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  • 151.
    Romu, Thobias
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    MANA - Multi scale adaptive normalized averaging2011In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE conference proceedings, 2011, p. 361-364Conference paper (Refereed)
    Abstract [en]

    It is possible to correct intensity inhomogeneity in fat–water Magnetic Resonance Imaging (MRI) by estimating a bias field based on the observed intensities of voxels classified as the pure adipose tissue. The same procedure can also be used to quantify fat volume and its distribution which opens up for new medical applications. The bias field estimation method has to be robust since pure fat voxels are irregularly located and the density varies greatly within and between image volumes. This paper introduces Multi scale Adaptive Normalized Average (MANA) that solves this problem bybasing the estimate on a scale space of weighted averages. By usingthe local certainty of the data MANA preserves details where the local data certainty is high and provides realistic values in sparse areas.

  • 152.
    Romu, Thobias
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Camilla, Vavruch
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlqvist Leinhard, Olof
    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). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Tallberg, Joakim
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlström, Nils
    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). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Persson, 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). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Heglind, Mikael
    University of Gothenburg, Gothenburg, Sweden..
    Lidell, Martin
    University of Gothenburg, Gothenburg, Sweden..
    Enerbäck, Sven
    University of Gothenburg, Gothenburg, Sweden..
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology.
    A randomized trial of cold-exposure on energy expenditure and supraclavicular brown adipose tissue volume in humans2016In: Metabolism: Clinical and Experimental, ISSN 0026-0495, E-ISSN 1532-8600, Vol. 65, no 6, p. 926-934Article in journal (Refereed)
    Abstract [en]

    Objective

    To study if repeated cold-exposure increases metabolic rate and/or brown adipose tissue (BAT) volume in humans when compared with avoiding to freeze.

    Design

    Randomized, open, parallel-group trial.

    Methods

    Healthy non-selected participants were randomized to achieve cold-exposure 1 hour/day, or to avoid any sense of feeling cold, for 6 weeks. Metabolic rate (MR) was measured by indirect calorimetry before and after acute cold-exposure with cold vests and ingestion of cold water. The BAT volumes in the supraclavicular region were measured with magnetic resonance imaging (MRI).

    Results

    Twenty-eight participants were recruited, 12 were allocated to controls and 16 to cold-exposure. Two participants in the cold group dropped out and one was excluded. Both the non-stimulated and the cold-stimulated MR were lowered within the group randomized to avoid cold (MR at room temperature from 1841 ± 199 kCal/24 h to 1795 ± 213 kCal/24 h, p = 0.047 cold-activated MR from 1900 ± 150 kCal/24 h to 1793 ± 215 kCal/24 h, p = 0.028). There was a trend towards increased MR at room temperature following the intervention in the cold-group (p = 0.052). The difference between MR changes by the interventions between groups was statistically significant (p = 0.008 at room temperature, p = 0.032 after cold-activation). In an on-treatment analysis after exclusion of two participants that reported ≥ 8 days without cold-exposure, supraclavicular BAT volume had increased in the cold-exposure group (from 0.0175 ± 0.015 l to 0.0216 ± 0.014 l, p = 0.049).

    Conclusions

    We found evidence for plasticity in metabolic rate by avoiding to freeze compared with cold-exposure in a randomized setting in non-selected humans.

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  • 153.
    Romu, Thobias
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Forsgren, Mikael
    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.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Gastroenterology and Hepatology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Dahlström, Nils
    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.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology 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, Center for Diagnostics, Department of Radiology in Linköping.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. 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.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Fat Water Classification of Symmetrically Sampled Two-Point Dixon Images Using Biased Partial Volume Effects2011In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2011), 2011., 2011Conference paper (Refereed)
  • 154.
    Romu, Thobias
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    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. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Robust fat-­‐water separation of symmetrically sampled two point Dixon data2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Refereed)
  • 155.
    Romu, Thobias
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Robust Water Fat Separated Dual-Echo MRI by Phase-Sensitive Reconstruction2017In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, no 3, p. 1208-1216Article in journal (Refereed)
    Abstract [en]

    Purpose: To develop and evaluate a robust water-fat separation method for T1-weighted symmetric two-point Dixon data.

    Methods: A method for water-fat separation by phase unwrapping of the opposite-phase images by phase-sensitive reconstruction (PSR) is introduced. PSR consists of three steps; 1, identification of clusters of tissue voxels; 2, unwrapping of the phase in each cluster by solving Poisson’s equation; 3, find the correct sign of each unwrapped opposite-phase cluster, so that the water-fat images are assigned the correct identities. The robustness was evaluated by counting the number of water-fat swap artifacts in a total of 733 image volumes. The method was also compared to commercial software.

    Results: In the water-fat separated image volumes, the PSR method failed to unwrap the phase of one cluster and misclassified 10. One swap was observed in areas affected by motion and was constricted to the affected area. Twenty swaps were observed surrounding susceptibility artifacts, none of which spread outside the artifact affected regions. The PSR method had fewer swaps when compared to commercial software.

    Conclusion: The PSR method can robustly produce water-fat separated whole-body images based on symmetric two-echo spoiled gradient echo images, under both ideal conditions and in the presence of common artifacts.

  • 156.
    Romu, Thobias
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Elander, Louise
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. 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.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    High resolution symmetrically sampled two point Dixon imaging2012In: Proceedings of the ISMRM Annual Meeting, 2012Conference paper (Other academic)
  • 157.
    Romu, Thobias
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Elander, Louise
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlqvist Leinhard, Olof
    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). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Lidell, Martin
    Göteborgs universitet.
    Betz, Matthias
    Klinikum der Ludwig Maximilians University, Munich.
    Persson, 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). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Enerbäck, Sven
    Göteborgs universitet.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Characterization of Brown Adipose Tissue by water-fat separated Magnetic Resonance Imaging2015In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 6, p. 1639-1645Article in journal (Refereed)
    Abstract [en]

    Purpose: To evaluate the possibility of quantifying brown adipose tissue (BAT) volume and fat concentration with a high resolution, long TE, dual-echo Dixon imaging protocol.

    Materials and methods: A 0.42 mm isotropic resolution water-fat separated MRI protocol was implemented by utilizing the second opposite-phase echo and third in-phase echo. Fat images were calibrated with regard to the intensity of nearby white adipose tissue (WAT) to form relative fat content (RFC) images. To evaluate the ability to measure BAT volume and RFC contrast dynamics, rats were divided into two groups that were kept at 4° or 22° C for five days. The rats were then scanned in a 70 cm bore 3.0 T MRI scanner and a human dual energy CT. Interscapular, paraaortal and perirenal BAT (i/pa/pr-BAT) depots as well as WAT and muscle were segmented in the MRI and CT images. Biopsies were collected from the identified BAT depots.

    Results: The biopsies confirmed that the three depots identified with the RFC images consisted of BAT. There was a significant linear correlation (p <0.001) between the measured RFC and the Hounsfield units from DECT. Significantly lower iBAT RFC (p = 0.0064) and significantly larger iBAT and prBAT volumes (p=0.0017) were observed in the cold stimulated rats.

    Conclusions: The calibrated Dixon images with RFC scaling can depict BAT and be used to measure differences in volume, and fat concentration, induced by cold stimulation. The high correlation between RFC and HU suggests that the fat concentration is the main RFC image contrast mechanism.

  • 158.
    Romu, Thobias
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Linge, Jennifer
    Advanced MR Analytics AB, Linköping, Sweden.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    West, Janne
    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 Medicine and Health Sciences.
    Bell, Jimmy
    Westminster University, London, UK.
    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 Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Hepatic Steatosis is Associated with Lower Prior Health Care Burden in Visceral Obesity2017Conference paper (Refereed)
  • 159.
    Romu, Thobias
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    West, Janne
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Spetz, Anna-Clara
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Lindblom, Hanna
    Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences.
    Lindh Åstrand, Lotta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Hammar, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    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 Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    The effect of flip-angle on body composition using calibrated water-fat MRI.2016Conference paper (Other academic)
    Abstract [en]

    This study tested how the flip angle affects body composition analysis by MRI, if adipose tissue is used as an internal intensity reference. Whole-body water-fat images with flip angle 5° and 10° were collected from 29 women in an ongoing study. The images were calibrated based on the adipose tissue signal and whole-body total adipose, lean and soft tissue volumes were measured. A mean difference of 0.29 L, or 0.90 % of the average volume, and a coefficient of variation of 0.40 % was observed for adipose tissue.

  • 160.
    Rydell, Joakim
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Johansson, Andreas
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Leinard, Olof Dahlqvist
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Farnebäck, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    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.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Health Sciences.
    Three Dimensional Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging using Inverse Gradient2008In: Proceedings of the International Society for Magnetic Resonance in Medicine annual meeting (ISMRM'08), 2008, p. 1519-Conference paper (Other academic)
    Abstract [en]

    Three dimensional phase sensitive reconstruction on two point Dixon volumes has been implemented with use of the inverse gradient. The results has beencompared with the inverse gradient method in two dimensions as well as with the well established region growing method proposed by Ma. The inversegradient method in 3D is able to unwrap the phase field in uncertain regions where the region growing method and the inverse gradient method in 2D cometo a stop.

  • 161.
    Rydell, Joakim
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Johansson, Andreas
    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.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    MRI Phase Unwrapping with Application to Water/Fat Separation2008In: Proceedings of the SSBA Symposium on Image Analysis,2008, 2008, p. 27-30Conference paper (Other academic)
  • 162.
    Rydell, Joakim
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Pettersson, Johanna
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Johansson, Andreas
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    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 Medicine and Care, Radiation Physics. Linköping University, Department of Medicine and Care, Medical Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging Using Inverse Gradient2007In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sebastien Ourselin and Anthony Maeder, Springer Berlin/Heidelberg, 2007, p. 210-218Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.

  • 163.
    Sandström, Per
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Surgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Surgery in Östergötland.
    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.
    Dahlström, Nils
    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.
    Freij, Anna
    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.
    Kihlberg, Johan
    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.
    Brismar, Torkel
    Karolinska University Hospital, Stockholm, Sweden .
    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.
    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.
    Upptag i levern av kontrastmedlet Gd-EOB-DTPA påverkas kraftigt av leverfunktionen2009Conference paper (Other academic)
  • 164.
    Schindler, Louise S.
    et al.
    Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Univ Oslo, Norway.
    Subramaniapillai, Sivaniya
    Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Univ Oslo, Norway.
    Barth, Claudia
    Univ Oslo, Norway; Univ Oslo, Norway; Diakonhjemmet Hosp, Norway.
    van der Meer, Dennis
    Univ Oslo, Norway; Univ Oslo, Norway; Maastricht Univ, Netherlands.
    Pedersen, Mads L.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Kaufmann, Tobias
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Tubingen, Germany.
    Maximov, Ivan I.
    Univ Oslo, Norway; Univ Oslo, Norway; Western Norway Univ Appl Sci, Norway.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    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). AMRA Med AB, Linkoping, Sweden.
    Beck, Dani
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Gurholt, Tiril P.
    Univ Oslo, Norway; Univ Oslo, Norway.
    Voldsbekk, Irene
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Suri, Sana
    Univ Oxford, England; Univ Oxford, England.
    Ebmeier, Klaus P.
    Univ Oxford, England.
    Draganski, Bogdan
    Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Max Planck Inst Human Cognit & Brain Sci, Germany.
    Andreassen, Ole A.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Westlye, Lars T.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    de Lange, Ann-Marie G.
    Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Univ Oslo, Norway; Univ Oxford, England.
    Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women2022In: NeuroImage: Clinical, E-ISSN 2213-1582, Vol. 36, article id 103239Article in journal (Refereed)
    Abstract [en]

    The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.

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  • 165.
    Tejani, Sanaa
    et al.
    University of Texas Southwestern Medical School, Dallas, TX.
    McCoy, Cody
    Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX.
    Ayers, Colby R.
    Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX.
    Powell-Wiley, Tiffany M.
    Cardiovasular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD .
    Després, Jean-Pierre
    Department of Kinesiology, Faculty of Medicine, Université Laval and VITAM e Centre de rercherche en santé durable, CIUSSS Capitale-Nationale, Québec, QC, Canada.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. AMRA Medical AB, Linköping, Sweden.
    Dahlqvist Leinhard, Olof
    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). AMRA Medical AB, Linköping, Sweden.
    Petersson, Mikael
    AMRA Medical AB, Linköping, Sweden.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Health, Medicine and Caring Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping, Sweden.
    Neeland, Ian J.
    University Hospitals Harrington Heart and Vascular Institute and Case Western Reserve University School of Medicine, Cleveland, OH.
    Cardiometabolic Health Outcomes Associated With Discordant Visceral and Liver Fat Phenotypes: Insights From the Dallas Heart Study and UK Biobank2022In: Mayo Clinic proceedings, ISSN 0025-6196, E-ISSN 1942-5546, Vol. 97, no 2, p. 225-237Article in journal (Refereed)
    Abstract [en]

    Objective: To evaluate the cardiometabolic outcomes associated with discordant visceral adipose tissue (VAT) and liver fat (LF) phenotypes in 2 cohorts.

    Patients and Methods: Participants in the Dallas Heart Study underwent baseline imaging from January 1, 2000, through December 31, 2002, and were followed for incident cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) through 2013. Associations between VAT-LF groups (low-low, high-low, low-high, and high-high) and outcomes were assessed using multivariable- adjusted regression and were replicated in the independent UK Biobank.

    Results: The Dallas Heart Study included 2064 participants (mean SD age, 449 years; 54% female; 47% black). High VATehigh LF and high VATelow LF were associated with prevalent atheroscle- rosis, whereas low VATehigh LF was not. Of 1731 participants without CVD/T2DM, 128 (7.4%) developed CVD and 95 (5.5%) T2DM over a median of 12 years. High VATehigh LF and high VATelow LF were associated with increased risk of CVD (hazard ratios [HRs], 2.0 [95% CI, 1.3 to 3.2] and 2.4 [95% CI, 1.4 to 4.1], respectively) and T2DM (odds ratios [ORs], 7.8 [95% CI, 3.8 to 15.8] and 3.3 [95% CI, 1.4 to 7.8], respectively), whereas low VATehigh LF was associated with T2DM (OR, 2.7 [95% CI, 1.1 to 6.7]). In the UK Biobank (N1⁄422,354; April 2014-May 2020), only high VATelow LF remained associated with CVD after multivariable adjustment for age and body mass index (HR, 1.5 [95% CI, 1.2 to 1.9]).

    Conclusion: Although VAT and LF are each associated with cardiometabolic risk, these observations demonstrate the importance of separating their cardiometabolic implications when there is presence or absence of either or both in an individual.

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  • 166.
    Thomas, Marianna
    et al.
    Department of Radiology, Norfolk & Norwich University Hospital, United Kingdom .
    Newman, David
    Department of Radiology, Norfolk & Norwich University Hospital, United Kingdom .
    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.
    Kasmai, Bahman
    Department of Radiology, Norfolk & Norwich University Hospital, United Kingdom .
    Malcolm, Paul
    Department of Radiology, Norfolk & Norwich University Hospital, United Kingdom .
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Toms, Andoni P.
    Department of Radiology, Norfolk & Norwich University Hospital, United Kingdom .
    Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system2013Conference paper (Other academic)
  • 167.
    Thomas, Marianna S
    et al.
    Department of Radiology, Norfolk & Norwich University Hospital, Norwich, UK..
    Newman, David
    Department of Radiology, Norfolk & Norwich University Hospital, Norwich, UK..
    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.
    Karlsson, Anette
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Rosander, Johannes
    Advanced MR Analytics AB, Linköping, Sweden.
    Kasmai, Bahman
    Department of Radiology Norfolk & Norwich University Hospital, United Kingdom.
    Greenwood, Richard
    Department of Radiology Norfolk & Norwich University Hospital, United Kingdom.
    Malcolm, Paul N
    Department of Radiology Norfolk & Norwich University Hospital, United Kingdom.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Toms, Andoni P
    Department of Radiology Norfolk & Norwich University Hospital, United Kingdom.
    Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system2014In: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 24, no 9, p. 2279-2291Article in journal (Refereed)
    Abstract [en]

    PURPOSE:

    To measure the test-retest reproducibility of an automated system for quantifying whole body and compartmental muscle volumes using wide bore 3 T MRI.

    MATERIALS AND METHODS:

    Thirty volunteers stratified by body mass index underwent whole body 3 T MRI, two-point Dixon sequences, on two separate occasions. Water-fat separation was performed, with automated segmentation of whole body, torso, upper and lower leg volumes, and manually segmented lower leg muscle volumes.

    RESULTS:

    Mean automated total body muscle volume was 19·32 L (SD9·1) and 19·28 L (SD9·12) for first and second acquisitions (Intraclass correlation coefficient (ICC) = 1·0, 95 % level of agreement -0·32-0·2 L). ICC for all automated test-retest muscle volumes were almost perfect (0·99-1·0) with 95 % levels of agreement 1.8-6.6 % of mean volume. Automated muscle volume measurements correlate closely with manual quantification (right lower leg: manual 1·68 L (2SD0·6) compared to automated 1·64 L (2SD 0·6), left lower leg: manual 1·69 L (2SD 0·64) compared to automated 1·63 L (SD0·61), correlation coefficients for automated and manual segmentation were 0·94-0·96).

    CONCLUSION:

    Fully automated whole body and compartmental muscle volume quantification can be achieved rapidly on a 3 T wide bore system with very low margins of error, excellent test-retest reliability and excellent correlation to manual segmentation in the lower leg.

    KEY POINTS:

    • Sarcopaenia is an important reversible complication of a number of diseases. • Manual quantification of muscle volume is time-consuming and expensive. • Muscles can be imaged using in and out of phase MRI. • Automated atlas-based segmentation can identify muscle groups. • Automated muscle volume segmentation is reproducible and can replace manual measurements.

  • 168.
    Thunón, Patrik
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Zanjanis, Sepher
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Gjellan, Solveig
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Nyström, Fredrik
    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 Endocrinology.
    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.
    Smedby, Örjan
    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.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    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.
    Body Composition Volumetry by Whole-Body Water-Fat Separated MRI2014Conference paper (Other academic)
  • 169.
    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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  • 170.
    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.

  • 171.
    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]

      

  • 172.
    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)
  • 173.
    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.
    Engström, Maria
    Linköping University, Department of Medicine and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization, CMIV. 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.
    Karlsson, T
    Vigren, P
    Landtblom, Anne-Marie
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Clinical and Experimental Medicine, Psychiatry. Östergötlands Läns Landsting, Sinnescentrum, Department of Neurosurgery UHL. 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.
    Combining fMRI with qMRS for understanding the etiology of periodic hypersomnia2009Conference paper (Other academic)
  • 174.
    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).
    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).
    Karlsson, Thomas
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Behavioural Sciences and Learning. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Vigren, Patrik
    NSC.
    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, 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.
    Etiology of periodic hypersomnia explored by combined functional and molecular neuroimaging methods2008In: World Molecular Imaging Conference,,2008, 2008Conference paper (Other academic)
    Abstract [en]

      

  • 175.
    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, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    Mellergård, Johan
    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.
    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.
    Dahle, Charlotte
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Immunology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Immunology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Vrethem, Magnus
    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.
    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.
    Brain Atrophy in MS Patients Correlates with Creatine Concentrations2012Conference paper (Other academic)
  • 176.
    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, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    Mellergård, Johan
    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.
    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.
    Dahle, Charlotte
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Immunology. Linköping University, Faculty of Health Sciences.
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Immunology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Vrethem, Magnus
    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.
    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 the West of Östergötland, Department of Medical Specialist.
    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. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Increased Glia in Multiple Sclerosis Patients Correlates with Intrathecal Inflammation2011Conference paper (Refereed)
  • 177.
    Tisell, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical 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.
    Mellergård, Johan
    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.
    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.
    Dahle, Charlotte
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Immunology. Linköping University, Faculty of Health Sciences.
    Ernerudh, Jan
    Linköping University, Department of Clinical and Experimental Medicine, Clinical Immunology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Vrethem, Magnus
    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.
    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 the West of Östergötland, Department of Medical Specialist.
    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. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Multiple Sclerosis Severity Score (MSSS) Correlates With Changes in NAWM Metabolism During Treatment2011Conference paper (Refereed)
  • 178.
    Ulbrich, Erika J.
    et al.
    University Hospital, Switzerland; University of Zurich, Switzerland.
    Nanz, Daniel
    University Hospital, Switzerland; University of Zurich, Switzerland.
    Dahlqvist Leinhard, Olof
    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). Adv MR Analyt AB, Linkoping, Sweden.
    Marcon, Magda
    University Hospital, Switzerland; University of Zurich, Switzerland.
    Fischer, Michael A.
    University of Zurich, Switzerland.
    Whole-body adipose tissue and lean muscle volumes and their distribution across gender and age: MR-derived normative values in a normal-weight Swiss population2018In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 79, no 1, p. 449-458Article in journal (Refereed)
    Abstract [en]

    PurposeTo determine age- and gender-dependent whole-body adipose tissue and muscle volumes in healthy Swiss volunteers in Dixon MRI in comparison with anthropometric and bioelectrical impedance (BIA) measurements. MethodsFat-water-separated whole-body 3 Tesla MRI of 80 healthy volunteers (ages 20 to 62 years) with a body mass index (BMI) of 17.5 to 26.2kg/m(2) (10 men, 10 women per decade). Age and gender-dependent volumes of total adipose tissue (TAT), visceral adipose tissue (VAT), total abdominal subcutaneous adipose tissue (ASAT) and total abdominal adipose tissue (TAAT), and the total lean muscle tissue (TLMT) normalized for body height were determined by semi-automatic segmentation, and correlated with anthropometric and BIA measurements as well as lifestyle parameters. ResultsThe TAT, ASAT, VAT, and TLMT indexes (TATi, ASATi, VATi, and TLMTi, respectively) (L/m(2)standard deviation) for women/men were 6.4 +/- 1.8/5.3 +/- 1.7, 1.6 +/- 0.7/1.2 +/- 0.5, 0.4 +/- 0.2/0.8 +/- 0.5, and 5.6 +/- 0.6/7.1 +/- 0.7, respectively. The TATi correlated strongly with ASATi (ramp;gt;0.93), VATi, BMI and BIA (ramp;gt;0.70), and TAATi (ramp;gt;0.96), and weak with TLMTi for both genders (ramp;gt;-0.34). The VAT was the only parameter showing an age dependency (ramp;gt;0.32). The BMI and BIA showed strong correlation with all MR-derived adipose tissue volumes. The TAT mass was estimated significantly lower from BIA than from MRI (both genders Pamp;lt;.001; mean bias -5kg). ConclusionsThe reported gender-specific MRI-based adipose tissue and muscle volumes might serve as normative values. The estimation of adipose tissue volumes was significantly lower from anthropometric and BIA measurements than from MRI. Magn Reson Med 79:449-458, 2018. (c) 2017 International Society for Magnetic Resonance in Medicine.

  • 179.
    van der Meer, Dennis
    et al.
    Univ Oslo, Norway; Univ Oslo, Norway; Maastricht Univ, Netherlands.
    Gurholt, Tiril P.
    Univ Oslo, Norway; Univ Oslo, Norway.
    Sonderby, Ida E.
    Univ Oslo, Norway; Univ Oslo, Norway; Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Shadrin, Alexey A.
    Univ Oslo, Norway; Univ Oslo, Norway.
    Hindley, Guy
    Univ Oslo, Norway; Univ Oslo, Norway; Kings Coll London, England.
    Rahman, Zillur
    Univ Oslo, Norway; Univ Oslo, Norway.
    de Lange, Ann-Marie G.
    Univ Oslo, Norway; Univ Oslo, Norway; Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Univ Oxford, England.
    Frei, Oleksandr
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Dahlqvist Leinhard, Olof
    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). AMRA Med, Linkoping, Sweden.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med, Linkoping, Sweden.
    Simon, Rozalyn
    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).
    Beck, Dani
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Diakonhjemmet Hosp, Norway.
    Westlye, Lars T.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Halvorsen, Sigrun
    Oslo Univ Hosp Ulleval, Norway; Univ Oslo, Norway.
    Dale, Anders M.
    Univ Calif San Diego, CA 92037 USA.
    Karlsen, Tom H.
    Univ Oslo, Norway; Oslo Univ Hosp, Norway; Oslo Univ Hosp, Norway.
    Kaufmann, Tobias
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Tubingen, Germany.
    Andreassen, Ole A.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition2022In: Communications Biology, E-ISSN 2399-3642, Vol. 5, no 1, article id 1271Article in journal (Refereed)
    Abstract [en]

    A GWAS study of European individuals uncovers genetic associations between whole-body MRI derived measures and cardiometabolic diseases and highlights the key role of liver fat in cardiometabolic health. Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h(2 )= .25 vs. .13, p = 1.8x10(-7)). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r(g )= .49, p = 2.7x10(-22)). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.

  • 180.
    van der Meer, Dennis
    et al.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, The Netherlands.
    Gurholt, Tiril P.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
    Sønderby, Ida E.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo.
    Shadrin, Alexey A.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
    Hindley, Guy
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, United Kingdom.
    Rahman, Zillur
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
    de Lange, Ann-Marie G.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; LREN, Centre for Research in Neurosciences, Dept. of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Dept. of Psychiatry, University of Oxford, Oxford, UK.
    Frei, Oleksandr
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway.
    Dahlqvist Leinhard, Olof
    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). AMRA Medical, Linköping, Sweden.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Medical, Linköping, Sweden.
    Simon, Rozalyn
    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).
    Westlye, Lars T.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo; Department of Psychology, University of Oslo, Oslo, Norway.
    Halvorsen, Sigrun
    Department of Cardiology, Oslo University Hospital Ullevål, and University of Oslo, Oslo, Norway.
    Dale, Anders M.
    Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, CA 92037, USA.
    Karlsen, Tom H.
    Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital Rikshospitalet, Oslo, Norway; Research Institute for Internal Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway.
    Kaufmann, Tobias
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany.
    Andreassen, Ole A.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo.
    The role of liver fat in cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition2022Manuscript (preprint) (Other academic)
    Abstract [en]

    Background & Aims

    Obesity and associated morbidities, metabolic associated liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting the first genome-wide association study (GWAS) of these MRI-derived measures.

    Methods

    We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 34,036 White European UK Biobank participants (mean age of 64.5 years, 51.5% female).

    Results

    Through multivariate analysis, we discovered 108 loci with distributed effects across the body composition measures and 256 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h2=.25 vs. .16, p=1.4×10−6). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (rg=.48, p=1.6×10−22).

    Conclusions

    These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.

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    fulltext
  • 181.
    Vigren, Patrick
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Neurology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Neurosurgery.
    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, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    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.
    Karlsson, Thomas
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    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.
    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.
    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.
    Low Thalamic NAA-Concentration Corresponds to Strong Neural Activation in Working Memory in Kleine-Levin Syndrome2013In: PLOS ONE, E-ISSN 1932-6203, Vol. 8, no 2Article in journal (Refereed)
    Abstract [en]

    Background

    Kleine Levin Syndrome (KLS) is a rare disorder of periodic hypersomnia and behavioural disturbances in young individuals. It has previously been shown to be associated with disturbances of working memory (WM), which, in turn, was associated with higher activation of the thalamus with increasing WM load, demonstrated with functional magnetic resonance imaging (fMRI). In this study we aimed to further elucidate how these findings are related to the metabolism of the thalamus.

    Methods

    fMRI and magnetic resonance spectroscopy were applied while performing a WM task. Standard metabolites were examined: n-acetylaspartate (NAA), myo-inositol, choline, creatine and glutamate-glutamine. Fourteen KLS-patients and 15 healthy controls participated in the study. The patients with active disease were examined in asymptomatic periods.

    Results

    There was a statistically significant negative correlation between thalamic fMRI-activation and thalamic NAA, i.e., high fMRI-activation corresponded to low NAA-levels. This correlation was not seen in healthy controls. Thalamic levels of NAA in patients and controls showed no significant differences between the groups. None of the other metabolites showed any co-variation with fMRI-activiation.

    Conclusion

    This study shows negative correlation between NAA-levels and fMRI-activity in the left thalamus of KLS-patients while performing a WM task. This correlation could not be found in healthy control subjects, primarily interpreted as an effect of increased effort in the patient group upon performing the task. It might indicate a disturbance in the neuronal networks responsible for WM in KLS patients, resulting in higher effort at lower WM load, compared with healthy subjects. The general relationship between NAA and BOLD-signal is also discussed in the article.

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  • 182.
    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.

  • 183.
    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)
  • 184.
    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)
  • 185.
    Warntjes, Marcel, Jan Bertus
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dahlqvist, Olof
    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).
    West, Janne
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). 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, Centre of Surgery and Oncology, Department of Radiation Physics. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Rapid magnetic resonance quantification on the brain: Optimization for clinical usage2008In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 60, no 2, p. 320-329Article in journal (Refereed)
    Abstract [en]

    A method is presented for rapid simultaneous quantification of the longitudinal T1 relaxation, the transverse T2 relaxation, the proton density (PD), and the amplitude of the local radio frequency B 1 field. All four parameters are measured in one single scan by means of a multislice, multiecho, and multidelay acquisition. It is based on a previously reported method, which was substantially improved for routine clinical usage. The improvements comprise of the use of a multislice spin-echo technique, a background phase correction, and a spin system simulation to compensate for the slice-selective RF pulse profile effects. The aim of the optimization was to achieve the optimal result for the quantification of magnetic resonance parameters within a clinically acceptable time. One benchmark was high-resolution coverage of the brain within 5 min. In this scan time the measured intersubject standard deviation (SD) in a group of volunteers was 2% to 8%, depending on the tissue (voxel size = 0.8 x 0.8 x 5 mm). As an example, the method was applied to a patient with multiple sclerosis in whom the diseased tissue could clearly be distinguished from healthy reference values. Additionally it was shown that, using the approach of synthetic MRI, both accurate conventional contrast images as well as quantification maps can be generated based on the same scan. © 2008 Wiley-Liss, Inc.

  • 186.
    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. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    West, Janne
    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.
    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.
    Helms, G.
    University Medical Center, Göttingen, Germany.
    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, 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.
    Estimation of total myelin volume in the brain2011In: Internationell Society for Magnetic Resonance in Medicin, 2011, 2011, p. 2175-2175Conference paper (Refereed)
  • 187.
    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. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    West, Janne
    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.
    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.
    Helms, G.
    University Medical Center, Göttingen, Germany.
    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, 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.
    Using multi-parametric quantitative MRI to model myelin in the brain2011In: Internationell Society for Magnetic Resonance in Medicin, 2011, 2011, p. 536-536Conference paper (Refereed)
  • 188.
    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. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    West, Janne
    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.
    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.
    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.
    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.
    Fully Automatic Brain Tissue Segmentation on Multiple Sclerosis Patients with a High and a Low Number of White Matter Lesions2012Conference paper (Other academic)
  • 189.
    West, Janne
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Aalto, Anne
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center 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, Division of Radiological Sciences. 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.
    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.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    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.
    Normal Appearing and Diffusely Abnormal White Matter in Patients with Multiple Sclerosis, Assessed with Quantitative MR: Optimization for clinical usage2014In: PLOS ONE, E-ISSN 1932-6203, Vol. 9, no 4, p. e95161-Article in journal (Refereed)
    Abstract [en]

    Introduction: Magnetic Resonance Imaging is a sensitive technique for detecting white matter (WM) MS lesions, but the relation with clinical disability is low. Because of this, changes in both ‘normal appearing white matter’ (NAWM) and ‘diffusely abnormal white matter’ (DAWM) have been of interest in recent years. MR techniques, including quantitative magnetic resonance imaging (qMRI) and quantitative magnetic resonance spectroscopy (qMRS), have been developed in order to detect and  quantify such changes.

    In this study, a combination of qMRI and qMRS was used to investigate NAWM and DAWM in typical MS patients and in MS patients with low number of WM lesions. Patient data were compared to ‘normal white matter’ (NWM) in healthy controls.

    Methods: QMRI and qMRS measurements were performed on a 1.5T Philips MR-scanner. 35 patients with clinically definite MS and 20 healthy controls were included. Fifteen of the patients showed few WM lesions (‘MRIneg‘) and 20 showed radiologically typical findings (‘MRIpos’). QMRI properties were determined in ROIs of NAWM, DAWM and WM lesions in the MS groups and of NWM in controls. Descriptive statistical analysis and comparisons were performed. Correlations were calculated between qMRI measurements and (1) clinical parameters and (2) WM metabolite concentrations. Regression analyses were performed with brain parenchyma fraction and MSSS.

    Results: NAWM in the MRIneg group was significantly different from NAWM in the MRIpos group and NWM. In addition, R1 and R2 of NAWM in the MRIpos group correlated negatively with EDSS and MSSS. DAWM was significantly different from NWM, but similar in the two MS groups. N-acetyl aspartate correlated negatively with R1 and R2 in MRIneg. Finally, R2 of DAWM was associated with BPF.

    Conclusions: Changes in NAWM and DAWM are independent pathological entities in the disease. Combined qMRI and qMRS measurements of NAWM and DAWM provide important markers for disease status.

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  • 190.
    West, Janne
    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.
    Aalto, Anne
    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.
    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 Centre, Department of Clinical Physiology UHL.
    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.
    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.
    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.
    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.
    Characterizing Normal Appearing White and Diseased Matter in Multiple Sclerosis Using Quantitative MRI2012Conference paper (Other academic)
  • 191.
    West, Janne
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences.
    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 Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Collins, Rory
    Nuffield Department of Population Health, University of Oxford.
    Garratt, Steve
    UK Biobank, Spectrum Way, Adswood, Stockport, UK.
    Bell, Jimmy
    Research Centre for Optimal Health, University of Westminster, London, UK.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Advanced MR Analytics AB, Linköping, Sweden.
    Thomas, E. Louise
    Research Centre for Optimal Health, University of Westminster, London, UK.
    Feasibility of MR-based Body Composition Analysis in Large Scale Population Studies2016In: PLOS ONE, E-ISSN 1932-6203, Vol. 11, no 9, article id e0163332Article in journal (Refereed)
    Abstract [en]

    Introduction

    Quantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects.

    Materials and Methods

    3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics.

    Results

    Of the 3,000 subjects, 2,995 (99.83 %) were analysable for fat, 2,828 (94.27 %) were analysable when fat and one thigh was included, and 2,775 (92.50 %) were fully analysable for fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view.

    Discussion and Conclusions

    In conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource.

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  • 192.
    West, Janne
    et al.
    Linköping University, Department of Medical and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    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 Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Thomas, E. Louise
    Department of Life Sciences Faculty of Science and Technology, University of Westminster, London, UK.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bell, Jimmy
    Department of Life Sciences Faculty of Science and Technology, University of Westminster, London, UK.
    Body Composition Analysis In Large Scale Population Studies using Dixon Water-Fat Separated Imaging2016Conference paper (Other academic)
    Abstract [en]

    Water-fat separated MRI, based on Dixon imaging techniques enables high soft-tissue contrast and the separation of fat and muscle compartments. This study investigate the feasibility and success-rate of one recently described method for MR data-acquisition and body composition analysis, in a large-scale population study. The first 1,000 subjects in the UK Biobank imaging cohort were scanned, quality assured and included for body composition analysis. Volumes of visceral adipose tissue, abdominal subcutaneous tissue, and thigh muscles were calculated. This study showed that the rapid MR-examination was sufficiently robust to achieve very high success-rate for body composition analysis. 

  • 193.
    West, Janne
    et al.
    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 Medicine and Health Sciences.
    Linge, Jennifer
    Advanced MR Analytics AB, Linköping, Sweden.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Bell, Jimmy
    Westminster University, London, UK.
    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 Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Distribution Matters – Body Composition Profiling Associated with Prior Health Care Burden2017Conference paper (Refereed)
  • 194.
    West, Janne
    et al.
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Spetz, Anna-Clara
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Lindblom, Hanna
    Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences.
    Lindh Åstrand, Lotta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Hammar, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Dahlqvist Leinhard, Olof
    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 Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Automatic combined whole-body muscle and fat volume quantification using water-fat separated MRI in postmenopausal women2015In: International Society for Magnetic Resonance in Medicine Annual Meeting: Proceedings, 2015Conference paper (Other academic)
    Abstract [en]

    Quantitative and exact measurements of fat and muscle in the body are important when addressing some of the greatest health-challenges today. In this study whole-body combined regional muscle and fat volume quantification was validated in a group of postmenopausal women, where the body composition is changing. Twelve subjects were scanned with a 4-echo 3D gradient-echo sequence. Water and fat image volumes were calculated using IDEAL, and image intensity correction was performed. Subsequently, automatic tissue segmentation was established using non-rigid morphon based registration. Whole-body regional fat and muscle segmentation could be performed with excellent test-retest reliability, in a single 7-minutes MR-scan.

  • 195.
    West, Janne
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Adv MR Analyt AB, Linkoping, Sweden.
    Thorell, Sofia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lindblom, Hanna
    Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences.
    Berin, Emilia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences.
    Spetz Holm, Anna-Clara
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Lindh Åstrand, Lotta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Karlsson, Anette
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Adv MR Analyt AB, Linkoping, Sweden.
    Hammar, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Division of Children's and Women's health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Dahlqvist Leinhard, Olof
    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). Adv MR Analyt AB, Linkoping, Sweden.
    Precision of MRI-based body composition measurements of postmenopausal women2018In: PLOS ONE, E-ISSN 1932-6203, Vol. 13, no 2, article id e0192495Article in journal (Refereed)
    Abstract [en]

    Objectives To determine precision of magnetic resonance imaging (MRI) based fat and muscle quantification in a group of postmenopausal women. Furthermore, to extend the method to individual muscles relevant to upper-body exercise. Materials and methods This was a sub-study to a randomized control trial investigating effects of resistance training to decrease hot flushes in postmenopausal women. Thirty-six women were included, mean age 56 +/- 6 years. Each subject was scanned twice with a 3.0T MR-scanner using a whole-body Dixon protocol. Water and fat images were calculated using a 6-peak lipid model including R2*-correction. Body composition analyses were performed to measure visceral and subcutaneous fat volumes, lean volumes and muscle fat infiltration (MFI) of the muscle groups thigh muscles, lower leg muscles, and abdominal muscles, as well as the three individual muscles pectoralis, latissimus, and rhomboideus. Analysis was performed using a multi-atlas, calibrated water-fat separated quantification method. Liver-fat was measured as average proton density fat-fraction (PDFF) of three regions-of-interest. Precision was determined with Bland-Altman analysis, repeatability, and coefficient of variation. Results All of the 36 included women were successfully scanned and analysed. The coefficient of variation was 1.1% to 1.5% for abdominal fat compartments (visceral and subcutaneous), 0.8% to 1.9% for volumes of muscle groups (thigh, lower leg, and abdomen), and 2.3% to 7.0% for individual muscle volumes (pectoralis, latissimus, and rhomboideus). Limits of agreement for MFI was within +/- 2.06% for muscle groups and within +/- 5.13% for individual muscles. The limits of agreement for liver PDFF was within +/- 1.9%. Conclusion Whole-body Dixon MRI could characterize a range of different fat and muscle compartments with high precision, including individual muscles, in the study-group of postmenopausal women. The inclusion of individual muscles, calculated from the same scan, enables analysis for specific intervention programs and studies.

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  • 196.
    West, Janne
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Thorell, Sophia
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Lindblom, Hanna
    Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences.
    Berin, Emilia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Spetz Holm, Anna-Clara
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Lindh Åstrand, Lotta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Hammar, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    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 Medicine and Health Sciences.
    Body Composition Analysis Combined with Individual Muscle Measurements using Dixon-MRI2017Conference paper (Refereed)
    Abstract [en]

    Body composition analysis is increasingly important for diagnosis and follow-up in many patient groups and medical conditions. The combined fat and muscle quantification on global and regional level is not commonly reported. In this study a Dixon-MRI based acquisition and body composition analysis was extended to quantify individual muscles. Test-retest reliability was established in a clinically relevant group of 36 postmenopausal women. This method enables advanced phenotyping combined with measurements of specific muscles to target clinical questions. 

  • 197.
    West, Janne
    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.
    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.
    Absolute Quantification of T1, T2, PD and B1 on Patients with Multiple Sclerosis, Covering the Brain in 5 Minutes2008Conference paper (Other academic)
  • 198.
    Widholm, Per
    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, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Ahlgren, Andre
    AMRA Med AB, Linkoping, Sweden.
    Karlsson, Markus
    AMRA Med AB, Linkoping, Sweden.
    Romu, Thobias
    AMRA Med AB, Linkoping, Sweden.
    Tawil, Rabi
    Univ Rochester, NY 14642 USA.
    Wagner, Kathryn R.
    Johns Hopkins Sch Med, MD USA.
    Statland, Jeffrey M.
    Univ Kansas, KS 66103 USA.
    Wang, Leo H.
    Univ Washington, WA USA.
    Shieh, Perry B.
    Univ Calif Los Angeles, CA USA.
    van Engelen, Baziel G. M.
    Radboud Univ Nijmegen, Netherlands.
    Cadavid, Diego
    Fulcrum Therapeut, MA 02139 USA.
    Ronco, Lucienne
    Fulcrum Therapeut, MA 02139 USA.
    Odueyungbo, Adefowope O.
    Fulcrum Therapeut, MA 02139 USA.
    Jiang, John G.
    Fulcrum Therapeut, MA 02139 USA.
    Mellion, Michelle L.
    Fulcrum Therapeut, MA 02139 USA.
    Dahlqvist Leinhard, Olof
    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). AMRA Med AB, Linkoping, Sweden.
    Quantitative muscle analysis in facioscapulohumeral muscular dystrophy using whole-body fat-referenced MRI: Protocol development, multicenter feasibility, and repeatability2022In: Muscle and Nerve, ISSN 0148-639X, E-ISSN 1097-4598, Vol. 66, no 2, p. 183-192Article in journal (Refereed)
    Abstract [en]

    Introduction/Aims Functional performance tests are the gold standard to assess disease progression and treatment effects in neuromuscular disorders. These tests can be confounded by motivation, pain, fatigue, and learning effects, increasing variability and decreasing sensitivity to disease progression, limiting efficacy assessment in clinical trials with small sample sizes. We aimed to develop and validate a quantitative and objective method to measure skeletal muscle volume and fat content based on whole-body fat-referenced magnetic resonance imaging (MRI) for use in multisite clinical trials. Methods Subjects aged 18 to 65 years, genetically confirmed facioscapulohumeral muscular dystrophy 1 (FSHD1), clinical severity 2 to 4 (Riccis scale, range 0-5), were enrolled at six sites and imaged twice 4-12 weeks apart with T1-weighted two-point Dixon MRI covering the torso and upper and lower extremities. Thirty-six muscles were volumetrically segmented using semi-automatic multi-atlas-based segmentation. Muscle fat fraction (MFF), muscle fat infiltration (MFI), and lean muscle volume (LMV) were quantified for each muscle using fat-referenced quantification. Results Seventeen patients (mean age +/- SD, 49.4 years +/- 13.02; 12 men) were enrolled. Within-patient SD ranged from 1.00% to 3.51% for MFF and 0.40% to 1.48% for MFI in individual muscles. For LMV, coefficients of variation ranged from 2.7% to 11.7%. For the composite score average of all muscles, observed SDs were 0.70% and 0.32% for MFF and MFI, respectively; composite LMV coefficient of variation was 2.0%. Discussion We developed and validated a method for measuring skeletal muscle volume and fat content for use in multisite clinical trials of neuromuscular disorders.

  • 199.
    Wiik, Anna
    et al.
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Lundberg, Tommy R.
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Rullman, Eric
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Andersson, Daniel P.
    Karolinska Inst, Sweden.
    Holmberg, Mats
    Karolinska Univ Hosp, Sweden.
    Mandic, Mirko
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Brismar, Torkel B.
    Karolinska Inst, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Chanpen, Setareh
    Karolinska Univ Hosp, Sweden.
    Flanagan, John N.
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Arver, Stefan
    Karolinska Univ Hosp, Sweden.
    Gustafsson, Thomas
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Muscle Strength, Size, and Composition Following 12 Months of Gender-affirming Treatment in Transgender Individuals2020In: Journal of Clinical Endocrinology and Metabolism, ISSN 0021-972X, E-ISSN 1945-7197, Vol. 105, no 3, p. E805-E813Article in journal (Refereed)
    Abstract [en]

    Context. As many sports are divided in male/female categories, governing bodies have formed regulations on the eligibility for transgender individuals to compete in these categories. Yet, the magnitude of change in muscle mass and strength with gender-affirming treatment remains insufficiently explored. Objective. This study explored the effects of gender-affirming treatment on muscle function, size, and composition during 12 months of therapy. Design, settings, participants. In this single-center observational cohort study, untrained transgender women (TW, n = 11) and transgender men (TM, n = 12), approved to start gender-affirming medical interventions, underwent assessments at baseline, 4 weeks after gonadal suppression of endogenous hormones but before hormone replacement, and 4 and 12 months after treatment initiation. Main outcome measures. Knee extensor and flexor strength were assessed at all examination time points, and muscle size and radiological density (using magnetic resonance imaging and computed tomography) at baseline and 12 months after treatment initiation. Results. Thigh muscle volume increased (15%) in TM, which was paralleled by increased quadriceps cross-sectional area (CSA) (15%) and radiological density (6%). In TW, the corresponding parameters decreased by -5% (muscle volume) and -4% (CSA), while density remained unaltered. The TM increased strength over the assessment period, while the TW generally maintained their strength levels. Conclusions. One year of gender-affirming treatment resulted in robust increases in muscle mass and strength in TM, but modest changes in TW. These findings add new knowledge on the magnitude of changes in muscle function, size, and composition with cross-hormone therapy, which could be relevant when evaluating the transgender eligibility rules for athletic competitions.

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