longitudinal relaxation R-1, transverse relaxation R-2, and proton density in the peritumoral edema in a group of patients with malignant glioma before surgery to assess whether relaxometry can detect changes not visible on conventional images. Methods In a prospective study, 24 patients with suspected malignant glioma were examined before surgery. A standard MRI protocol was used with the addition of a quantitative MR method (MAGIC), which measured R-1, R-2, and proton density. The diagnosis of malignant glioma was confirmed after biopsy/surgery. In 19 patients synthetic MR images were then created from the MAGIC scan, and ROIs were placed in the peritumoral edema to obtain the quantitative values. Dynamic susceptibility contrast perfusion was used to obtain cerebral blood volume (rCBV) data of the peritumoral edema. Voxel-based statistical analysis was performed using a mixed linear model. Results R-1, R-2, and rCBV decrease with increasing distance from the contrast-enhancing part of the tumor. There is a significant increase in R1 gradient after contrast agent injection (Pamp;lt;.0001). There is a heterogeneous pattern of relaxation values in the peritumoral edema adjacent to the contrast-enhancing part of the tumor. Conclusion Quantitative analysis with relaxometry of peritumoral edema in malignant gliomas detects tissue changes not visualized on conventional MR images. The finding of decreasing R-1 and R-2 means shorter relaxation times closer to the tumor, which could reflect tumor invasion into the peritumoral edema. However, these findings need to be validated in the future.

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    fulltext
  • 7.
    Blystad, Ida
    et al.
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine.
    Warntjes, Marcel, Jan Bertus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Smedby, Örjan
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Larsson, Elna-Marie
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences. Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.
    Tisell, Anders
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema2020In: Scientific Reports, E-ISSN 2045-2322, Vol. 10, no 1, article id 17986Article in journal (Refereed)
    Abstract [en]

    Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R1), transverse relaxation (R2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R1-difference-map. The quantitative R1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.

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    Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema.
  • 8.
    Chougar, Lydia
    et al.
    Juntendo Univ, Japan; Hop Cochin, France.
    Hagiwara, Akifumi
    Juntendo Univ, Japan; Univ Tokyo, Japan.
    Takano, Nao
    Juntendo Univ, Japan.
    Andica, Christina
    Juntendo Univ, Japan.
    Cohen-Adad, Julien
    Juntendo Univ, Japan; Polytech Montreal, Canada; Univ Montreal, Canada.
    Warntjes, Marcel, Jan Bertus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR AB, Linkoping, Sweden.
    Maekawa, Tomoko
    Juntendo Univ, Japan; Univ Tokyo, Japan.
    Hori, Masaaki
    Juntendo Univ, Japan.
    Koshino, Saori
    Juntendo Univ, Japan; Univ Tokyo, Japan.
    Nakazawa, Misaki
    Juntendo Univ, Japan.
    Abe, Osamu
    Univ Tokyo, Japan.
    Aoki, Shigeki
    Juntendo Univ, Japan.
    Signal Intensity within Cerebral Venous Sinuses on Synthetic MRI2020In: MAGNETIC RESONANCE IN MEDICAL SCIENCES, ISSN 1347-3182, Vol. 19, no 1, p. 56-63Article in journal (Refereed)
    Abstract [en]

    Purpose: Flowing blood sometimes appears bright on synthetic T-1-weighted images, which could be misdiagnosed as a thrombus. This study aimed to investigate the frequency of hyperintensity within cerebral venous sinuses on synthetic MR images and to evaluate the influence of increasing flow rates on signal intensity using a flow phantom. Materials and Methods: Imaging data, including synthetic and conventional MRI scans, from 22 patients were retrospectively analyzed. Signal intensities at eight locations of cerebral venous sinuses on synthetic images were graded using the following three-point scale: 0, "dark vessel"; 1, "hyperintensity within the walls"; and 2, "hyperintensity within the lumen:" A phantom with gadolinium solution inside a U-shaped tube was acquired without flow and then with increasing flow rates (60, 100, 200, 300, 400 ml/min). Results: Considering all sinus locations, the venous signal intensity on synthetic T-1-weighted images was graded as 2 in 79.8% of the patients. On synthetic T-2-weighted images, all sinuses were graded as 0. On fluid-attenuated inversion recovery (FLAIR) images, sinuses were almost always graded as 0 (99.4%). In the phantom study, the signal initially became brighter on synthetic T-1-weighted images as the flow rate increased. Above a certain flow rate, the signal started to decrease. Conclusion: High signal intensity within the cerebral venous sinuses is a frequent finding on synthetic T-1-weighted images. This corresponds to the hyperintensity noted at certain flow rates in the phantom experiment.

  • 9.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Department of Medicine and Health Sciences, Radiology. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Whole volume three dimensional B1 mapping in 10 second2008Conference paper (Other academic)
  • 10.
    Ebbers, Tino
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Haraldsson, Henrik
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dyverfeldt, Petter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Sigfridsson, Andreas
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Warntjes, Marcel Jan Bertus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Wigström, Lars
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences.
    Higher order weighted least-squares phase offset correction for improved accuracy in phase-contrast MRI2008Conference paper (Refereed)
    Abstract [en]

    Phase-contrast magnetic resonance imaging has the ability to accurately measure blood flow and myocardial velocities in the human body. Unwanted spatially varying phase offsets are, however, always present and may deteriorate the measurements significantly. Some of these phase offsets can be estimated based on the pulse sequence (1), but effects caused by eddy currents are more difficult to predict. A linear fit of the phase values is often estimated from either a number of manually defined areas containing stationary tissue or by semi-automatic detection of stationary tissue using the

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    Higer order weighted least-squares phase offset correction for improved accuracy in phase-contrast MRI
  • 11.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Warntjes, Marcel
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Phase Based Volume Registration on the GPU with Application to Quantitative MRI2010Conference paper (Other academic)
    Abstract [en]

    We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work with medical image registration, one that works with refining of the registration algorithms and one that works with implementation of more simple algorithms on graphic cards for speeding up the algorithms. We put the work from these groups together and get the best from both worlds. We achieve a speedup of 10-30 compared to our CPU implementation, which makes fast phase based registration possible for large medical volumes.

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    Phase Based Volume Registration on the GPU with Application to Quantitative MRI
  • 12.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Warntjes, Marcel
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Fast Phase Based Registration for Robust Quantitative MRI2010In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2010), 2010Conference paper (Other academic)
    Abstract [en]

    Quantitative magnetic resonance imaging has the major advantage that it handles absolute measurements of physical parameters. Quantitative MRI can for example be used to estimate the amount of different tissue types in the brain, but other applications are possible. Parameters such as relaxation rates R1 and R2 and proton density (PD) are independent of MR scanner settings and imperfections and hence are directly representative of the underlying tissue characteristics. Brain tissue quantification is an important aid for diagnosis of neurological diseases, such as multiple sclerosis (MS) and dementia. It is applied to estimate the volume of each tissue type, such as white tissue, grey tissue, myelin and cerebrospinal fluid (CSF). Tissue that deviates from normal values can be found automatically using computer aided diagnosis. In order for the quantification to have a clinical value, both the time in the MR scanner and the time for the data analysis have to be minimized. A challenge in MR quantification is to keep the scan time within clinically acceptable limits. The quantification method that we have used is based on the work by Warntjes et al.

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    FULLTEXT01
  • 13.
    Engström, Maria
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bertus Warntjes, Marcel, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping. SyntheticMR AB, Linkoping, Sweden.
    Tisell, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Landtblom, Anne-Marie
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuroscience. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology. Östergötlands Läns Landsting, Local Health Care Services in West Östergötland, Department of Medical Specialist in Motala.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Multi-Parametric Representation of Voxel-Based Quantitative Magnetic Resonance Imaging2014In: PLOS ONE, E-ISSN 1932-6203, Vol. 9, no 11, p. e111688-Article in journal (Refereed)
    Abstract [en]

    The aim of the study was to explore the possibilities of multi-parametric representations of voxel-wise quantitative MRI data to objectively discriminate pathological cerebral tissue in patients with brain disorders. For this purpose, we recruited 19 patients with Multiple Sclerosis (MS) as benchmark samples and 19 age and gender matched healthy subjects as a reference group. The subjects were examined using quantitative Magnetic Resonance Imaging (MRI) measuring the tissue structure parameters: relaxation rates, R-1 and R-2, and proton density. The resulting parameter images were normalized to a standard template. Tissue structure in MS patients was assessed by voxel-wise comparisons with the reference group and with correlation to a clinical measure, the Expanded Disability Status Scale (EDSS). The results were visualized by conventional geometric representations and also by multi-parametric representations. Data showed that MS patients had lower R-1 and R-2, and higher proton density in periventricular white matter and in wide-spread areas encompassing central and sub-cortical white matter structures. MS-related tissue abnormality was highlighted in posterior white matter whereas EDSS correlation appeared especially in the frontal cortex. The multi-parameter representation highlighted disease-specific features. In conclusion, the proposed method has the potential to visualize both high-probability focal anomalies and diffuse tissue changes. Results from voxel-based statistical analysis, as exemplified in the present work, may guide radiologists where in the image to inspect for signs of disease. Future clinical studies must validate the usability of the method in clinical practice.

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  • 14.
    Engström, Maria
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Jan Bertus Warntje, Marcel
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Tisell, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Landtblom, Anne-Marie
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuroscience. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Multi-Parametric Representation of Voxel-Based Quantitative Magnetic Resonance Imaging2014Conference paper (Other academic)
    Abstract [en]

    The aim of the study was to explore the possibilities of multi-parametric representations of voxel-wise quantitative MRI data to objectively discriminate pathological cerebral tissue in patients with brain disorders. For this purpose, we recruited 19 patients with Multiple Sclerosis (MS) as benchmark samples and 19 age and gender matched healthy subjects as a reference group. The subjects were examined using quantitative Magnetic Resonance Imaging (MRI) measuring the tissue structure parameters: relaxation rates, R and R, and proton density. The resulting parameter images were normalized to a standard template. Tissue structure in MS patients was assessed by voxel-wise comparisons with the reference group and with correlation to a clinical measure, the Expanded Disability Status Scale (EDSS). The results were visualized by conventional geometric representations and also by multi-parametric representations. Data showed that MS patients had lower R and R, and higher proton density in periventricular white matter and in wide-spread areas encompassing central and sub-cortical white matter structures. MS-related tissue abnormality was highlighted in posterior white matter whereas EDSS correlation appeared especially in the frontal cortex. The multi-parameter representation highlighted disease-specific features. In conclusion, the proposed method has the potential to visualize both high-probability focal anomalies and diffuse tissue changes. Results from voxel-based statistical analysis, as exemplified in the present work, may guide radiologists where in the image to inspect for signs of disease. Future clinical studies must validate the usability of the method in clinical practice.

  • 15.
    Fujita, Shohei
    et al.
    Juntendo Univ, Japan; Univ Tokyo, Japan; Athinoula A Martinos Ctr Biomed Imaging, MA USA; Harvard Med Sch, MA USA.
    Gagoski, Borjan
    Harvard Med Sch, MA USA; Boston Childrens Hosp, MA USA.
    Hwang, Ken-Pin
    MD Anderson Canc Ctr, TX USA.
    Hagiwara, Akifumi
    Juntendo Univ, Japan.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR, Linkoping, Sweden.
    Fukunaga, Issei
    Juntendo Univ, Japan.
    Uchida, Wataru
    Juntendo Univ, Japan.
    Saito, Yuya
    Juntendo Univ, Japan.
    Sekine, Towa
    Juntendo Univ, Japan.
    Tachibana, Rina
    Juntendo Univ, Japan.
    Muroi, Tomoya
    Juntendo Univ, Japan.
    Akatsu, Toshiya
    Juntendo Univ, Japan.
    Kasahara, Akihiro
    Univ Tokyo, Japan.
    Sato, Ryo
    Univ Tokyo, Japan.
    Ueyama, Tsuyoshi
    Univ Tokyo, Japan.
    Andica, Christina
    Juntendo Univ, Japan; Juntendo Univ, Japan.
    Kamagata, Koji
    Juntendo Univ, Japan.
    Amemiya, Shiori
    Univ Tokyo, Japan.
    Takao, Hidemasa
    Univ Tokyo, Japan.
    Hoshino, Yasunobu
    Juntendo Univ, Japan.
    Tomizawa, Yuji
    Juntendo Univ, Japan.
    Yokoyama, Kazumasa
    Juntendo Univ, Japan.
    Bilgic, Berkin
    Athinoula A Martinos Ctr Biomed Imaging, MA USA; Harvard Med Sch, MA USA; Harvard MIT Hlth Sci & Technol, MA USA.
    Hattori, Nobutaka
    Juntendo Univ, Japan.
    Abe, Osamu
    Univ Tokyo, Japan.
    Aoki, Shigeki
    Juntendo Univ, Japan.
    Cross-vendor multiparametric mapping of the human brain using 3D-QALAS: A multicenter and multivendor study2024In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594Article in journal (Refereed)
    Abstract [en]

    Purpose: To evaluate a vendor-agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) for whole-brain T1, T2, and proton density (PD) mapping.Methods: This prospective, multi-institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra-scanner repeatability and inter-vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age +/- SD, 28.0 +/- 5.6 y) who underwent scan-rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D-QALAS, nine patients with multiple sclerosis (nine women; mean age +/- SD, 48.2 +/- 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers.Results: Quantitative maps obtained with 3D-QALAS showed high linearity (R2 = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra-scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure-wise test-retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter-vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis.Conclusion: The vendor-agnostic multiparametric mapping technique 3D-QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers.

  • 16.
    Georgiopoulos, Charalampos
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR AB, Linkoping, Sweden.
    Dizdar Segrell, Nil
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Neurology.
    Zachrisson, Helene
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Engström, Maria
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Haller, Sven
    Affidea CDRC Centre Diagnost Radiol Carouge SA, Switzerland; Uppsala University, Sweden.
    Larsson, Elna-Marie
    Uppsala University, Sweden.
    Olfactory Impairment in Parkinsons Disease Studied with Diffusion Tensor and Magnetization Transfer Imaging2017In: Journal of Parkinson's Disease, ISSN 1877-7171, E-ISSN 1877-718X, Vol. 7, no 2, p. 301-311Article in journal (Refereed)
    Abstract [en]

    Background: Olfactory impairment is an early manifestation of Parkinsons disease (PD). Diffusion Tensor Imaging (DTI) and Magnetization Transfer (MT) are two imaging techniques that allow noninvasive detection of microstructural changes in the cerebral white matter. Objective: To assess white matter alterations associated with olfactory impairment in PD, using a binary imaging approach with DTI and MT. Methods: 22 PD patients and 13 healthy controls were examined with DTI, MT and an odor discrimination test. DTI data were first analyzed with tract-based spatial statistics (TBSS) in order to detect differences in fractional anisotropy, mean, radial and axial diffusivity between PD patients and controls. Voxelwise randomized permutation was employed for the MT analysis, after spatial and intensity normalization. Additionally, ROI analysis was performed on both the DTI and MT data, focused on the white matter adjacent to olfactory brain regions. Results: Whole brain voxelwise analysis revealed decreased axial diffusivity in the left uncinate fasciculus and the white matter adjacent to the left olfactory sulcus of PD patients. ROI analysis demonstrated decreased axial diffusivity in the right orbitofrontal cortex, as well as decreased mean diffusivity and axial diffusivity in the white matter of the left entorhinal cortex of PD patients. There were no significant differences regarding fractional anisotropy, radial diffusivity or MT between patients and controls. Conclusions: ROI analysis of DTI could detect microstructural changes in the white matter adjacent to olfactory areas in PD patients, whereas MT imaging could not.

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  • 17.
    Good, Elin
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart Center, Department of Cardiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ochoa-Figueroa, Miguel
    Region Östergötland, Heart Center, Department of Clinical Physiology in Linköping. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ziegler, Magnus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ressner, Marcus
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR AB, Linkoping, Sweden.
    Dyverfeldt, Petter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lubberink, Mark
    Uppsala Univ, Sweden.
    Ahlström, Håkan
    Uppsala Univ, Sweden; Antaros Med AB, Sweden.
    de Muinck, Ebo
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart Center, Department of Cardiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    (18)Fluorodeoxyglucose uptake in relation to fat fraction and R2*in atherosclerotic plaques, using PET/MRI: a pilot study2021In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 14217Article in journal (Refereed)
    Abstract [en]

    Inflammation inside Atherosclerotic plaques represents a major pathophysiological process driving plaques towards rupture. Pre-clinical studies suggest a relationship between lipid rich necrotic core, intraplaque hemorrhage and inflammation, not previously explored in patients. Therefore, we designed a pilot study to investigate the feasibility of assessing the relationship between these plaque features in a quantitative manner using PET/MRI. In 12 patients with high-grade carotid stenosis the extent of lipid rich necrotic core and intraplaque hemorrhage was quantified from fat and R2* maps acquired with a previously validated 4-point Dixon MRI sequence in a stand-alone MRI. PET/MRI was used to measure F-18-FDG uptake. T1-weighted images from both scanners were used for registration of the quantitative Dixon data with the PET images. The plaques were heterogenous with respect to their volumes and composition. The mean values for the group were as follows: fat fraction (FF) 0.17% (0.07), R2* 47.6 s(-1) (+/- 10.9) and target-to-blood pool ratio (TBR) 1.49 (+/- 0.48). At group level the correlation between TBR and FFmean was - 0.406, p 0.19 and for TBR and R2*(mean) 0.259, p 0.42. The lack of correlation persisted when analysed on a patient-by-patient basis but the study was not powered to draw definitive conclusions. We show the feasibility of analysing the quantitative relationship between lipid rich necrotic cores, intraplaque haemorrhage and plaque inflammation. The F-18-FDG uptake for most patients was low. This may reflect the biological complexity of the plaques and technical aspects inherent to F-18-FDG measurements. Trial registration: ISRCTN, ISRCTN30673005. Registered 05 January 2021, retrospectively registered.

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  • 18.
    Good, Elin
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart Center, Department of Cardiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ziegler, Magnus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR AB, Linkoping, Sweden.
    Dyverfeldt, Petter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    de Muinck, Ebo
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart Center, Department of Cardiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Quantitative Magnetic Resonance Imaging Assessment of the Relationships Between Fat Fraction and R2*Inside Carotid Plaques, and Circulating Lipoproteins2022In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 55, no 4, p. 1260-1270Article in journal (Refereed)
    Abstract [en]

    Background Lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) are morphological features of high-risk atherosclerotic plaques. However, their relationship to circulating lipoproteins is unclear. Purpose To study associations between changes in lipoproteins vs. changes in LRNC (represented by fat fraction [FF]) and IPH (represented by R2*). Study Type Prospective. Subjects Fifty-two patients with carotid plaques, 33 males (63.5%), mean age 72 (+/- 5). Field Strength/Sequence Four-point fast gradient Dixon magnetic resonance imaging (MRI) was used to quantify FF and R2* (to measure IPH) inside plaques and in vessel wall. Turbo-spin echo was used for T-1 weighted sequences to guide manual segmentation. Assessment Carotid MRI and serum lipid levels were assessed at baseline and at 1-year follow-up. For patients, lipid-lowering therapy was customized to reduce low-density lipoprotein (LDL) levels below 1.8 mmol/L. Segmentation was performed with one set of regions of interest for the plaque and one for the vessel wall at the location of the plaque. Thereby MRI data for FF, R2*, and volumes in plaque- and vessel-wall segmentations could be obtained from baseline and follow-up, as well as changes over the study year. Statistical Tests Pearson correlation coefficient for correlations. Paired samples t-test for changes over time. Significance at P < 0.05, 95% confidence interval. Results LDL decreased significantly (2.19-1.88 mmol/L, Z - 2.9), without correlation to changes in plaque composition, nor to the significant reduction in vessel-wall volume (-106.3 mm(3)). Plaque composition remained unchanged, FF +8.5% (P = 0.366) and R2* +3.5% (P = 0.304). Compared to plaque segmentations, R2* was significantly lower in the vessel-wall segmentations both at baseline (-9.3%) and at follow-up (-9.1%). Data Conclusion The absence of correlations between changes in lipoproteins and changes in plaque composition indicates more complex relationships between these parameters than previously anticipated. The significant differences in both R2* and volume dynamics comparing plaque segmentations and vessel-wall segmentations suggest differences in their pathobiology of atherosclerosis. Level of Evidence 1 Technical Efficacy Stage 4

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  • 19.
    Hagiwara, Akifumi
    et al.
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan. a-hagiwara@juntendo.ac.jp; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
    Hori, Masaaki
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
    Kamagata, Koji
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
    Warntjes, Marcel
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. SyntheticMR AB, Linköping, Sweden.
    Matsuyoshi, Daisuke
    Araya Inc., Tokyo, Japan; Research Institute for Science and Engineering, Waseda University, Waseda, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.
    Nakazawa, Misaki
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
    Ueda, Ryo
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan; Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan; Office of Radiation Technology, Keio University Hospital, Tokyo, Japan.
    Andica, Christina
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
    Koshino, Saori
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
    Maekawa, Tomoko
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
    Irie, Ryusuke
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
    Takamura, Tomohiro
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
    Kumamaru, Kanako Kunishima
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
    Abe, Osamu
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
    Aoki, Shigeki
    Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
    Myelin Measurement: Comparison Between Simultaneous Tissue Relaxometry, Magnetization Transfer Saturation Index, and T1w/T2w Ratio Methods2018In: Scientific Reports, E-ISSN 2045-2322, Vol. 8, no 1, article id 10554Article in journal (Refereed)
    Abstract [en]

    Magnetization transfer (MT) imaging has been widely used for estimating myelin content in the brain. Recently, two other approaches, namely simultaneous tissue relaxometry of R1 and R2 relaxation rates and proton density (SyMRI) and the ratio of T1-weighted to T2-weighted images (T1w/T2w ratio), were also proposed as methods for measuring myelin. SyMRI and MT imaging have been reported to correlate well with actual myelin by histology. However, for T1w/T2w ratio, such evidence is limited. In 20 healthy adults, we examined the correlation between these three methods, using MT saturation index (MTsat) for MT imaging. After calibration, white matter (WM) to gray matter (GM) contrast was the highest for SyMRI among these three metrics. Even though SyMRI and MTsat showed strong correlation in the WM (r?=?0.72), only weak correlation was found between T1w/T2w and SyMRI (r?=?0.45) or MTsat (r?=?0.38) (correlation coefficients significantly different from each other, with p values?amp;lt;?0.001). In subcortical and cortical GM, these measurements showed moderate to strong correlations to each other (r?=?0.54 to 0.78). In conclusion, the high correlation between SyMRI and MTsat indicates that both methods are similarly suited to measure myelin in the WM, whereas T1w/T2w ratio may be less optimal.

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  • 20.
    Hagiwara, Akifumi
    et al.
    Department of Radiology, Juntendo University School of Medicine, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan .
    Warntjes, Marcel, Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Hori, Masaaki
    Department of Radiology, Juntendo University School of Medicine, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan .
    Andica, Christina
    Department of Radiology, Juntendo University School of Medicine, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan .
    Nakazawa, Misaki
    Department of Radiology, Juntendo University School of Medicine, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; |Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan.
    Kumamaru, Kanako Kunishima
    Department of Radiology, Juntendo University School of Medicine, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan .
    Abe, Osamu
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
    Aoki, Shigeki
    Department of Radiology, Juntendo University School of Medicine, Japan.
    SyMRI of the Brain: Rapid Quantification of Relaxation Rates and Proton Density, With Synthetic MRI, Automatic Brain Segmentation, and Myelin Measurement2017In: Investigative Radiology, ISSN 0020-9996, E-ISSN 1536-0210, Vol. 52, no 10, p. 647-657Article, review/survey (Refereed)
    Abstract [en]

    Conventional magnetic resonance images are usually evaluated using the image signal contrast between tissues and not based on their absolute signal intensities. Quantification of tissue parameters, such as relaxation rates and proton density, would provide an absolute scale; however, these methods have mainly been performed in a research setting. The development of rapid quantification, with scan times in the order of 6 minutes for full head coverage, has provided the prerequisites for clinical use. The aim of this review article was to introduce a specific quantification method and synthesis of contrast-weighted images based on the acquired absolute values, and to present automatic segmentation of brain tissues and measurement of myelin based on the quantitative values, along with application of these techniques to various brain diseases. The entire technique is referred to as "SyMRI" in this review. SyMRI has shown promising results in previous studies when used for multiple sclerosis, brain metastases, Sturge-Weber syndrome, idiopathic normal pressure hydrocephalus, meningitis, and postmortem imaging.

  • 21.
    Hedlund, Anna
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Faculty of Health Sciences.
    Ahrén, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Surface Physics and Chemistry . Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Gustafsson, Håkan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Abrikossova, Natalia
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Physics, Chemistry and Biology. Linköping University, The Institute of Technology.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Jönsson, Jan-Ingvar
    Linköping University, Department of Clinical and Experimental Medicine, Experimental Hematology. Linköping University, Faculty of Health Sciences.
    Uvdal, Kajsa
    Linköping University, Department of Physics, Chemistry and Biology, Sensor Science and Molecular Physics . Linköping University, The Institute of Technology.
    Engström, Maria
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Detection of Gd2O3 Nanoparticles in Hematopoietic Cells for MRI Contrast EnhancementManuscript (preprint) (Other academic)
    Abstract [en]

    As the utility of magnetic resonance imaging (MRI) broadens, the importance of having specific and efficient contrast agents increases and there has been a huge development in the fields of molecular imaging and intracellular markers.

    Previous studies have shown that gadolinium oxide (Gd2O3 ) nanoparticles generate higher relaxivity than currently available Gd chelates. The Gd2O3 nanoparticles are also promising for MRI cell tracking. The aim of the present work was to study cell labeling with Gd2O3 nanoparticles and to improve techniques for monitoring hematopoietic stem cell migration by MRI.

    We studied particle uptake in two cell lines; the hematopoietic progenitor cell line Ba/F3 and the monocytic cell line THP-1. Cells were incubated with Gd2O3 nanoparticles as well as superparamagnetic iron oxide particles (SPIOs) for comparison. In addition, it was investigated whether the transfection agent protamine sulfate increased the particle uptake. Treated cells were examined by microscopic techniques, MRI and analyzed for particle content.

    Results showed that particles were intracellular, however in Ba/F3 only sparsely. The relaxation times were shortened with increasing particle concentration. Overall relaxivities, r1 and r2 for Gd2O3 nanoparticles in all cell samples measured were 5.1 ± 0.3 and 14.9 ± 0.7 (s-1mM-1) respectively. Goodness of fit was 0.97 in both cases. Protamine sulfate treatment increased the uptake in both Ba/F3 cells and THP-1 cells.

    Viability of treated cells was not significantly decreased and thus, we conclude that the use of Gd2O3 nanoparticles is suitable for this type of cell labeling by means of detecting and monitoring hematopoietic cells.

  • 22.
    Hedlund, Anna
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Ahrén, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Molecular Surface Physics and Nano Science. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Håkan
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Abrikossova, Natalia
    Linköping University, Department of Physics, Chemistry and Biology, Molecular Surface Physics and Nano Science. Linköping University, Faculty of Science & Engineering.
    Warntjes, Marcel
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Jönsson, Jan-Ivar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Medical and Physiological Chemistry.
    Uvdal, Kajsa
    Linköping University, Department of Physics, Chemistry and Biology, Molecular Surface Physics and Nano Science. Linköping University, Faculty of Science & Engineering.
    Engström, Maria
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Gd2O3 nanoparticles in hematopoietic cells for MRI contrast enhancement2011In: International journal of nano medicine, ISSN 1178-2013, Vol. 6, p. 3233-3240Article in journal (Refereed)
    Abstract [en]

    As the utility of magnetic resonance imaging (MRI) broadens, the importance of having specific and efficient contrast agents increases and in recent time there has been a huge development in the fields of molecular imaging and intracellular markers. Previous studies have shown that gadolinium oxide (Gd2O3) nanoparticles generate higher relaxivity than currently available Gd chelates: In addition, the Gd2O3 nanoparticles have promising properties for MRI cell tracking. The aim of the present work was to study cell labeling with Gd2O3 nanoparticles in hematopoietic cells and to improve techniques for monitoring hematopoietic stem cell migration by MRI. Particle uptake was studied in two cell lines: the hematopoietic progenitor cell line Ba/F3 and the monocytic cell line THP-1. Cells were incubated with Gd2O3 nanoparticles and it was investigated whether the transfection agent protamine sulfate increased the particle uptake. Treated cells were examined by electron microscopy and MRI, and analyzed for particle content by inductively coupled plasma sector field mass spectrometry. Results showed that particles were intracellular, however, sparsely in Ba/F3. The relaxation times were shortened with increasing particle concentration. Relaxivities, r1 and r2 at 1.5 T and 21°C, for Gd2O3 nanoparticles in different cell samples were 3.6–5.3 s-1 mM-1 and 9.6–17.2 s-1 mM-1, respectively. Protamine sulfate treatment increased the uptake in both Ba/F3 cells and THP-1 cells. However, the increased uptake did not increase the relaxation rate for THP-1 as for Ba/F3, probably due to aggregation and/or saturation effects. Viability of treated cells was not significantly decreased and thus, it was concluded that the use of Gd2O3 nanoparticles is suitable for this type of cell labeling by means of detecting and monitoring hematopoietic cells. In conclusion, Gd2O3 nanoparticles are a promising material to achieve positive intracellular MRI contrast; however, further particle development needs to be performed.

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  • 23.
    Jackowski, Christian
    et al.
    Universität Zürich, Inst für Rechtsmedizin, Winterthurerstrasse 190/52, CH-8057 Zürich, Switzerland.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Berge, Johan
    Rättsmedicinalverket, Rättsmedicinska avdelningen, Artillerigatan 12, 587 58 Linköping.
    Bär, Walter
    Universität Zürich, Inst für Rechtsmedizin, Winterthurerstrasse 190/52, CH-8057 Zürich, Switzerland.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Magnetic resonance imaging goes postmortem: noninvasive detection and assessment of myocardial infarction by postmortem MRI2011In: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. Jan;21, no 1, p. 70-78Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE: To investigate the performance of postmortem magnetic resonance imaging (pmMRI) in identification and characterization of lethal myocardial infarction in a non-invasive manner on human corpses.

    MATERIALS AND METHODS: Before forensic autopsy, 20 human forensic corpses were examined on a 1.5-T system for the presence of myocardial infarction. Short axis, transversal and longitudinal long axis images (T1-weighted; T2-weighted; PD-weighted) were acquired in situ. In subsequent autopsy, the section technique was adapted to short axis images. Histological investigations were conducted to confirm autopsy and/or radiological diagnoses.

    RESULTS: Nineteen myocardial lesions were detected and age staged with pmMRI, of which 13 were histologically confirmed (chronic, subacute and acute). Six lesions interpreted as peracute by pmMRI showed no macroscopic or histological finding. Five of the six peracute lesions correlated well to coronary pathology, and one case displayed a severe hypertrophic alteration.

    CONCLUSION: pmMRI reliably demonstrates chronic, subacute and acute myocardial infarction in situ. In peracute cases pmMRI may display ischemic lesions undetectable at autopsy and routine histology. pmMRI has the potential to substantiate autopsy and to counteract the loss of reliable information on causes of death due to the recent disappearance of the clinical autopsy.

  • 24.
    Jackowski, Christian
    et al.
    Universität Zürich, Inst für Rechtsmedizin, Winterthurerstrasse 190/52, CH-8057 Zürich, Switzerland.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Berge, Johan
    Rättsmedicinalverket, Rättsmedicinska avdelningen, Artillerigatan 12, 587 58 Linköping.
    Thali, Michael J.
    Univ Bern, Inst Forensic Medicine, Ctr Forens Imaging & Virtopsy, CH-3012 Bern, Switzerland.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Quantitative MRI in Isotropic Spatial Resolution for Forensic Soft Tissue Documentation. Why and How?2011In: Journal of Forensic Sciences, ISSN 0022-1198, E-ISSN 1556-4029, Vol. 56, no 1, p. 208-215Article in journal (Refereed)
    Abstract [en]

    A quantification of T1, T2, and PD in high isotropic resolution was performed on corpses. Isotropic and quantified postmortem magnetic resonance (IQpmMR) enables sophisticated 3D postprocessing, such as reformatting and volume rendering. The body tissues can be characterized by the combination of these three values. The values of T1, T2, and PD were given as coordinates in a T1-T2-PD space where similar tissue voxels formed clusters. Implementing in a volume rendering software enabled color encoding of specific tissues and pathologies in 3D models of the corpse similar to computed tomography, but with distinctively more powerful soft tissue discrimination. From IQpmMR data, any image plane at any contrast weighting may be calculated or 3D color-encoded volume rendering may be carried out. The introduced approach will enable future computer-aided diagnosis that, e.g., checks corpses for a hemorrhage distribution based on the knowledge of its T1-T2-PD vector behavior in a high spatial resolution.

  • 25.
    Kihlberg, Johan
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Fransson, Sven-Göran
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Engvall, Jan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Maret, Eva
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Rommel, Franz
    Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Haematology UHL.
    Ackumulering av överskottsjärn kan bestämmas med MR2009In: Ackumulering av överskottsjärn kan bestämmas med MR, 2009Conference paper (Refereed)
    Abstract [sv]

    Järnöverskott kan vara toxiskt i kroppen. Järnöverskott ses fr a efter multipla blodtransfusioner vid vissa blodsjukdomar. Internationellt är den vanligaste orsaken thalassemi. Antalet patienter med denna problematik är i Sverige ännu begränsat. Järnöverskott kan leda till allvarlig, svårbehandlad hjärtsvikt man kan behandlas med chelaterande perorala läkemedel och styrs då i allmänhet utifrån ferritin/s. Vår hypotes var att överensstämmelsen mellan järnöverskott och transferrin är låg, att järnöverskott bättre karaktäriseras med MR som också kan differentiera mellan järnöverskott i  hjärta respektive lever.

  • 26.
    Koppal, Sandeep
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences.
    Moreno, Rodrigo
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences.
    Dyverfeldt, Petter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    de Muinck, Ebo
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences.
    Optimal validering av MR-bildtagning av aterosklerotiska plack genom användning av multi-modal MR och 3D histologi2013Conference paper (Other academic)
    Abstract [sv]

    BAKGRUND: Magnetkamera (MR) kan identifiera aterosklerotiska plack som löper risk att brista och därmed orsaka stroke eller hjärtinfarkt. Metoden är dock bristfälligt validerad på grund av den osäkerhet som uppstår då 2D histologiska snitt ska registreras med 3D MR-bilder.

    SYFTE: Att optimera validering av MR-bildtagning av aterosklerotiska plack genom användning av multi-modal MR och 3D histologi.

    MATERIAL och METOD: Patienter som skulle opereras för att avlägsna aterosklerotiska plack från arteria karotis genomgick dedikerad plack-MR där följande parametrar undersöktes: plackets fettinnehåll, blödning inuti placket och maximal intensitet av turbulent blodflöde. Undersökningarna gjordes med en Philips 3T MR-kamera: (a) 4-punkt Dixon 3D gradient-eko, (b) T1-viktad spin-eko, (c) 4D fas-kontrast. Upplösningen var 0.6x0.6x0.7mm, 0.35x0.35x3mm respektive 1.14x1.25x1.14mm x 25ms. Vatten-, fett- and R2*-bilder (blödning) beräknades utifrån Dixon-sekvensen.Efter operation bäddades placken in i paraffin och enface-bilder togs varje 50µm i Z-riktning. Bilderna registrerades i ImageJ/Fiji och användes för att bygga en 3D-volym av placket. Vid varje 200µm togs snitt för biologiska markörer och histologiska färgningar. De färgade snitten registrerades med motsvarande enface-bilder. Detta resulterade i 3D-volymer med en upplösning på 1.02x1.02x200µm. Den histologiska 3D-volymen registrerades manuellt med uppsamplade och co-registrerade MR-bilder.

    RESULTAT: T1-viktade bilder var bäst för registrering av plack inom varje snitt. Registrering av kärlets lumen optimerades genom en kombination av 4D fas-kontrast, det första Dixon-ekot och vatten-bilder. Registrering av fett och R2* från MR-bilder med fett och blödning från 3D histologi uppvisade god överensstämmelse.

    SLUTSATS: Optimal validering av MR-bilder av aterosklerotiska plack kan åstadkommas genom att kombinera olika anatomiska landmärken från multimodala MR-bilder av plack och 3D-histologi. Genom att använda 3D-histologi korrigerar man för registreringsproblem som är relaterade till ”out-of-plane” vinklingar av vävnadssnitt och krympning och deformering till följd av histologiskt bearbetning av placket. Den detaljerade biologiska informationen från 3D-histologi kan förväntas förstärka fynden från in vivo MR-bilder.

  • 27.
    Koppal, Sandeep
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. SyntheticMR AB, Linköping, Sweden.
    Swann, Jeremy
    School of Computing, University of Leeds, Leeds, United Kingdom.
    Dyverfeldt, Petter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Moreno, Rodrigo
    Linköping University, Center for Medical Image Science and Visualization (CMIV). KTH, Royal Institute of Technology, Stockholm, Sweden.
    Magee, Derek
    School of Computing, University of Leeds, Leeds, United Kingdom.
    Roberts, Nicholas
    Division of Brain Sciences, Department of Medicine, Institute of Neurology, Imperial College, London, United Kingdom.
    Zachrisson, Helene
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Forssell, Claes
    Region Östergötland, Heart and Medicine Center, Department of Thoracic and Vascular Surgery.
    Länne, Toste
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Thoracic and Vascular Surgery.
    Treanor, Darren
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Department of Pathology and Tumour Biology, Leeds Institute of Molecular Medicine, University of Leeds, Leeds, United Kingdom.
    de Muinck, Ebo
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Cardiology in Linköping.
    Quantitative Fat and R2* Mapping In Vivo to Measure Lipid-Rich Necrotic Core and Intraplaque Hemorrhage in Carotid Atherosclerosis2017In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, no 1, p. 285-296Article in journal (Refereed)
    Abstract [en]

    Purpose: The aim of this work was to quantify the extent of lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in atherosclerotic plaques.

    Methods: Patients scheduled for carotid endarterectomy underwent four-point Dixon and T1-weighted magnetic resonance imaging (MRI) at 3 Tesla. Fat and R2* maps were generated from the Dixon sequence at the acquired spatial resolution of 0.60 × 0.60 × 0.70 mm voxel size. MRI and three-dimensional (3D) histology volumes of plaques were registered. The registration matrix was applied to segmentations denoting LRNC and IPH in 3D histology to split plaque volumes in regions with and without LRNC and IPH.

    Results: Five patients were included. Regarding volumes of LRNC identified by 3D histology, the average fat fraction by MRI was significantly higher inside LRNC than outside: 12.64 ± 0.2737% versus 9.294 ± 0.1762% (mean ± standard error of the mean [SEM]; P < 0.001). The same was true for IPH identified by 3D histology, R2* inside versus outside IPH was: 71.81 ± 1.276 s−1 versus 56.94 ± 0.9095 s−1 (mean ± SEM; P < 0.001). There was a strong correlation between the cumulative fat and the volume of LRNC from 3D histology (R2 = 0.92) as well as between cumulative R2* and IPH (R2 = 0.94).

    Conclusion: Quantitative mapping of fat and R2* from Dixon MRI reliably quantifies the extent of LRNC and IPH.

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  • 28.
    Kumar, Neil M.
    et al.
    Johns Hopkins Univ, MD 21287 USA.
    Fritz, Benjamin
    Balgrist Univ Hosp, Switzerland; Univ Zurich, Switzerland.
    Stern, Steven E.
    Bond Univ, Australia.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR AB, Linkoping, Sweden.
    Chuah, Yen Mei Lisa
    Siemens Healthcare GmbH, Germany.
    Fritz, Jan
    Johns Hopkins Univ, MD 21287 USA.
    Synthetic MRI of the Knee: Phantom Validation and Comparison with Conventional MRI2018In: Radiology, ISSN 0033-8419, E-ISSN 1527-1315, Vol. 289, no 2, p. 465-477Article in journal (Refereed)
    Abstract [en]

    Purpose: To test the hypothesis that synthetic MRI of the knee generates accurate and repeatable quantitative maps and produces morphologic MR images with similar quality and detection rates of structural abnormalities than does conventional MRI. Materials and Methods: Data were collected prospectively between January 2017 and April 2018 and were retrospectively analyzed. An International Society for Magnetic Resonance in Medicine-National Institute of Standards and Technology phantom was used to determine the accuracy of T1, T2, and proton density (PD) quantification. Statistical models were applied for correction. Fifty-four participants (24 men, 30 women; mean age, 40 years; range, 18-62 years) underwent synthetic and conventional 3-T MRI twice on the same day. Fifteen of 54 participants (28%) repeated the protocol within 9 days. The intra-and interday agreements of quantitative cartilage measurements were assessed. Contrast-to-noise (CNR) ratios, image quality, and structural abnormalities were assessed on corresponding synthetic and conventional images. Statistical analyses included the Wilcoxon test, chi(2) test, and Cohen Kappa. P values less than or equal to.01 were considered to indicate a statistically significant difference. Results: Synthetic MRI quantification of T1, T2, and PD values had an overall model-corrected error margin of 0.8%. The synthetic MRI interday repeatability of articular cartilage quantification had native and model-corrected error margins of 3.3% and 3.5%, respectively. The cartilage-to-fluid CNR and menisci-to-fluid CNR was higher on synthetic than conventional MR images (P amp;lt;= .001, respectively). Synthetic MRI improved short-tau inversion recovery fat suppression (P amp;lt; .01). Intermethod agreements of structural abnormalities were good (kappa, 0.621-0.739). Conclusion: Synthetic MRI of the knee is accurate for T1, T2, and proton density quantification, and simultaneously generated morphologic MR images have detection rates of structural abnormalities similar to those of conventional MR images, with similar acquisition time. (c) RSNA, 2018

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  • 29.
    Kvernby, Sofia
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Rönnerfalk, Mattias
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR AB, Linkoping, Sweden.
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Nylander, Eva
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Tamas, Eva
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Thoracic and Vascular Surgery. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Longitudinal Changes in Myocardial T-1 and T-2 Relaxation Times Related to Diffuse Myocardial Fibrosis in Aortic Stenosis; Before and After Aortic Valve Replacement2018In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 48, no 3, p. 799-807Article in journal (Refereed)
    Abstract [en]

    Background: Diffuse myocardial fibrosis is associated with adverse outcomes, although detection and quantification is challenging. Cardiac MR relaxation times mapping represents a promising imaging biomarker for diffuse myocardial fibrosis. Purpose: To investigate whether relaxation times can detect longitudinal changes in myocardial tissue composition associated with diffuse fibrosis in patients with severe aortic stenosis (AS) before and after aortic valve replacement (AVR). Study type: Prospective longitudinal study. Population/Subjects/Phantom/Specimen/Animal Model: Fifteen patients with severe AS. Field Strength/Sequence: 3T /3(3) 3(3) 5-MOLLI, T2-GraSE, and 3D-QALAS. Assessment: Patients underwent MR examinations at three timepoints: before AVR, as well as 3 and 12 months after AVR. Data from each patient was analyzed in 16 myocardial segments. Statistical Tests: The segment-wise T1 and T2 data were analyzed over time after surgery using linear mixed models for repeated measures analysis. Results: The results showed that T1 relaxation times were significantly (Pamp;lt; 0.05) shorter 3 and 12 months postoperative than preoperative and that the T2 relaxation times were significantly (Pamp;lt; 0.05) longer 3 and 12 months postoperative than preoperative for both 3D and 2D mapping methods. No significant changes were seen between 3 and 12 months postoperative for any of the methods (P50.06/0.19 for T1 with 3D-QALAS/MOLLI and P50.09/0.25 for T2 with 3DQALAS/ GraSE). Data Conclusion: We demonstrated that changes in myocardial relaxation times and thus tissue characteristics can be observed within 3 months after AVR surgery. The significant changes in relaxation times from preoperative examinations to the follow-up may be interpreted as a reduction of interstitial fibrosis in the left ventricular wall. Level of Evidence: 1 Technical Efficacy: Stage 3

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  • 30.
    Kvernby, Sofia
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR AB, Linkoping, Sweden.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Clinical feasibility of 3D-QALAS - Single breath-hold 3D myocardial T1 and T2-mapping2017In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 38, p. 13-20Article in journal (Refereed)
    Abstract [en]

    Purpose: To investigate the in-vivo precision and clinical feasibility of 3D-QALAS- a novel method for simultaneous three-dimensional myocardial T1- and T2-mapping. Methods: Ten healthy subjects and 23 patients with different cardiac pathologies underwent cardiovascular 3 T MRI examinations including 3D-QALAS, MOLLI and T2-GraSE acquisitions. Precision was investigated in the healthy subjects between independent scans, between dependent scans and as standard deviation of consecutive scans. Clinical feasibility of 3D-QALAS was investigated for native and contrast enhanced myocardium in patients. Data were analyzed using mean value and 95% confidence interval, Pearson correlation, Paired t-tests, intraclass correlation and Bland-Altman analysis. Results: Average myocardial relaxation time values and SD from eight repeated acquisitions within the group of healthy subjects were 1178 +/- 18.5 ms (1.6%) for T1 with 3D-QALAS, 52.7 +/- 1.2 ms (23%) for T2 with 3D-QALAS, 1145 +/- 10.0 ms (0.9%) for Tl with MOLLI and 49.2 +/- 0.8 ms (1.6%) for T2 with GraSE. Myocardial Tl and T2 relaxation times obtained with 3D-QALAS correlated very well with reference methods; MOW for T1 (r = 0.994) and T2-GraSE for T2 (r = 0.818) in the 23 patients. Average native/post-contrast myocardial Tl values from the patients were 1166.2 ms/411.8 ms for 3D-QALAS and 1174.4 ms/438.9 ms for MOW. Average native myocardial T2 values from the patients were 53.2 ms for 3D-QAIAS and 54.4 ms for T2-GraSE. Conclusions: Repeated independent and dependent scans together with the intra-scan repeatability, demonstrated all a very good precision for the 3D-QALAS method in healthy volunteers. This study shows that 3D T1 and T2 mapping in the left ventricle is feasible in one breath hold for patients with different cardiac pathologies using 3D-QALAS. (C) 2016 Elsevier Inc. All rights reserved.

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  • 31.
    Kvernby, Sofia
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Haraldsson, Henrik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Carlhäll, Carl-Johan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Department of Science and Technology, Media and Information Technology.
    Simultaneous three-dimensional myocardial T1 and T2 mapping in one breath hold with 3D-QALAS2014In: Journal of Cardiovascular Magnetic Resonance, ISSN 1097-6647, E-ISSN 1532-429X, Vol. 16, no 102Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Quantification of the longitudinal- and transverse relaxation time in the myocardium has shown to provide important information in cardiac diagnostics. Methods for cardiac relaxation time mapping generally demand a long breath hold to measure either T1 or T2 in a single 2D slice. In this paper we present and evaluate a novel method for 3D interleaved T1 and T2 mapping of the whole left ventricular myocardium within a single breath hold of 15 heartbeats.

    METHODS: The 3D-QALAS (3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse) is based on a 3D spoiled Turbo Field Echo sequence using inversion recovery with interleaved T2 preparation. Quantification of both T1 and T2 in a volume of 13 slices with a resolution of 2.0x2.0x6.0 mm is obtained from five measurements by using simulations of the longitudinal magnetizations Mz. This acquisition scheme is repeated three times to sample k-space. The method was evaluated both in-vitro (validated against Inversion Recovery and Multi Echo) and in-vivo (validated against MOLLI and Dual Echo).

    RESULTS: In-vitro, a strong relation was found between 3D-QALAS and Inversion Recovery (R = 0.998; N = 10; p < 0.01) and between 3D-QALAS and Multi Echo (R = 0.996; N = 10; p < 0.01). The 3D-QALAS method showed no dependence on e.g. heart rate in the interval of 40-120 bpm. In healthy myocardium, the mean T1 value was 1083 ± 43 ms (mean ± SD) for 3D-QALAS and 1089 ± 54 ms for MOLLI, while the mean T2 value was 50.4 ± 3.6 ms 3D-QALAS and 50.3 ± 3.5 ms for Dual Echo. No significant difference in in-vivo relaxation times was found between 3D-QALAS and MOLLI (N = 10; p = 0.65) respectively 3D-QALAS and Dual Echo (N = 10; p = 0.925) for the ten healthy volunteers.

    CONCLUSIONS: The 3D-QALAS method has demonstrated good accuracy and intra-scan variability both in-vitro and in-vivo. It allows rapid acquisition and provides quantitative information of both T1 and T2 relaxation times in the same scan with full coverage of the left ventricle, enabling clinical application in a broader spectrum of cardiac disorders.

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  • 32.
    Lundberg, Peter
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Abrahamsson, Annelie
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Oncology.
    Kihlberg, Johan
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Tellman, Jens
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Tomkeviciene, Ieva
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Karlsson, Anette
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Kristoffersen Wiberg, Maria
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dabrosin, Charlotta
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Oncology.
    Low-dose acetylsalicylic acid reduces local inflammation and tissue perfusion in dense breast tissue in postmenopausal women2024In: Breast Cancer Research, ISSN 1465-5411, E-ISSN 1465-542X, Vol. 26, no 1, article id 22Article in journal (Refereed)
    Abstract [en]

    Purpose One major risk factor for breast cancer is high mammographic density. It has been estimated that dense breast tissue contributes to similar to 30% of all breast cancer. Prevention targeting dense breast tissue has the potential to improve breast cancer mortality and morbidity. Anti-estrogens, which may be associated with severe side-effects, can be used for prevention of breast cancer in women with high risk of the disease per se. However, no preventive therapy targeting dense breasts is currently available. Inflammation is a hallmark of cancer. Although the biological mechanisms involved in the increased risk of cancer in dense breasts is not yet fully understood, high mammographic density has been associated with increased inflammation. We investigated whether low-dose acetylsalicylic acid (ASA) affects local breast tissue inflammation and/or structural and dynamic changes in dense breasts. Methods Postmenopausal women with mammographic dense breasts on their regular mammography screen were identified. A total of 53 women were randomized to receive ASA 160 mg/day or no treatment for 6 months. Magnetic resonance imaging (MRI) was performed before and after 6 months for a sophisticated and continuous measure breast density by calculating lean tissue fraction (LTF). Additionally, dynamic quantifications including tissue perfusion were performed. Microdialysis for sampling of proteins in vivo from breasts and abdominal subcutaneous fat, as a measure of systemic effects, before and after 6 months were performed. A panel of 92 inflammatory proteins were quantified in the microdialysates using proximity extension assay. Results After correction for false discovery rate, 20 of the 92 inflammatory proteins were significantly decreased in breast tissue after ASA treatment, whereas no systemic effects were detected. In the no-treatment group, protein levels were unaffected. Breast density, measured by LTF on MRI, were unaffected in both groups. ASA significantly decreased the perfusion rate. The perfusion rate correlated positively with local breast tissue concentration of VEGF. Conclusions ASA may shape the local breast tissue microenvironment into an anti-tumorigenic state. Trials investigating the effects of low-dose ASA and risk of primary breast cancer among postmenopausal women with maintained high mammographic density are warranted.hic density are warranted.

  • 33.
    Mangeat, Gabriel
    et al.
    NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada.
    Ouellette, Russell
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.
    Wabartha, Maxime
    NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada.
    De Leener, Benjamin
    Department of Computer Sciences and Software Engineering, Polytechnique Montreal, Montreal, Quebec, Canada.
    Plattén, Michael
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biochemistry and Health, Royal Institute of Technology, Stockholm, Sweden.
    Danylaité Karrenbauer, Virginija
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Warntjes, Marcel
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR, Linköping, Sweden.
    Stikov, Nikola
    NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada; Montreal Heart Institute, Montreal, Quebec, Canada.
    Mainero, Caterina
    Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, MGH, Charlestown, MA; Harvard Medical School, Boston, MA.
    Cohen-Adad, Julien
    NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada; and Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Quebec, Canada.
    Granberg, Tobias
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.
    Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis2020In: Journal of Neuroimaging, ISSN 1051-2284, E-ISSN 1552-6569, Vol. 30, no 5, p. 674-682Article in journal (Refereed)
    Abstract [en]

    Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder compared to MS, which has led to occurrent misdiagnosis of HDLS as MS. That is problematic since their prognosis and treatment differ. Both disorders are investigated by MRI, which could help to identify patients with high probability of having HDLS, which could guide targeted genetic testing to confirm the HDLS diagnosis.

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  • 34.
    Morales Drissi, Natasha
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Wessen, Alexander
    Linköping University.
    Szakacs, Attila
    Univ Gothenburg, Sweden.
    darin, Niklas
    Univ Gothenburg, Sweden.
    Hallbook, Tove
    Univ Gothenburg, Sweden.
    Landtblom, Anne-Marie
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Neurology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). Uppsala Univ, Sweden.
    Gauffin, Helena
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Neurology in Linköping.
    Engström, Maria
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Structural anomaly in the reticular formation in narcolepsy type 1, suggesting lower levels of neuromelanin2019In: NeuroImage: Clinical, E-ISSN 2213-1582, Vol. 23, article id 101875Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to investigate structural changes in the brain stem of adolescents with narcolepsy, a disorder characterized by excessive daytime sleepiness, fragmented night-time sleep, and cataplexy. For this purpose, we used quantitative magnetic resonance imaging to obtain R1 and R2 relaxation rates, proton density, and myelin maps in adolescents with narcolepsy (n = 14) and healthy controls (n = 14). We also acquired resting state functional magnetic resonance imaging (fMRI) for brainstem connectivity analysis. We found a significantly lower R2 in the rostral reticular formation near the superior cerebellar peduncle in narcolepsy patients, family wise error corrected p = .010. Narcolepsy patients had a mean R2 value of 1.17 s(-1) whereas healthy controls had a mean R2 of 1.31 s(-1), which was a large effect size with Cohen d = 4.14. We did not observe any significant differences in R1 relaxation, proton density, or myelin content. The sensitivity of R2 to metal ions in tissue and the transition metal ion chelating property of neuromelanin indicate that the R2 deviant area is one of the neuromelanin containing nuclei of the brain stem. The close proximity and its demonstrated involvement in sleep-maintenance, specifically through orexin projections from the hypothalamus regulating sleep stability, as well as the results from the connectivity analysis, suggest that the observed deviant area could be the locus coeruleus or other neuromelanin containing nuclei in the proximity of the superior cerebellar peduncle. Hypothetically, the R2 differences described in this paper could be due to lower levels of neuromelanin in this area of narcolepsy patients.

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  • 35.
    Ouellette, R.
    et al.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden; A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
    Mangeat, G.
    A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
    Polyak, I.
    Invicro, Boston, MA, United States.
    Warntjes, Marcel, Jan Bertus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR, Linköping, Sweden.
    Forslin, Y.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.
    Bergendal, Å.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Medical Psychology, Karolinska University Hospital, Stockholm, Sweden.
    Plattén, M.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biology, and Health, Royal Institute of Technology, Stockholm, Sweden.
    Uppman, M.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.
    Treaba, C.A.
    A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
    Cohen-Adad, J.
    NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
    Piehl, F.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Kristoffersen, Wiberg M.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.
    Fredrikson, S.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
    Mainero, C.
    A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
    Granberg, T.
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden; A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
    Validation of Rapid Magnetic Resonance Myelin Imaging in Multiple Sclerosis2020In: Annals of Neurology, ISSN 0364-5134, E-ISSN 1531-8249, Vol. 87, no 5, p. 710-724Article in journal (Refereed)
    Abstract [en]

    Objective: Magnetic resonance imaging (MRI) is essential for multiple sclerosis diagnostics but is conventionally not specific to demyelination. Myelin imaging is often hampered by long scanning times, complex postprocessing, or lack of clinical approval. This study aimed to assess the specificity, robustness, and clinical value of Rapid Estimation of Myelin for Diagnostic Imaging, a new myelin imaging technique based on time-efficient simultaneous T1/T2 relaxometry and proton density mapping in multiple sclerosis. Methods: Rapid myelin imaging was applied using 3T MRI ex vivo in 3 multiple sclerosis brain samples and in vivo in a prospective cohort of 71 multiple sclerosis patients and 21 age/sex-matched healthy controls, with scan–rescan repeatability in a subcohort. Disability in patients was assessed by the Expanded Disability Status Scale and the Symbol Digit Modalities Test at baseline and 2-year follow-up. Results: Rapid myelin imaging correlated with myelin-related stains (proteolipid protein immunostaining and Luxol fast blue) and demonstrated good precision. Multiple sclerosis patients had, relative to controls, lower normalized whole-brain and normal-appearing white matter myelin fractions, which correlated with baseline cognitive and physical disability. Longitudinally, these myelin fractions correlated with follow-up physical disability, even with correction for baseline disability. Interpretation: Rapid Estimation of Myelin for Diagnostic Imaging provides robust myelin quantification that detects diffuse demyelination in normal-appearing tissue in multiple sclerosis, which is associated with both cognitive and clinical disability. Because the technique is fast, with automatic postprocessing and US Food and Drug Administration/CE clinical approval, it can be a clinically feasible biomarker that may be suitable to monitor myelin dynamics and evaluate treatments aiming at remyelination.

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  • 36.
    Persson, Anders
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Baeckmann, John
    Natl Board Forens Med Linkoping, Dept Forens Med, Linkoping, Sweden.
    Berge, Johan
    Natl Board Forens Med Linkoping, Dept Forens Med, Linkoping, Sweden.
    Jackowski, Christian
    Univ Bern, Switzerland.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Zech, Wolf-Dieter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Natl Board Forens Med Linkoping, Dept Forens Med, Linkoping, Sweden; Univ Bern, Switzerland.
    Temperature-corrected postmortem 3-T MR quantification of histopathological early acute and chronic myocardial infarction: a feasibility study2018In: International journal of legal medicine, ISSN 0937-9827, E-ISSN 1437-1596, Vol. 132, no 2, p. 541-549Article in journal (Refereed)
    Abstract [en]

    The goal of the present study was to evaluate if quantitative postmortem cardiac 3-T magnetic resonance (QPMCMR) T1 and T2 relaxation times and proton density values of histopathological early acute and chronic myocardial infarction differ to the quantitative values of non-pathologic myocardium and other histopathological age stages of myocardial infarction with regard to varying corpse temperatures. In 60 forensic corpses (25 female, 35 male), a cardiac 3-T MR quantification sequence was performed prior to autopsy and cardiac dissection. Core body temperature was assessed during MR examinations. Focal myocardial signal alterations in synthetically generated MR images were measured for their T1, T2, and proton density (PD) values. Locations of signal alteration measurements in PMCMR were targeted at heart dissection, and myocardial tissue specimens were taken for histologic examinations. Quantified signal alterations in QPMCMR were correlated to their according histologic age stage of myocardial infarction, and quantitative values were corrected for a temperature of 37 A degrees C. In QPMCMR, 49 myocardial signal alterations were detected in 43 of 60 investigated hearts. Signal alterations were diagnosed histologically as early acute (n = 16), acute (n = 10), acute with hemorrhagic component (n = 9), subacute (n = 3), and chronic (n = 11) myocardial infarction. Statistical analysis revealed that based on their temperature-corrected quantitative T1, T2, and PD values, a significant difference between early acute, acute, and chronic myocardial infarction can be determined. It can be concluded that quantitative 3-T postmortem cardiac MR based on temperature-corrected T1, T2, and PD values may be feasible for pre-autopsy diagnosis of histopathological early acute, acute, and chronic myocardial infarction, which needs to be confirmed histologically.

  • 37.
    Schwendener, Nicole
    et al.
    University of Bern, Switzerland.
    Jackowski, Christian
    University of Bern, Switzerland.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Schuster, Frederick
    University of Bern, Switzerland; Hospital and University of Bern Inselspital, Switzerland.
    Riva, Fabiano
    University of Bern, Switzerland.
    Zech, Wolf-Dieter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. University of Bern, Switzerland.
    Detection and differentiation of early acute and following age stages of myocardial infarction with quantitative post-mortem cardiac 1.5 T MR2017In: Forensic Science International, ISSN 0379-0738, E-ISSN 1872-6283, Vol. 270, p. 248-254Article in journal (Refereed)
    Abstract [en]

    Recently, quantitative MR sequences have started being used in post-mortem imaging. The goal of the present study was to evaluate if early acute and following age stages of myocardial infarction can be detected and discerned by quantitative 1.5 T post-mortem cardiac magnetic resonance (PMCMR) based on quantitative T1, T2 and PD values. In 80 deceased individuals (25 female, 55 male), a cardiac MR quantification sequence was performed prior to cardiac dissection at autopsy in a prospective study. Focal myocardial signal alterations detected in synthetically generated MR images were MR quantified for their T1, T2 and PD values. The locations of signal alteration measurements in PMCMR were targeted at autopsy heart dissection and cardiac tissue specimens were taken for histologic examinations. Quantified signal alterations in PMCMR were correlated to their according histologic age stage of myocardial infarction. In PMCMR seventy-three focal myocardial signal alterations were detected in 49 of 80 investigated hearts. These signal alterations were diagnosed histologically as early acute (n = 39), acute (n = 14), subacute (n = 10) and chronic (n = 10) age stages of myocardial infarction. Statistical analysis revealed that based on their quantitative T1, T2 and PD values, a significant difference between all defined age groups of myocardial infarction can be determined. It can be concluded that quantitative 1.5 T PMCMR quantification based on quantitative T1, T2 and PD values is feasible for characterization and differentiation of early acute and following age stages of myocardial infarction. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

  • 38.
    Schwendener, Nicole
    et al.
    University of Bern, Switzerland.
    Jackowski, Christian
    University of Bern, Switzerland.
    Schuster, Frederick
    University of Bern, Switzerland.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Zech, Wolf-Dieter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. University of Bern, Switzerland.
    Temperature-corrected post-mortem 1.5 T MRI quantification of non-pathologic upper abdominal organs2017In: International journal of legal medicine, ISSN 0937-9827, E-ISSN 1437-1596, Vol. 131, no 5, p. 1369-1376Article in journal (Refereed)
    Abstract [en]

    The present study aimed to evaluate if simultaneous temperature-corrected T1, T2, and proton density (PD) 1.5 T post-mortem MR quantification [quantitative post-mortem magnetic resonance imaging (QPMMRI)] is feasible for characterizing and discerning non-pathologic upper abdominal organs (liver, spleen, pancreas, kidney) with regard to varying body temperatures. QPMMRI was performed on 80 corpses (25 females, 55 males; mean age 56.2 years, SD 17.2) prior to autopsy. Core body temperature was measured during QPMMRI. Quantitative T1, T2, and PD values were measured in the liver, pancreas, spleen, and left kidney and temperature corrected to 37 A degrees C. Histologic examinations were conducted on each measured organ to determine non-pathologic organs. Quantitative T1, T2, and PD values of non-pathologic organs were ANOVA tested against values of other non-pathologic organ types. Based on temperature-corrected quantitative T1, T2, and PD values, ANOVA testing verified significant differences between the non-pathologic liver, spleen, pancreas, and left kidneys. Temperature-corrected 1.5 T QPMMRI based on T1, T2, and PD values may be feasible for characterization and differentiation of the non-pathologic liver, spleen, pancreas, and kidney. The results may provide a base for future specific pathology diagnosis of upper abdominal organs in post-mortem imaging.

  • 39.
    Tisell, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Warntjes, Jan Bertus Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Aalto, Arne
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Landtblom, Anne-Marie
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Neurology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology. Östergötlands Läns Landsting, Local Health Care Services in West Östergötland, Department of Medical Specialist in Motala.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Increased Concentrations of Glutamate and Glutamine in Normal Appearing White Matter of Patients with Multiple Sclerosis and Normal MR Imaging Brain Scans2013In: PLOS ONE, E-ISSN 1932-6203, Vol. 8, no 4Article in journal (Refereed)
    Abstract [en]

    In Multiple Sclerosis (MS) the relationship between disease process in normal-appearing white matter (NAWM) and the development of white matter lesions is not well understood. In this study we used single voxel proton ‘Quantitative Magnetic Resonance Spectroscopy’ (qMRS) to characterize the NAWM and thalamus both in atypical ‘Clinically Definite MS’ (CDMS) patients, MRIneg (N = 15) with very few lesions (two or fewer lesions), and in typical CDMS patients, MRIpos (N = 20) with lesions, in comparison with healthy control subjects (N = 20). In addition, the metabolite concentrations were also correlated with extent of brain atrophy measured using Brain Parenchymal Fraction (BPF) and severity of the disease measured using ‘Multiple Sclerosis Severity Score’ (MSSS). Elevated concentrations of glutamate and glutamine (Glx) were observed in both MS groups (MRIneg 8.12 mM, p<0.001 and MRIpos 7.96 mM p<0.001) compared to controls, 6.76 mM. Linear regressions of Glx and total creatine (tCr) with MSSS were 0.16±0.06 mM/MSSS (p = 0.02) for Glx and 0.06±0.03 mM/MSSS (p = 0.04) for tCr, respectively. Moreover, linear regressions of tCr and myo-Inositol (mIns) with BPF were −6.22±1.63 mM/BPF (p<0.001) for tCr and −7.71±2.43 mM/BPF (p = 0.003) for mIns. Furthermore, the MRIpos patients had lower N-acetylaspartate and N-acetylaspartate-glutamate (tNA) and elevated mIns concentrations in NAWM compared to both controls (tNA: p = 0.04 mIns p<0.001) and MRIneg (tNA: p = 0.03 , mIns: p = 0.002). The results suggest that Glx may be an important marker for pathology in non-lesional white matter in MS. Moreover, Glx is related to the severity of MS independent of number of lesions in the patient. In contrast, increased glial density indicated by increased mIns and decreased neuronal density indicated by the decreased tNA, were only observed in NAWM of typical CDMS patients with white matter lesions.

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  • 40.
    Tisell, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Warntjes, Jan Bertus Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Procedure for Quantitative 1H Magnetic Resonance Spectroscopy and Tissue Characterization of Human Brain Tissue Based on the Use of Quantitative Magnetic Resonance Imaging2013In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 70, no 4, p. 905-915Article in journal (Refereed)
    Abstract [en]

    PurposeExisting methods for quantitative magnetic resonance spectroscopy are not widely used for magnetic resonance spectroscopy examinations in clinical practice due to the lengthy and difficult workflow. In this report, we aimed to investigate whether metabolite concentrations show co-variation with relaxation parameters (R-1,R-H2O,R-2,R-H2O), water concentration (C-H2O), and age, using a quantitative magnetic resonance spectroscopy method, which is suitable for a clinical setting. MethodsWe performed 166 single voxel magnetic resonance spectroscopy measurements in the white matter and thalamus in 47 healthy subjects, aged 18-72 years. Whole brain R-1,R-H2O, R-2,R-H2O, and C-H2O maps were determined for each subject using quantitative magnetic resonance imaging. Absolute metabolite concentrations were calculated by calibrating the water-scaled magnetic resonance spectroscopy, using the quantitative magnetic resonance imaging maps of R-1,R-H2O, R-2,R-H2O, and C-H2O. ResultsAbsolute concentrations in white matter of total Creatine and myo-Inositol were correlated with age (total Creatine: 12 4 M/year, P < 0.01; myo-Inositol: 23 +/- 9 M/year, P < 0.05), suggesting a process of increased glia density in aging white matter. Moreover, total Creatine and total N-acetylaspartate were inversely correlated with the R-1,R-H2O and positively correlated with the C-H2O of white matter. In addition, the Cramer-Rao lower bound was biased regarding the metabolite concentration, suggesting that should not be used as a quality assessment. ConclusionThe implemented method was fast, robust, and user-independent.

  • 41.
    Tisell, Anders
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Warntjes, Marcel, Jan Bertus
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Engström, Maria
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Landtblom, Anne-Marie
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Absolute quantification of LCModel water scaled metabolite concentration of 1H magnetic resonance spectroscopy (MRS) using quantitative magnetic resoonance imaging (qMRI)2008In: ESMRMB,2008, 2008Conference paper (Other academic)
    Abstract [en]

      

  • 42.
    Tisell, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Warntjes, Marcel
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences. Linköping University, Faculty of Health Sciences.
    West, Janne
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Department of Medicine and Health Sciences, Radiology. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Absolute quantification of 1H Magnetic Resonance Spectroscopy of human brain using qMRI2009Conference paper (Other academic)
  • 43.
    Vagberg, M.
    et al.
    Umeå University, Sweden.
    Lindqvist, T.
    Umeå University, Sweden.
    Ambarki, K.
    Umeå University, Sweden Umeå University, Sweden.
    Warntjes, Jan Bertus Marcel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Sundstrom, P.
    Umeå University, Sweden.
    Birgander, R.
    Umeå University, Sweden.
    Svenningsson, A.
    Umeå University, Sweden.
    Automated Determination of Brain Parenchymal Fraction in Multiple Sclerosis2013In: American Journal of Neuroradiology, ISSN 0195-6108, E-ISSN 1936-959X, Vol. 34, no 3, p. 498-504Article in journal (Refereed)
    Abstract [en]

    BACKGROUND AND PURPOSE: Brain atrophy is a manifestation of tissue damage in MS. Reduction in brain parenchymal fraction is an accepted marker of brain atrophy. In this study, the approach of synthetic tissue mapping was applied, in which brain parenchymal fraction was automatically calculated based on absolute quantification of the tissue relaxation rates R1 and R2 and the proton attenuation. MATERIALS AND METHODS: The BPF values of 99 patients with MS and 35 control subjects were determined by using SyMap and tested in relationship to clinical variables. A subset of 5 patients with MS and 5 control subjects were also analyzed with a manual segmentation technique as a reference. Reproducibility of SyMap was assessed in a separate group of 6 healthy subjects, each scanned 6 consecutive times. RESULTS: Patients with MS had significantly lower BPF (0.852 0.0041, mean +/- SE) compared with control subjects (0.890 +/- 0.0040). Significant linear relationships between BPF and age, disease duration, and Expanded Disability Status Scale scores were observed (P less than .001). A strong correlation existed between SyMap and the reference method (r = 0.96; P less than .001) with no significant difference in mean BPF. Coefficient of variation of repeated SyMap BPF measurements was 0.45%. Scan time was less than6 minutes, and postprocessing time was less than2 minutes. CONCLUSIONS: SyMap is a valid and reproducible method for determining BPF in MS within a clinically acceptable scan time and postprocessing time. Results are highly congruent with those described using other methods and show high agreement with the manual reference method.

  • 44.
    Virhammar, J.
    et al.
    Uppsala University, Sweden.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR, Linkoping, Sweden.
    Laurell, K.
    Umeå University, Sweden.
    Larsson, E. -M.
    Uppsala University, Sweden.
    Quantitative MRI for Rapid and User-Independent Monitoring of Intracranial CSF Volume in Hydrocephalus2016In: American Journal of Neuroradiology, ISSN 0195-6108, E-ISSN 1936-959X, Vol. 37, no 5, p. 797-801Article in journal (Refereed)
    Abstract [en]

    BACKGROUND AND PURPOSE: Quantitative MR imaging allows segmentation of different tissue types and automatic calculation of intracranial volume, CSF volume, and brain parenchymal fraction. Brain parenchymal fraction is calculated as (intracranial volume - CSF volume) / intracranial volume. The purpose of this study was to evaluate whether the automatic calculation of intracranial CSF volume or brain parenchymal fraction could be used as an objective method to monitor volume changes in the ventricles. MATERIALS AND METHODS: A lumbar puncture with drainage of 40 mL of CSF was performed in 23 patients under evaluation for idiopathic normal pressure hydrocephalus. Quantitative MR imaging was performed twice within 1 hour before the lumbar puncture and was repeated 30 minutes, 4 hours, and 24 hours afterward. For each time point, the volume of the lateral ventricles was manually segmented and total intracranial CSF volume and brain parenchymal fraction were automatically calculated by using Synthetic MR postprocessing. RESULTS: At 30 minutes after the lumbar puncture, the volume of the lateral ventricles decreased by 5.6 +/- 1.9 mL (P &lt; .0001) and the total intracranial CSF volume decreased by 11.3 +/- 5.6 mL (P &lt; .001), while brain parenchymal fraction increased by 0.78% +/- 0.41% (P &lt; .001). Differences were significant for manual segmentation and brain parenchymal fraction even at 4 hours and 24 hours after the lumbar tap. There was a significant association using a linear mixed model between change in manually segmented ventricular volume and change in brain parenchymal fraction and total CSF volume, (P &lt; .0001). CONCLUSIONS: Brain parenchymal fraction is provided rapidly and fully automatically with Synthetic MRI and can be used to monitor ventricular volume changes. The method may be useful for objective clinical monitoring of hydrocephalus.

  • 45.
    Vågberg, Mattias
    et al.
    Department of Clinical Neurosci, Umeå.
    Lindqvist, Thomas
    Department of Radiation Science, Umeå.
    Warntjes, Marcel Jan Bertus
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    Sundström, Peter
    Department of Clinical Neurosci, Umeå.
    Birgander, Richard
    Department of Radiation Science, Umeå.
    Svenningsson, Anders
    Department of Clinical Neurosci, Umeå.
    Automated Determination of Brain Parenchymal Fraction in Multiple Sclerosis in NEUROLOGY, vol 78, issue , pp2012In: NEUROLOGY, American Academy of Neurology (AAN) , 2012, Vol. 78Conference paper (Refereed)
    Abstract [en]

    n/a

  • 46.
    Warntjes, J.B.M.
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Novel method for rapid, simultaneous T1, T*2, and proton density quantification2007In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 57, no 3, p. 528-537Article in journal (Refereed)
    Abstract [en]

    An imaging method called “quantification of relaxation times and proton density by twin-echo saturation-recovery turbo-field echo” (QRAPTEST) is presented as a means of quickly determining the longitudinal T1 and transverse T relaxation time and proton density (PD) within a single sequence. The method also includes an estimation of the B1 field inhomogeneity. High-resolution images covering large volumes can be achieved within clinically acceptable times of 5–10 min. The range of accuracy for determining T1, T, and PD values is flexible and can be optimized relative to any anticipated values. We validated the experimental results against existing methods, and provide a clinical example in which quantification of the whole brain using 1.5 mm3 voxels was achieved in less than 8 min.

  • 47.
    Warntjes, Marcel
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Blystad, Ida
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Tisell, Anders
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    Landtblom, Anne-Marie
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Clinical and Experimental Medicine, Neurology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    Multiparametric Quantitative Magnetic Resonance Imaging of the Normal Appearing Brain in Multiple Sclerosis2012Conference paper (Other academic)
  • 48.
    Warntjes, Marcel
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Department of Medicine and Health Sciences, Radiology. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Method for rapid, high-resolution, whole volume T1, T2* and proton density quantification2006Conference paper (Other academic)
  • 49.
    Warntjes, Marcel
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiation Physics. Linköping University, Department of Medicine and Health Sciences, Radiology. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    The 5 Minutes Exam using Rapid Quantification of T1, T2* and Proton Density2007Conference paper (Other academic)
  • 50.
    Warntjes, Marcel Jan Bertus
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). SyntheticMR AB, Linkoping, Sweden.
    Blystad, Ida
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Tisell, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Larsson, E. -M.
    Uppsala Univ, Sweden.
    Synthesizing a Contrast-Enhancement Map in Patients with High-Grade Gliomas Based on a Postcontrast MR Imaging Quantification Only2018In: American Journal of Neuroradiology, ISSN 0195-6108, E-ISSN 1936-959X, Vol. 39, no 12, p. 2194-2199Article in journal (Refereed)
    Abstract [en]

    BACKGROUND AND PURPOSE: Administration of a gadolinium-based contrast agent is an important diagnostic biomarker for blood-brain barrier damage. In clinical use, detection is based on subjective comparison of native and postgadolinium-based contrast agent T1-weighted images. Quantitative MR imaging studies have suggested a relation between the longitudinal relaxation rate and proton-density in the brain parenchyma, which is disturbed by gadolinium-based contrast agents. This discrepancy can be used to synthesize a contrast-enhancement map based solely on the postgadolinium-based contrast agent acquisition. The aim of this study was to compare synthetic enhancement maps with subtraction maps of native and postgadolinium-based contrast agent images. MATERIALS AND METHODS: For 14 patients with high-grade gliomas, quantitative MR imaging was performed before and after gadolinium-based contrast agent administration. The quantification sequence was multidynamic and multiecho, with a scan time of 6 minutes. The 2 image stacks were coregistered using in-plane transformation. The longitudinal relaxation maps were subtracted and correlated with the synthetic longitudinal relaxation enhancement maps on the basis of the postgadolinium-based contrast agent images only. ROIs were drawn for tumor delineation. RESULTS: Linear regression of the subtraction and synthetic longitudinal relaxation enhancement maps showed a slope of 1.02 0.19 and an intercept of 0.05 +/- 0.12. The Pearson correlation coefficient was 0.861 +/- 0.059, and the coefficient of variation was 0.18 +/- 0.04. On average, a volume of 1.71 +/- 1.28 mL of low-intensity enhancement was detected in the synthetic enhancement maps outside the borders of the drawn ROI. CONCLUSIONS: The study shows that there was a good correlation between subtraction longitudinal relaxation enhancement maps and synthetic longitudinal relaxation enhancement maps in patients with high-grade gliomas. The method may improve the sensitivity and objectivity for the detection of gadolinium-based contrast agent enhancement.

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