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  • 1. Order onlineBuy this publication >>
    Abramian, David
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Modern multimodal methods in brain MRI2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Magnetic resonance imaging (MRI) is one of the pillars of modern medical imaging, providing a non-invasive means to generate 3D images of the body with high soft-tissue contrast. Furthermore, the possibilities afforded by the design of MRI sequences enable the signal to be sensitized to a multitude of physiological tissue properties, resulting in a wide variety of distinct MRI modalities for clinical and research use. 

    This thesis presents a number of advanced brain MRI applications, which fulfill, to differing extents, two complementary aims. On the one hand, they explore the benefits of a multimodal approach to MRI, combining structural, functional and diffusion MRI, in a variety of contexts. On the other, they emphasize the use of advanced mathematical and computational tools in the analysis of MRI data, such as deep learning, Bayesian statistics, and graph signal processing. 

    Paper I introduces an anatomically-adapted extension to previous work in Bayesian spatial priors for functional MRI data, where anatomical information is introduced from a T1-weighted image to compensate for the low anatomical contrast of functional MRI data. 

    It has been observed that the spatial correlation structure of the BOLD signal in brain white matter follows the orientation of the underlying axonal fibers. Paper II argues about the implications of this fact on the ideal shape of spatial filters for the analysis of white matter functional MRI data. By using axonal orientation information extracted from diffusion MRI, and leveraging the possibilities afforded by graph signal processing, a graph-based description of the white matter structure is introduced, which, in turn, enables the definition of spatial filters whose shape is adapted to the underlying axonal structure, and demonstrates the increased detection power resulting from their use. 

    One of the main clinical applications of functional MRI is functional localization of the eloquent areas of the brain prior to brain surgery. This practice is widespread for various invasive surgeries, but is less common for stereotactic radiosurgery (SRS), a non-invasive surgical procedure wherein tissue is ablated by concentrating several beams of high-energy radiation. Paper III describes an analysis and processing pipeline for functional MRI data that enables its use for functional localization and delineation of organs-at-risk for Elekta GammaKnife SRS procedures. 

    Paper IV presents a deep learning model for super-resolution of diffusion MRI fiber ODFs, which outperforms standard interpolation methods in estimating local axonal fiber orientations in white matter. Finally, Paper V demonstrates that some popular methods for anonymizing facial data in structural MRI volumes can be partially reversed by applying generative deep learning models, highlighting one way in which the enormous power of deep learning models can potentially be put to use for harmful purposes. 

    List of papers
    1. Anatomically Informed Bayesian Spatial Priors for FMRI Analysis
    Open this publication in new window or tab >>Anatomically Informed Bayesian Spatial Priors for FMRI Analysis
    Show others...
    2020 (English)In: ISBI 2020: IEEE International Symposium on Biomedical Imaging / [ed] IEEE, IEEE, 2020Conference paper, Published paper (Refereed)
    Abstract [en]

    Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure.

    Place, publisher, year, edition, pages
    IEEE, 2020
    Series
    IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928, E-ISSN 1945-8452
    Keywords
    Bayesian statistics, functional MRI, activation mapping, adaptive smoothing
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-165856 (URN)10.1109/ISBI45749.2020.9098342 (DOI)000578080300208 ()978-1-5386-9330-8 (ISBN)
    Conference
    IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 3-7 April 2020
    Funder
    Swedish Research Council, 2017- 04889
    Note

    Funding agencies:  Swedish Research CouncilSwedish Research Council [201704889]; Center for Industrial Information Technology (CENIIT) at Linkoping University

    Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2023-03-31Bibliographically approved
    2. Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters
    Open this publication in new window or tab >>Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters
    Show others...
    2021 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 237, article id 118095Article in journal (Refereed) Published
    Abstract [en]

    Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject’s unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project’s 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

    Place, publisher, year, edition, pages
    Elsevier, 2021
    Keywords
    functional MRI, diffusion MRI, white matter, graph signal processing, anisotropy
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-175762 (URN)10.1016/j.neuroimage.2021.118095 (DOI)000671134200006 ()34000402 (PubMedID)
    Funder
    Swedish Research Council, 2018-06689Swedish Research Council, 2017- 04889Vinnova, 2018-02230NIH (National Institute of Health), K01DK101631NIH (National Institute of Health), R56AG068261
    Note

    Funding: McDonnell Center for Systems Neuroscience at Washington University; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2017-04889, 2018-06689]; Royal Physiographic Society of Lund; Thorsten and Elsa Segerfalk Foundation; Hans Werthen Foundation; ITEA3/VINNOVA; Center for Industrial Information Technology (CENIIT) at Linkoping University; BrightFocus FoundationBrightFocus Foundation [A2016172S]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA; National Institute of Diabetes and Digestive and Kidney DiseasesUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK) [K01DK101631]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [R56AG068261];  [1U54MH091657]

    Available from: 2021-05-19 Created: 2021-05-19 Last updated: 2023-03-31
    3. REFACING: RECONSTRUCTING ANONYMIZED FACIAL FEATURES USING GANS
    Open this publication in new window or tab >>REFACING: RECONSTRUCTING ANONYMIZED FACIAL FEATURES USING GANS
    2019 (English)In: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019), IEEE , 2019, p. 1104-1108Conference paper, Published paper (Refereed)
    Abstract [en]

    Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in deep learning may raise the bar on the amount of distortion that needs to be applied to guarantee anonymity. To test such possibilities, we have applied the novel CycleGAN unsupervised image-to-image translation framework on sagittal slices of T1 MR images, in order to reconstruct, facial features from anonymized data. We applied the CycleGAN framework on both face-blurred and face-removed images. Our results show that face blurring may not provide adequate protection against malicious attempts at identifying the subjects, while face removal provides more robust anonymization, but is still partially reversible.

    Place, publisher, year, edition, pages
    IEEE, 2019
    Series
    IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928, E-ISSN 1945-8452
    Keywords
    MRI; anonymization; GANs; image-to-image translation
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-160633 (URN)10.1109/ISBI.2019.8759515 (DOI)000485040000234 ()978-1-5386-3641-1 (ISBN)
    Conference
    16th IEEE International Symposium on Biomedical Imaging (ISBI)
    Note

    Funding Agencies|Swedish research councilSwedish Research Council [201704889]; Center for Industrial Information Technology (CENIIT) at Linkoping University; Knut and Alice Wallenberg foundationKnut & Alice Wallenberg Foundation

    Available from: 2019-10-10 Created: 2019-10-10 Last updated: 2023-03-31
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  • 2.
    Abramian, David
    et al.
    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).
    Larsson, Martin
    Centre of Mathematical Sciences, Lund University, Lund, Sweden.
    Eklund, Anders
    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). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Aganj, Iman
    Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA.
    Westin, Carl-Fredrik
    Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA.
    Behjat, Hamid
    Department of Biomedical Engineering, Lund University, Lund, Sweden; Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
    Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters2021In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 237, article id 118095Article in journal (Refereed)
    Abstract [en]

    Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject’s unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project’s 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

    Download full text (pdf)
    fulltext
  • 3.
    Abramian, David
    et al.
    Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Larsson, Martin
    Centre for Mathematical Sciences, Lund University, Sweden.
    Eklund, Anders
    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). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Behjat, Hamid
    Department of Biomedical Engineering, Lund University, Sweden.
    Improved Functional MRI Activation Mapping in White Matter Through Diffusion-Adapted Spatial Filtering2020In: ISBI 2020: IEEE International Symposium on Biomedical Imaging, IEEE, 2020Conference paper (Refereed)
    Abstract [en]

    Brain activation mapping using functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) contrast has been conventionally focused on probing gray matter, the BOLD contrast in white matter having been generally disregarded. Recent results have provided evidence of the functional significance of the white matter BOLD signal, showing at the same time that its correlation structure is highly anisotropic, and related to the diffusion tensor in shape and orientation. This evidence suggests that conventional isotropic Gaussian filters are inadequate for denoising white matter fMRI data, since they are incapable of adapting to the complex anisotropic domain of white matter axonal connections. In this paper we explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of encoding the anisotropy of the domain. Based on this representation we design localized spatial filters that adapt to white matter structure by leveraging graph signal processing principles. The performance of the proposed filtering technique is evaluated on semi-synthetic data, where it shows potential for greater sensitivity and specificity in white matter activation mapping, compared to isotropic filtering.

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  • 4.
    Abramian, David
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Sidén, Per
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Knutsson, Hans
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Department of Statistics, Stockholm University.
    Eklund, Anders
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Anatomically Informed Bayesian Spatial Priors for FMRI Analysis2020In: ISBI 2020: IEEE International Symposium on Biomedical Imaging / [ed] IEEE, IEEE, 2020Conference paper (Refereed)
    Abstract [en]

    Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure.

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  • 5.
    ADOK, ILDI
    Linköping University, Department of Biomedical Engineering.
    Development of a tool for analysis and visualization of longitudinal magnetic resonance flowmeasurements: of subarachnoid hemorrhage patients in the neurointensivecare unit2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Patients who are treated in an intensive care unit need continuous monitoring in orderfor clinicians to be prepared to intervene should a secondary event occur. For patientstreated at the neurointensive care unit (NICU) who have suffered a subarachnoid hemorrhage (SAH) this secondary event could be ischemia, resulting in a lack of blood flow.Blood flow can be measured using magnetic resonance imaging (MRI). The process is facilitated with a software called NOVA. Repeated measurements can therefore be performedas a way to monitor the patients, which in this context would be referred to as longitudinalmeasurements. As more data can be collected ways of analyzing and visualizing the datain a comprehensible way is needed. The aim of this thesis was therefore to develop and implement a method for analyzing and visualizing the longitudinal MR measurement data.With this aim in mind two research questions were relevant. The first one was how NOVAflow longitudinal measurements can be visualized to simplify interpretation by cliniciansand the second one was in what ways the longitudinal data can be analyzed. A graphicaluser interface (GUI) was created to present the developed analysis and visualization tool.Development of the tool progressed using feedback from supervisors and neurosurgeons.Visualization and analysis was done through plots of blood velocity and blood flow as themain component as well as a 2D vessel map. The final implementation showed multipleexamples of how the longitudinal data could be both visualized and analyzed. The resultstherefore provided a tool to analyze and visualize NOVA flow longitudinal measurementsin a way which was easily interpreted. Further improvements of the tool is possible andan area of improvement could involve increasing the adaptability of the tool.

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  • 6.
    Afzali, Maryam
    et al.
    Cardiff Univ, Wales; Univ Leeds, England.
    Knutsson, Hans
    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).
    Özarslan, Evren
    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).
    Jones, Derek K.
    Cardiff Univ, Wales.
    Computing the orientational-average of diffusion-weighted MRI signals: a comparison of different techniques2021In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 14345Article in journal (Refereed)
    Abstract [en]

    Numerous applications in diffusion MRI involve computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or shell), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres. To ameliorate this challenge, alternative averaging methods include: weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its isotropic part. Here, these different methods are simulated and compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61 orientations per shell), and isotropically-distributed sampling vectors, all averaging methods give comparable results, (MAP-MRI-based estimates give slightly higher accuracy, albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give significantly higher accuracy compared with the other methods. We also apply these approaches to in vivo data where the results are broadly consistent with our simulations. A statistical analysis of the simulated data shows that the orientationally-averaged signals at each b-value are largely Gaussian distributed.

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  • 7.
    Agmell, Simon
    et al.
    Linköping University, Department of Science and Technology, Physics and Electronics. Linköping University, Faculty of Science & Engineering.
    Dekker, Marcus
    Linköping University, Department of Science and Technology, Physics and Electronics. Linköping University, Faculty of Science & Engineering.
    IR-Based Indoor Localisation and Positioning System2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis presents a prototype beacon-based indoor positioning system using IR-based triangulation together with various inertial sensors mounted onto the receiver. By applying a Kalman filter, the mobile receivers can estimate their position by fusing the data received from the two independent measurement systems. Furthermore, the system is aimed to operate and conduct all calculations using microcontrollers. Multiple IR beacons and an AGV were constructed to determine the systems performance.

    Empirical and practical experiments show that the proposed localisation system is capable centimeter accuracy. However, because of hardware limitation the system has lacking update frequency and range. With the limitations in mind, it can be established that the final sensor-fused solution shows great promise but requires an extended component assessment and more advanced localisation estimations method such as an Extended Kalman Filter or particle filter to increase reliability.

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    IR-Based Indoor Localisation and Positioning System
  • 8. Order onlineBuy this publication >>
    Ahlander, Britt-Marie
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Magnetic Resonance Imaging of the Heart: Image quality, measurement accuracy and patient experience2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Background: Non-invasive diagnostic imaging of atherosclerotic coronary artery disease (CAD) is frequently carried out with cardiovascular magnetic resonance imaging (CMR) or myocardial perfusion single photon emission computed tomography (MPS). CMR is the gold standard for the evaluation of scar after myocardial infarction and MPS the clinical gold standard for ischemia. Magnetic Resonance Imaging (MRI) is at times difficult for patients and may induce anxiety while patient experience of MPS is largely unknown.

    Aims: To evaluate image quality in CMR with respect to the sequences employed, the influence of atrial fibrillation, myocardial perfusion and the impact of patient information. Further, to study patient experience in relation to MRI with the goal of improving the care of these patients.

    Method: Four study designs have been used. In paper I, experimental cross-over, paper (II) experimental controlled clinical trial, paper (III) psychometric crosssectional study and paper (IV) prospective intervention study. A total of 475 patients ≥ 18 years with primarily cardiac problems (I-IV) except for those referred for MRI of the spine (III) were included in the four studies.

    Result: In patients (n=20) with atrial fibrillation, a single shot steady state free precession (SS-SSFP) sequence showed significantly better image quality than the standard segmented inversion recovery fast gradient echo (IR-FGRE) sequence (I). In first-pass perfusion imaging the gradient echo-echo planar imaging sequence (GREEPI) (n=30) had lower signal-to-noise and contrast–to-noise ratios than the steady state free precession sequence (SSFP) (n=30) but displayed a higher correlation with the MPS results, evaluated both qualitatively and quantitatively (II). The MRIAnxiety Questionnaire (MRI-AQ) was validated on patients, referred for MRI of either the spine (n=193) or the heart (n=54). The final instrument had 15 items divided in two factors regarding Anxiety and Relaxation. The instrument was found to have satisfactory psychometric properties (III). Patients who prior CMR viewed an information video scored significantly (lower) better in the factor Relaxation, than those who received standard information. Patients who underwent MPS scored lower on both factors, Anxiety and Relaxation. The extra video information had no effect on CMR image quality (IV).

    Conclusion: Single shot imaging in atrial fibrillation produced images with less artefact than a segmented sequence. In first-pass perfusion imaging, the sequence GRE-EPI was superior to SSFP. A questionnaire depicting anxiety during MRI showed that video information prior to imaging helped patients relax but did not result in an improvement in image quality.

    List of papers
    1. Image quality and myocardial scar size determined with magnetic resonance imaging in patients with permanent atrial fibrillation: a comparison of two imaging protocols
    Open this publication in new window or tab >>Image quality and myocardial scar size determined with magnetic resonance imaging in patients with permanent atrial fibrillation: a comparison of two imaging protocols
    Show others...
    2010 (English)In: CLINICAL PHYSIOLOGY AND FUNCTIONAL IMAGING, ISSN 1475-0961, Vol. 30, no 2, p. 122-129Article in journal (Refereed) Published
    Abstract [en]

    Pandgt;Background: Magnetic resonance imaging (MRI) of the heart generally requires breath holding and a regular rhythm. Single shot 2D steady-state free precession (SS_SSFP) is a fast sequence insensitive to arrhythmia as well as breath holding. Our purpose was to determine image quality, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios and infarct size with a fast single shot and a standard segmented MRI sequence in patients with permanent atrial fibrillation and chronic myocardial infarction. Methods: Twenty patients with chronic myocardial infarction and ongoing atrial fibrillation were examined with inversion recovery SS_SSFP and segmented inversion recovery 2D fast gradient echo (IR_FGRE). Image quality was assessed in four categories: delineation of infarcted and non-infarcted myocardium, occurrence of artefacts and overall image quality. SNR and CNR were calculated. Myocardial volume (ml) and infarct size, expressed as volume (ml) and extent (%), were calculated, and the methodological error was assessed. Results: SS_SSFP had significantly better quality scores in all categories (P = 0 center dot 037, P = 0 center dot 014, P = 0 center dot 021, P = 0 center dot 03). SNRinfarct and SNRblood were significantly better for IR_FGRE than for SS_SSFP (P = 0 center dot 048, P = 0 center dot 018). No significant difference was found in SNRmyocardium and CNR. The myocardial volume was significantly larger with SS_SSFP (170 center dot 7 versus 159 center dot 2 ml, P andlt; 0 center dot 001), but no significant difference was found in infarct volume and infarct extent. Conclusion: SS_SSFP displayed significantly better image quality than IR_FGRE. The infarct size and the error in its determination were equal for both sequences, and the examination time was shorter with SS_SSFP.

    Keywords
    atrial fibrillation, magnetic resonance imaging, myocardial infarction, segmented inversion recovery 2D fast gradient echo, single shot inversion recovery 2D steady-state free precession
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-54159 (URN)10.1111/j.1475-097X.2009.00914.x (DOI)000274438800006 ()
    Available from: 2010-02-26 Created: 2010-02-26 Last updated: 2021-12-28
    2. An echo-planar imaging sequence is superior to a steady-state free precession sequence for visual as well as quantitative assessment of cardiac magnetic resonance stress perfusion
    Open this publication in new window or tab >>An echo-planar imaging sequence is superior to a steady-state free precession sequence for visual as well as quantitative assessment of cardiac magnetic resonance stress perfusion
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    2017 (English)In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 37, no 1, p. 52-61Article in journal (Refereed) Published
    Abstract [en]

    Background To assess myocardial perfusion, steady-state free precession cardiac magnetic resonance (SSFP, CMR) was compared with gradient-echo–echo-planar imaging (GRE-EPI) using myocardial perfusion scintigraphy (MPS) as reference. Methods Cardiac magnetic resonance perfusion was recorded in 30 patients with SSFP and in another 30 patients with GRE-EPI. Timing and extent of inflow delay to the myocardium was visually assessed. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated. Myocardial scar was visualized with a phase-sensitive inversion recovery sequence (PSIR). All scar positive segments were considered pathologic. In MPS, stress and rest images were used as in clinical reporting. The CMR contrast wash-in slope was calculated and compared with the stress score from the MPS examination. CMR scar, CMR perfusion and MPS were assessed separately by one expert for each method who was blinded to other aspects of the study. Results Visual assessment of CMR had a sensitivity for the detection of an abnormal MPS at 78% (SSFP) versus 91% (GRE-EPI) and a specificity of 58% (SSFP) versus 84% (GRE-EPI). Kappa statistics for SSFP and MPS was 0·29, for GRE-EPI and MPS 0·72. The ANOVA of CMR perfusion slopes for all segments versus MPS score (four levels based on MPS) had correlation r = 0·64 (SSFP) and r = 0·96 (GRE-EPI). SNR was for normal segments 35·63 ± 11·80 (SSFP) and 17·98 ± 8·31 (GRE-EPI), while CNR was 28·79 ± 10·43 (SSFP) and 13·06 ± 7·61 (GRE-EPI). Conclusion GRE-EPI displayed higher agreement with the MPS results than SSFP despite significantly lower signal intensity, SNR and CNR.

    Place, publisher, year, edition, pages
    John Wiley & Sons, 2017
    Keywords
    cardiac imaging techniques, coronary heart disease, Magnetic Resonance Imaging, nuclear medicine, perfusion
    National Category
    Radiology, Nuclear Medicine and Medical Imaging Medical Laboratory and Measurements Technologies Medical Image Processing Clinical Medicine
    Identifiers
    urn:nbn:se:liu:diva-130795 (URN)10.1111/cpf.12267 (DOI)000390688200008 ()26147785 (PubMedID)
    Note

    Funding agencies: Medical Research Council of Southeast Sweden [12437]; Futurum, the County council of Jonkoping [12440, 81851, 217261]; Linkoping University; County Council of Ostergotland [281281]; Swedish Heart-Lung Foundation [20120449]

    Available from: 2016-08-24 Created: 2016-08-24 Last updated: 2021-12-28Bibliographically approved
    3. Development and validation of a questionnaire evaluating patient anxiety during Magnetic Resonance Imaging: the Magnetic Resonance Imaging-Anxiety Questionnaire (MRI-AQ)
    Open this publication in new window or tab >>Development and validation of a questionnaire evaluating patient anxiety during Magnetic Resonance Imaging: the Magnetic Resonance Imaging-Anxiety Questionnaire (MRI-AQ)
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    2016 (English)In: Journal of Advanced Nursing, ISSN 0309-2402, E-ISSN 1365-2648, Vol. 72, no 6, p. 1368-1380Article in journal (Refereed) Published
    Abstract [en]

    Aim. To develop and validate a new instrument measuring patient anxiety during Magnetic Resonance Imaging examinations, Magnetic Resonance Imaging-Anxiety Questionnaire. Background. Questionnaires measuring patients anxiety during Magnetic Resonance Imaging examinations have been the same as used in a wide range of conditions. To learn about patients experience during examination and to evaluate interventions, a specific questionnaire measuring patient anxiety during Magnetic Resonance Imaging is needed. Design. Psychometric cross-sectional study with test-retest design. Methods. A new questionnaire, Magnetic Resonance Imaging-Anxiety Questionnaire, was designed from patient expressions of anxiety in Magnetic Resonance Imaging-scanners. The sample was recruited between October 2012-October 2014. Factor structure was evaluated with exploratory factor analysis and internal consistency with Cronbachs alpha. Criterion-related validity, known-group validity and test-retest was calculated. Results. Patients referred for Magnetic Resonance Imaging of either the spine or the heart, were invited to participate. The development and validation of Magnetic Resonance Imaging-Anxiety Questionnaire resulted in 15 items consisting of two factors. Cronbachs alpha was found to be high. Magnetic Resonance Imaging-Anxiety Questionnaire correlated higher with instruments measuring anxiety than with depression scales. Known-group validity demonstrated a higher level of anxiety for patients undergoing Magnetic Resonance Imaging scan of the heart than for those examining the spine. Test-retest reliability demonstrated acceptable level for the scale. Conclusion. Magnetic Resonance Imaging-Anxiety Questionnaire bridges a gap among existing questionnaires, making it a simple and useful tool for measuring patient anxiety during Magnetic Resonance Imaging examinations.

    Place, publisher, year, edition, pages
    WILEY-BLACKWELL, 2016
    Keywords
    anxiety; instrument development; magnetic resonance imaging; nurse; nursing; reliability; validity
    National Category
    Other Medical Sciences not elsewhere specified
    Identifiers
    urn:nbn:se:liu:diva-129145 (URN)10.1111/jan.12917 (DOI)000376007400014 ()26893007 (PubMedID)
    Note

    Funding Agencies|Swedish Heart and Lung Foundation; Futurum County Council of Jonkoping

    Available from: 2016-06-13 Created: 2016-06-13 Last updated: 2021-12-28
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    Magnetic Resonance Imaging of the Heart: Image quality, measurement accuracy and patient experience
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  • 9.
    Ahlander, Britt-Marie
    et al.
    Department of Radiology, Ryhov County Hospital, Jönköping.
    Maret, Eva
    Department of Radiology, Ryhov County Hospital, Jönköping / Department of Clinical Physiology, Karolinska University Hospital, Stockholm.
    Brudin, Lars
    Department of Clinical Physiology, Kalmar County Hospital, Kalmar.
    Starck, Sven-Åke
    Department of Natural Science and Biomedicine, School of Health Sciences, Jönköping University / Department of Oncology, Hospital Physics, Ryhov County Hospital, Jönköping.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    An echo-planar imaging sequence is superior to a steady-state free precession sequence for visual as well as quantitative assessment of cardiac magnetic resonance stress perfusion2017In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 37, no 1, p. 52-61Article in journal (Refereed)
    Abstract [en]

    Background To assess myocardial perfusion, steady-state free precession cardiac magnetic resonance (SSFP, CMR) was compared with gradient-echo–echo-planar imaging (GRE-EPI) using myocardial perfusion scintigraphy (MPS) as reference. Methods Cardiac magnetic resonance perfusion was recorded in 30 patients with SSFP and in another 30 patients with GRE-EPI. Timing and extent of inflow delay to the myocardium was visually assessed. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated. Myocardial scar was visualized with a phase-sensitive inversion recovery sequence (PSIR). All scar positive segments were considered pathologic. In MPS, stress and rest images were used as in clinical reporting. The CMR contrast wash-in slope was calculated and compared with the stress score from the MPS examination. CMR scar, CMR perfusion and MPS were assessed separately by one expert for each method who was blinded to other aspects of the study. Results Visual assessment of CMR had a sensitivity for the detection of an abnormal MPS at 78% (SSFP) versus 91% (GRE-EPI) and a specificity of 58% (SSFP) versus 84% (GRE-EPI). Kappa statistics for SSFP and MPS was 0·29, for GRE-EPI and MPS 0·72. The ANOVA of CMR perfusion slopes for all segments versus MPS score (four levels based on MPS) had correlation r = 0·64 (SSFP) and r = 0·96 (GRE-EPI). SNR was for normal segments 35·63 ± 11·80 (SSFP) and 17·98 ± 8·31 (GRE-EPI), while CNR was 28·79 ± 10·43 (SSFP) and 13·06 ± 7·61 (GRE-EPI). Conclusion GRE-EPI displayed higher agreement with the MPS results than SSFP despite significantly lower signal intensity, SNR and CNR.

  • 10.
    Alickovic, Emina
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Subasi, Abdulhamit
    Effat Univ, Saudi Arabia.
    Automatic Detection of Alzheimer Disease Based on Histogram and Random Forest2020In: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING, CMBEBIH 2019, SPRINGER , 2020, Vol. 73, p. 91-96Conference paper (Refereed)
    Abstract [en]

    Alzheimer disease is one of the most prevalent dementia types affecting elder population. On-time detection of the Alzheimer disease (AD) is valuable for finding new approaches for the AD treatment. Our primary interest lies in obtaining a reliable, but simple and fast model for automatic AD detection. The approach we introduced in the present contribution to identify AD is based on the application of machine learning (ML) techniques. For the first step, we use histogram to transform brain images to feature vectors, containing the relevant "brain" features, which will later serve as the inputs in the classification step. Next, we use the ML algorithms in the classification task to identify AD. The model presented and elaborated in the present contribution demonstrated satisfactory performances. Experimental results suggested that the Random Forest classifier can discriminate the AD subjects from the control subjects. The presented modeling approach, consisting of the histogram as the feature extractor and Random Forest as the classifier, yielded to the sufficiently high overall accuracy rate of 85.77%.

  • 11.
    Almquist, Camilla
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine.
    Implementation of an automated,personalized model of the cardiovascularsystem using 4D Flow MRI2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A personalized cardiovascular lumped parameter model of the left-sided heart and thesystemic circulation has been developed by the cardiovascular medicine research groupat Linköping University. It provides information about hemodynamics, some of whichcould otherwise only have been retrieved by invasive measurements. The framework forpersonalizing the model is made using 4D Flow MRI data, containing volumes describinganatomy and velocities in three directions. Thus far, the inputs to this model have beengenerated manually for each subject. This is a slow and tedious process, unpractical touse clinically, and unfeasible for many subjects.This project aims to develop a tool to calculate the inputs and run the model for mul-tiple subjects in an automatic way. It has its basis in 4D Flow MRI data sets segmentedto identify the locations of left atrium (LA), left ventricle (LV), and aorta, along with thecorresponding structures on the right side.The process of making this tool started by calculation of the inputs. Planes were placedin the relevant positions, at the mitral valve, aortic valve (AV) and in the ascending aortaupstream the brachiocephalic branches, and flow rates were calculated through them. TheAV plane was used to calculate effective orifice area of AV and aortic cross-sectional area,while the LV end systolic and end diastolic volumes were extracted form the segmentation.The tool was evaluated by comparison with manually created inputs and outputs,using 9 healthy volunteers and one patient deemed to have normal left ventricular func-tion. The patient was chosen from a subject group diagnosed with chronic ischemic heartdisease, and/or a history of angina, together with fulfillment of the high risk score ofcardiovascular diseases of the European Society of Cardiology. This data was evaluatedusing coefficient of variation, Bland-Altman plots and sum squared error. The tool wasalso evaluated visually on some subjects with pathologies of interest.This project shows that it is possible to calculate inputs fully automatically fromsegmented 4D Flow MRI and run the cardiovascular avatar in an automatic way, withoutuser interaction. The method developed seems to be in good to moderate agreement withthose obtained manually, and could be the basis for further development of the model.

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  • 12.
    Amgad, Mohamed
    et al.
    Emory Univ, GA USA.
    Stovgaard, Elisabeth Specht
    Univ Copenhagen, Denmark.
    Balslev, Eva
    Univ Copenhagen, Denmark.
    Thagaard, Jeppe
    Tech Univ Denmark, Denmark; Visiopharm AS, Denmark.
    Chen, Weijie
    FDA CDRH OSEL, MD USA.
    Dudgeon, Sarah
    FDA CDRH OSEL, MD USA.
    Sharma, Ashish
    Emory Univ, GA USA.
    Kerner, Jennifer K.
    PathAI, MA USA.
    Denkert, Carsten
    Philipps Univ Marburg, Germany; Philipps Univ Marburg, Germany; German Canc Consortium DKTK, Germany.
    Yuan, Yinyin
    Inst Canc Res, England.
    AbdulJabbar, Khalid
    Inst Canc Res, England.
    Wienert, Stephan
    Philipps Univ Marburg, Germany.
    Savas, Peter
    Univ Melbourne, Australia.
    Voorwerk, Leonie
    Netherlands Canc Inst, Netherlands.
    Beck, Andrew H.
    PathAI, MA USA.
    Madabhushi, Anant
    Case Western Reserve Univ, OH 44106 USA; Louis Stokes Cleveland Vet Adm Med Ctr, OH USA.
    Hartman, Johan
    Karolinska Inst, Sweden; Univ Hosp, Sweden.
    Sebastian, Manu M.
    Univ Texas MD Anderson Canc Ctr, TX 77030 USA.
    Horlings, Hugo M.
    Netherlands Canc Inst, Netherlands.
    Hudecek, Jan
    Netherlands Canc Inst, Netherlands.
    Ciompi, Francesco
    Radboud Univ Nijmegen, Netherlands.
    Moore, David A.
    UCL Canc Inst, England; Icahn Sch Med Mt Sinai, NY 10029 USA.
    Singh, Rajendra
    Icahn Sch Med Mt Sinai, NY 10029 USA.
    Roblin, Elvire
    Univ Paris Sud, France.
    Balancin, Marcelo Luiz
    Univ Sao Paulo, Brazil.
    Mathieu, Marie-Christine
    Gustave Roussy Canc Campus, France.
    Lennerz, Jochen K.
    Massachusetts Gen Hosp, MA 02114 USA.
    Kirtani, Pawan
    Manipal Hosp Dwarka, India.
    Chen, I-Chun
    Natl Taiwan Univ, Taiwan.
    Braybrooke, Jeremy P.
    Univ Oxford, England; Univ Hosp Bristol NHS Fdn Trust, England.
    Pruneri, Giancarlo
    Ist Nazl Tumori, Italy; Univ Milan, Italy.
    Demaria, Sandra
    Weill Cornell Med Coll, NY USA.
    Adams, Sylvia
    NYU Langone Med Ctr, NY USA.
    Schnitt, Stuart J.
    Brigham & Womens Hosp, MA 02115 USA.
    Lakhani, Sunil R.
    Univ Queensland, Australia.
    Rojo, Federico
    CIBERONC Inst Invest Sanitaria Fdn Jimenez Diaz I, Spain; GEICAM Spanish Breast Canc Res Grp, Spain.
    Comerma, Laura
    CIBERONC Inst Invest Sanitaria Fdn Jimenez Diaz I, Spain; GEICAM Spanish Breast Canc Res Grp, Spain.
    Badve, Sunil S.
    Indiana Univ Sch Med, IN 46202 USA.
    Khojasteh, Mehrnoush
    Roche Tissue Diagnost, CA USA.
    Symmans, W. Fraser
    Univ Texas MD Anderson Canc Ctr, TX 77030 USA.
    Sotiriou, Christos
    Univ Libre Bruxelles ULB, Belgium; Univ Libre Bruxelles, Belgium.
    Gonzalez-Ericsson, Paula
    Vanderbilt Univ, TN USA.
    Pogue-Geile, Katherine L.
    NRG Oncol NSABP, PA USA.
    Kim, Rim S.
    NRG Oncol NSABP, PA USA.
    Rimm, David L.
    Yale Univ, CT 06510 USA.
    Viale, Giuseppe
    European Inst Oncol IRCCS, Italy; State Univ Milan, Italy.
    Hewitt, Stephen M.
    NCI, MD 20892 USA.
    Bartlett, John M. S.
    Ontario Inst Canc Res, Canada; Western Gen Hosp, Scotland.
    Penault-Llorca, Frederique
    Ctr Jean Perrin, France; Univ Clermont Auvergne, France.
    Goel, Shom
    Peter MacCallum Canc Ctr, Australia.
    Lien, Huang-Chun
    Natl Taiwan Univ Hosp, Taiwan.
    Loibl, Sibylle
    GBG Forsch GmbH, Germany.
    Kos, Zuzana
    BC Canc, Canada.
    Loi, Sherene
    Univ Melbourne, Australia; Peter MacCallum Canc Ctr, Australia.
    Hanna, Matthew G.
    Mem Sloan Kettering Canc Ctr, NY 10021 USA.
    Michiels, Stefan
    Univ Paris Saclay, France; Univ Paris Sud, France.
    Kok, Marleen
    Netherlands Canc Inst, Netherlands; Netherlands Canc Inst, Netherlands.
    Nielsen, Torsten O.
    Univ British Columbia, Canada.
    Lazar, Alexander J.
    Univ Texas MD Anderson Canc Ctr, TX 77030 USA; Univ Texas MD Anderson Canc Ctr, TX 77030 USA; Univ Texas MD Anderson Canc Ctr, TX 77030 USA; Univ Texas MD Anderson Canc Ctr, TX 77030 USA.
    Bago-Horvath, Zsuzsanna
    Med Univ Vienna, Austria.
    Kooreman, Loes F. S.
    Maastricht Univ, Netherlands; Maastricht Univ, Netherlands.
    van der Laak, Jeroen
    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, Clinical pathology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Radboud Univ Nijmegen, Netherlands.
    Saltz, Joel
    SUNY Stony Brook, NY 11794 USA.
    Gallas, Brandon D.
    FDA CDRH OSEL, MD USA.
    Kurkure, Uday
    Roche Tissue Diagnost, CA USA.
    Barnes, Michael
    Roche Diagnost Informat Solut, CA USA.
    Salgado, Roberto
    Univ Melbourne, Australia; GZA ZNA Ziekenhuizen, Belgium.
    Cooper, Lee A. D.
    Northwestern Univ, IL 60611 USA.
    Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group2020In: npj Breast Cancer, E-ISSN 2374-4677, Vol. 6, no 1, article id 16Article, review/survey (Refereed)
    Abstract [en]

    Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.

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  • 13.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics, Mathematics and Applied Mathematics. Linköping University, The Institute of Technology.
    Global search strategies for solving multilinear least-squares problems2012In: Sultan Qaboos University Journal for Science, ISSN 1027-524X, Vol. 17, no 1, p. 12-21Article in journal (Refereed)
    Abstract [en]

    The multilinear least-squares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrix-vector product. The MLLS is typically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows for moving from one local minimizer to a better one. The efficiency of this strategy is illustrated by results of numerical experiments performed for some problems related to the design of filter networks.

    Download full text (pdf)
    TR2011-17
  • 14.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
    Global Search Strategies for Solving Multilinear Least-squares Problems2011Report (Other academic)
    Abstract [en]

    The multilinear least-squares (MLLS) problem is an extension of the linear least-squares problem. The difference is that a multilinearoperator is used in place of a matrix-vector product. The MLLS istypically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows formoving from one local minimizer to a better one. The efficiencyof this strategy isillustrated by results of numerical experiments performed forsome problems related to the design of filter networks.

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    Global Search Strategies for Solving Multilinear Least-squares Problems
  • 15.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Zikrin, Spartak
    Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
    Sparsity Optimization in Design of Multidimensional Filter Networks2013Report (Other academic)
    Abstract [en]

    Filter networks is a powerful tool used for reducing the image processing time, while maintaining its reasonably high quality.They are composed of sparse sub-filters whose low sparsity ensures fast image processing.The filter network design is related to solvinga sparse optimization problem where a cardinality constraint bounds above the sparsity level.In the case of sequentially connected sub-filters, which is the simplest network structure of those considered in this paper, a cardinality-constrained multilinear least-squares (MLLS) problem is to be solved. If to disregard the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers. Each of the local minimizers is singular and non-isolated.The cardinality constraint makes the problem even more difficult to solve.An approach for approximately solving the cardinality-constrained MLLS problem is presented.It is then applied to solving a bi-criteria optimization problem in which both thetime and quality of image processing are optimized. The developed approach is extended to designing filter networks of a more general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. It is also compared with the existing approaches.

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    Sparsity Optimization in Design of Multidimensional Filter Networks (revised version)
  • 16.
    Andersson, Mats
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Adaptive Spatio-temporal Filtering of 4D CT-Heart2013In: Image Analyses: Image Processing, Computer Vision, Pattern Recognition, and Graphics / [ed] Joni-Kristian Kämäräinen, Markus Koskela, Berlin Heidelberg: Springer, 2013, p. 246-255Conference paper (Refereed)
    Abstract [en]

    The aim of this project is to keep the x-ray exposure of the patient as low as reasonably achievable while improving the diagnostic image quality for the radiologist. The means to achieve these goals is to develop and evaluate an efficient adaptive filtering (denoising/image enhancement) method that fully explores true 4D image acquisition modes.

    The proposed prototype system uses a novel filter set having directional filter responses being monomials. The monomial filter concept is used both for estimation of local structure and for the anisotropic adaptive filtering. Initial tests on clinical 4D CT-heart data with ECG-gated exposure has resulted in a significant reduction of the noise level and an increased detail compared to 2D and 3D methods. Another promising feature is that the reconstruction induced streak artifacts which generally occur in low dose CT are remarkably reduced in 4D.

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    fulltext
  • 17.
    Andersson, Thord
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Dept. of C4ISR, Swedish Defence Research Agency, Linköping, Sweden, .
    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).
    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.
    Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds2018In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 112, p. 340-345Article in journal (Refereed)
    Abstract [en]

    Atlas-based segmentation is often used to segment medical image regions. For intensity-normalized data, the quality of these segmentations is highly dependent on the similarity between the atlas and the target under the used registration method. We propose a geodesic registration method for interactive atlas-based segmentation using empirical multi-scale anatomical manifolds. The method utilizes unlabeled images together with the labeled atlases to learn empirical anatomical manifolds. These manifolds are defined on distinct scales and regions and are used to propagate the labeling information from the atlases to the target along anatomical geodesics. The resulting competing segmentations from the different manifolds are then ranked according to an image-based similarity measure. We used image volumes acquired using magnetic resonance imaging from 36 subjects. The performance of the method was evaluated using a liver segmentation task. The result was then compared to the corresponding performance of direct segmentation using Dice Index statistics. The method shows a significant improvement in liver segmentation performance between the proposed method and direct segmentation. Furthermore, the standard deviation in performance decreased significantly. Using competing complementary manifolds defined over a hierarchy of region of interests gives an additional improvement in segmentation performance compared to the single manifold segmentation.

  • 18.
    Andersson, Thord
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Läthén, Gunnar
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Lenz, Reiner
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    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.
    Modified Gradient Search for Level Set Based Image Segmentation2013In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, p. 621-630Article in journal (Refereed)
    Abstract [en]

    Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.

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  • 19.
    Andersson, Thord
    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).
    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).
    Karlsson, Anette
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Forsgren, Mikael
    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.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Lundberg, Peter
    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. Region Östergötland, 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 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.
    Consistent intensity inhomogeneity correction in water–fat MRI2015In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 2, p. 468-476Article in journal (Refereed)
    Abstract [en]

    PURPOSE:

    To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.

    RESULTS:

    CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).

    CONCLUSION:

    CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type. J. Magn. Reson. Imaging 2014.

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  • 20.
    Andersson, Thord
    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.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Norén, Bengt
    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.
    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. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    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.
    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.
    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.
    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.
    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.
    Self-calibrated DCE MRI using Multi Scale Adaptive Normalized Averaging (MANA)2012In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), 2012, 2012Conference paper (Other academic)
  • 21.
    Ax, Erika
    et al.
    Linköping University, Department of Biomedical Engineering.
    Djerf, Elin
    Linköping University, Department of Biomedical Engineering.
    Automatic Detection of Common Signal Quality Issues in MRI Data using Deep Neural Networks2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Magnetic resonance imaging (MRI) is a commonly used non-invasive imaging technique that provides high resolution images of soft tissue. One problem with MRI is that it is sensitive to signal quality issues. The issues can arise for various reasons, for example by metal located either inside or outside of the body. Another common signal quality issue is caused by the patient being partly placed outside field of view of the MRI scanner.  

    This thesis aims to investigate the possibility to automatically detect these signal quality issues using deep neural networks. More specifically, two different 3D CNN network types were studied, a classification-based approach and a reconstruction-based approach. The datasets used consist of MRI volumes from UK Biobank which have been processed and manually annotated by operators at AMRA Medical. For the classification method four different network architectures were explored utilising supervised learning with multi-label classification. The classification method was evaluated using accuracy and label-based evaluation metrics, such as macro-precision, macro-recall and macro-F1. The reconstruction method was based on anomaly detection using an autoencoder which was trained to reconstruct volumes without any artefacts. A mean squared prediction error was calculated for the reconstructed volume and compared against a threshold in order to classify a volume with or without artefacts. The idea was that volumes containing artefacts should be more difficult to reconstruct and thus, result in a higher prediction error. The reconstruction method was evaluated using accuracy, precision, recall and F1-score. 

    The results show that the classification method has overall higher performance than the reconstruction method. The achieved accuracy for the classification method was 98.0% for metal artefacts and 97.5% for outside field of view artefacts. The best architecture for the classification method proved to be DenseNet201. The reconstruction method worked for metal artefacts with an achieved accuracy of 75.7%. Furthermore, it was concluded that reconstruction method did not work for detection of outside field of view artefacts.   

    The results from the classification method indicate that there is a possibility to automatically detect artefacts with deep neural networks. However, it is needed to further improve the method in order to completely replace a manual quality control step before using the volumes for calculation of biomarkers. 

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  • 22.
    Badian, Reza A.
    et al.
    Oslo Univ Hosp, Norway.
    Utheim, Tor Paaske
    Oslo Univ Hosp, Norway; Sorlandet Hosp Arendal, Norway.
    Lagali, Neil
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Sensory Organs and Communication. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Ophthalmology in Linköping. Sorlandet Hosp Arendal, Norway.
    Region of interest and directional analysis of subbasal nerves in wide-area corneal nerve plexus mosaics in type 2 diabetes mellitus2020In: Scientific Reports, E-ISSN 2045-2322, Vol. 10, no 1, article id 10802Article in journal (Refereed)
    Abstract [en]

    In vivo confocal microscopy (IVCM) imaging of the corneal subbasal nerve plexus (SBNP) is a clinical imaging modality gaining popularity for the diagnosis and follow-up of corneal neuropathy in various conditions such as diabetes mellitus. There remain, however, major limitations to the method, hindering its widespread clinical use. Finding the same exact area of the central cornea to standardize image acquisition is difficult without a reference point. Alternatively, creating wide-area mosaics of the SBNP is resource-intensive and has not yet been developed for routine clinical use. Here, we investigated whether IVCM analysis of the corneal SBNP in a predetermined, anatomically standardized region of interest (ROI) could be applied as an equivalent substitution for wide-area SBNP mosaic generation and analysis. Furthermore, we investigated nerve patterns outside the central corneal region for a possible relationship to type 2 diabetes mellitus status using a publicly available dataset. We found that corneal nerve fibre length density (CNFL) based on the ROI underestimated the mosaic-based CNFL by an average of 34% in 90% of cases (150 eyes), and did not exhibit a significant reduction with diabetes, as seen in the full SBNP. Outside the central cornea, nerve orientation differed depending on the anatomic region (left, central or right superior plexus, P<0.001). Moreover, in long-term type 2 diabetes mellitus (>= 10 years, 28 subjects), nerve density in the left superior sector of the SBNP was decreased (P<0.001) while that in the central superior SBNP increased (P=0.01) relative to 35 age-matched healthy subjects with normal glucose tolerance. These results indicate that subbasal nerve degeneration in type 2 diabetes mellitus can vary according to anatomic location, and regions with potential diagnostic value outside the central SBNP may warrant further investigation.

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  • 23.
    Bae, Sang Won
    et al.
    Kyonggi University, Suwon, South Korea.
    Korman, Matias
    Tohoku University, Sendai, Japan.
    Mitchell, Joseph SB
    Stony Brook University, New York, USA.
    Okamoto, Yoshio
    The University of Electro-Communications, Tokyo, Japan.
    Polishchuk, Valentin
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Wang, Haitao
    Utah State University, Utah, USA.
    Computing the $ L_1 $ Geodesic Diameter and Center of a Polygonal Domain2016In: 33rd Symposium on Theoretical Aspects of Computer Science / [ed] Nicolas Ollinger; Heribert Vollmer, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik , 2016, Vol. 47, p. 14:1-14:14Conference paper (Refereed)
    Abstract [en]

    For a polygonal domain with h holes and a total of n vertices, we present algorithms that compute the L1 geodesic diameter in O(n2+h4) time and the L1 geodesic center in O((n4+n2h4) (n)) time, where (·) denotes the inverse Ackermann function. No algorithms were known for these problems before. For the Euclidean counterpart, the best algorithms compute the geodesic diameter in O(n7.73) or O(n7(h+log n)) time, and compute the geodesic center in O(n12+) time. Therefore, our algorithms are much faster than the algorithms for the Euclidean problems. Our algorithms are based on several interesting observations on L1 shortest paths in polygonal domains.

  • 24.
    Baeza Ortega, José Antonio
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Combined Visualization of Intracardiac Blood Flow and Wall Motion Assessed by MRI2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    MRI is a well known and widely spread technique to characterize cardiac pathologies due to its high spatial resolution, its accessibility and its adjustable contrast among soft tissues.

    In attempt to close the gap between blood flow, myocardial movement and the cardiac fucntion, researching in the MRI field addresses the quantification of some of the most relevant blood and myocardial parameters.

    During this proyect a new tool that allows reading, postprocessing, quantifying and visualizing 2D motion sense MR data has been developed. In order to analyze intracardiac blood flow and wall motion, the new tool quantifies velocity, turbulent kinetic energy, pressure and strain.

    In the results section the final tool is presented, describing the visualization modes, which represent the quantified parameters both individually and combined; and detailing auxiliary tool features as masking, thresholding, zooming, and cursors.

    Finally, thecnical aspects as the convenience of two dimensional examinations to create compact tools, and the challenges of masking as part of the relative pressure calculation, among others, are discussed; ending up with the proposal of some future developments.

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  • 25.
    Bagheri, Maryam
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Rezakhani, Arjang
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Roghani, Mehrdad
    Neurophysiology Research Center, Shahed University, Iran.
    Joghataei, Mohammad T.
    Iran University of Medical Science, Iran.
    Mohseni, Simin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Protocol for Three-dimensional Confocal Morphometric Analysis of Astrocytes2015In: Journal of Visualized Experiments, E-ISSN 1940-087X, no 106, p. e53113-Article in journal (Refereed)
    Abstract [en]

    As glial cells in the brain, astrocytes have diverse functional roles in the central nervous system. In the presence of harmful stimuli, astrocytes modify their functional and structural properties, a condition called reactive astrogliosis. Here, a protocol for assessment of the morphological properties of astrocytes is presented. This protocol includes quantification of 12 different parameters i.e. the surface area and volume of the tissue covered by an astrocyte (astrocyte territory), the entire astrocyte including branches, cell body, and nucleus, as well as total length and number of branches, the intensity of fluorescence immunoreactivity of antibodies used for astrocyte detection, and astrocyte density (number/1,000 mu m(2)). For this purpose three-dimensional (3D) confocal microscopic images were created, and 3D image analysis software such as Volocity 6.3 was used for measurements. Rat brain tissue exposed to amyloid beta(1-40) (A beta(1-40)) with or without a therapeutic intervention was used to present the method. This protocol can also be used for 3D morphometric analysis of other cells from either in vivo or in vitro conditions.

  • 26.
    Bardolet Pettersson, Susana
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Managing imbalanced training data by sequential segmentation in machine learning2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Imbalanced training data is a common problem in machine learning applications. Thisproblem refers to datasets in which the foreground pixels are significantly fewer thanthe background pixels. By training a machine learning model with imbalanced data, theresult is typically a model that classifies all pixels as the background class. A result thatindicates no presence of a specific condition when it is actually present is particularlyundesired in medical imaging applications. This project proposes a sequential system oftwo fully convolutional neural networks to tackle the problem. Semantic segmentation oflung nodules in thoracic computed tomography images has been performed to evaluate theperformance of the system. The imbalanced data problem is present in the training datasetused in this project, where the average percentage of pixels belonging to the foregroundclass is 0.0038 %. The sequential system achieved a sensitivity of 83.1 % representing anincrease of 34 % compared to the single system. The system only missed 16.83% of thenodules but had a Dice score of 21.6 % due to the detection of multiple false positives. Thismethod shows considerable potential to be a solution to the imbalanced data problem withcontinued development.

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  • 27.
    Behjat, Hamid
    et al.
    Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Aganj, Iman
    Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA.
    Abramian, David
    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).
    Eklund, Anders
    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). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Westin, Carl-Fredrik
    Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA.
    Characterization of Spatial Dynamics of Fmri Data in White Matter Using Diffusion-Informed White Matter Harmonics2021In: 2021 IEEE 18th International Symposium On Biomedical Imaging (ISBI), Institute of Electrical and Electronics Engineers (IEEE), 2021Conference paper (Refereed)
    Abstract [en]

    In this work, we leverage the Laplacian eigenbasis of voxelwise white matter (WM) graphs derived from diffusionweighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM harmonics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.

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  • 28.
    Bianchessi, Tamara
    Linköping University, Department of Biomedical Engineering.
    Pediatric Brain Tumor Type Classification in MR Images Using Deep Learning2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Brain tumors present the second highest cause of death among pediatric cancers. About 60% are located in the posterior fossa region of the brain; among the most frequent types the ones considered for this project were astrocytomas, medulloblastomas, and ependymomas. Diagnosis can be done either through invasive histopathology exams or by non-invasive magnetic resonance (MR) scans. The tumors listed can be difficult to diagnose, even for trained radiologists, so machine learning methods, in particular deep learning, can be useful in helping to assess a diagnosis. Deep learning has been investigated only in a few other studies.The dataset used included 115 different subjects, some with multiple scan sessions, for which there were 142 T2-w, 119 T1Gd-w, and 89 volumes that presented both MR modalities. 2D slices have been manually extracted from the registered and skull-stripped volumes in the transversal, sagittal, and frontal anatomical plane and have been preprocessed by normalizing them and selecting the slices containing the tumor. The scans employed are T2-w, T1Gd-w, and a combination of the two referred to as multimodal images. The images were divided session-wise into training, validation, and testing, using stratified cross-validation and have also been augmented. The convolutional neural networks (CNN) investigated were ResNet50, VGG16, and MobileNetV2. The model performances were evaluated for two-class and three-class classification tasks by computing the confusion matrix, accuracy, receiver operating characteristic curve (ROC), the area under the curve (AUROC), and F1-score. Moreover,  explanations for the behavior of networks were investigated using GradCAMs and occlusion maps. Preliminary investigations showed that the best plane and modality were the transversal one and T2-w images. Overall the best model was VGG16, for the two-class tasks the best classification was between astrocytomas and medulloblastomas which reached an F1-score of 0.86 for both classes on multimodal images, followed by astrocytomas and ependymomas with an F1-score of 0.76 for astrocytomas and 0.74 for ependymomas on T2-w, and last F1-score of 0.30 for ependymomas and 0.65 for medulloblastomas on multimodal images. The three-class classification reached F1-score values of 0.59 for astrocytomas, 0.46 for ependymomas, and 0.64 for medulloblastomas on T2-w images. GradCAMs and occlusion maps showed that VGG16 was able to focus mostly on the tumor region but that there also seemed to be other information in the background of the images that contributed to the final classification.To conclude, the classification of infratentorial pediatric brain tumors can be achieved with acceptable results by means of deep learning and using a single MR modality, though one might have to account for the dataset size, number of classes and class imbalance. GradCAMs and occlusion maps offer important insights into the decision process of the networks

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  • 29.
    Bianchessi, Tamara
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Tampu, Iulian Emil
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Haj-Hosseini, Neda
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.
    Classification of pediatric brain tumors based on MR-images using deep learning2022Conference paper (Other academic)
  • 30.
    Björkman, Ann-Sofi
    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).
    Malusek, Alexandr
    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).
    Gauffin, Håkan
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Persson, Anders
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Koskinen, Seppo
    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). Terveystalo Inc, Finland; Karolinska Inst, Sweden.
    Spectral photon-counting CT: Image quality evaluation using a metal-containing bovine bone specimen2023In: European Journal of Radiology, ISSN 0720-048X, E-ISSN 1872-7727, Vol. 168, article id 111110Article in journal (Refereed)
    Abstract [en]

    Purpose: To find the optimal imaging parameters for a photon-counting detector CT (PCD-CT) and to compare it to an energy-integrating detector CT (EID-CT) in terms of image quality and metal artefact severity using a metal-containing bovine knee specimen. Methods: A bovine knee with a stainless-steel plate and screws was imaged in a whole-body research PCD-CT at 120 kV and 140 kV and in an EID dual-source CT (DSCT) at Sn150 kV and 80/Sn150 kV. PCD-CT virtual monoenergetic 72 and 150 keV images and EID-CT images processed with and without metal artefact reduction algorithms (iMAR) were compared. Four radiologists rated the visualisation of bony structures and metal artefact severity. The Friedman test and Wilcoxon signed-rank test with Bonferronis correction were used. P-values of <= 0.0001 were considered statistically significant. Distributions of HU values of regions of interest (ROIs) in artefact-affected areas were analysed.Results: PCD-CT 140 kV 150 keV images received the highest scores and were significantly better than EID-CT Sn150 kV images. PCD-CT 72 keV images were rated significantly lower than all the others. HU-value variation was larger in the 120 kV and the 72 keV images. The ROI analysis revealed no large difference between scanners regarding artefact severity.Conclusion: PCD-CT 140 kV 150 keV images of a metal-containing bovine knee specimen provided the best image quality. They were superior to, or as good as, the best EID-CT images; even without the presumed advantage of tin filter and metal artefact reduction algorithms. PCD-CT is a promising method for reducing metal artefacts.

  • 31.
    Björn, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Laterality Classification of X-Ray Images: Using Deep Learning2021Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    When radiologists examine X-rays, it is crucial that they are aware of the laterality of the examined body part. The laterality refers to which side of the body that is considered, e.g. Left and Right. The consequences of a mistake based on information regarding the incorrect laterality could be disastrous. This thesis aims to address this problem by providing a deep neural network model that classifies X-rays based on their laterality.

    X-ray images contain markers that are used to indicate the laterality of the image. In this thesis, both a classification model and a detection model have been trained to detect these markers and to identify the laterality. The models have been trained and evaluated on four body parts: knees, feet, hands and shoulders. The images can be divided into three laterality classes: Bilateral, Left and Right.

    The model proposed in this thesis is a combination of two classification models: one for distinguishing between Bilateral and Unilateral images, and one for classifying Unilateral images as Left or Right. The latter utilizes the confidence of the predictions to categorize some of them as less accurate (Uncertain), which includes images where the marker is not visible or very hard to identify.

    The model was able to correctly distinguish Bilateral from Unilateral with an accuracy of 100.0 %. For the Unilateral images, 5.00 % were categorized as Uncertain and for the remaining images, 99.99 % of those were classified correctly as Left or Right.

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  • 32.
    Björnfot, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Extension of DIRA (Dual-Energy Iterative Algorithm) to 3D Helical CT2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    There is a need for quantitative CT data in radiation therapy. Currently there are only few algorithms that address this issue, for instance the commercial DirectDensity algorithm. In scientific literature, an example of such an algorithm is DIRA. DIRA is an iterative model-based reconstruction method for dual-energy CT whose goal is to determine the material composition of the patient from accurate linear attenuation coefficients. It has been implemented in a two dimensional geometry, i.e., it could process axial scans only.  There was a need to extend DIRA so that it could process projection data generated in helical scanning geometries. The newly developed algorithm (DIRA-3D) implemented (i) polyenergetic semi-parallel projection generation, (ii) mono-energetic parallel projection generation and (iii) the PI-method for image reconstruction. The computation experiments showed that the accuracies of the resulting LAC and mass fractions were comparable to the ones of the original DIRA. The results converged after 10 iterations.

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  • 33.
    Björnsdotter, Malin
    et al.
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden.
    Rylander, Karin
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden.
    Wessberg, Johan
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden.
    A Monte Carlo method for locally multivariate brain mapping.2011In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 56, no 2, p. 508-516Article in journal (Refereed)
    Abstract [en]

    Locally multivariate approaches to functional brain mapping offer a highly appealing complement to conventional statistics, but require restrictive region-of-interest hypotheses, or, in exhaustive search forms (such as the "searchlight" algorithm; Kriegeskorte et al., 2006), are excessively computer intensive. We therefore propose a non-restrictive, comparatively fast yet highly sensitive method based on Monte Carlo approximation principles where locally multivariate maps are computed by averaging across voxelwise condition-discriminative information obtained from repeated stochastic sampling of fixed-size search volumes. On simulated data containing discriminative regions of varying size and contrast-to-noise ratio (CNR), the Monte Carlo method reduced the required computer resources by as much as 75% compared to the searchlight with no reduction in mapping performance. Notably, the Monte Carlo mapping approach not only outperformed the general linear method (GLM), but also produced higher discriminative voxel detection scores than the searchlight irrespective of classifier (linear or nonlinear support vector machine), discriminative region size or CNR. The improved performance was explained by the information-average procedure, and the Monte Carlo approach yielded mapping sensitivities of a few percent lower than an information-average exhaustive search. Finally, we demonstrate the utility of the algorithm on whole-brain, multi-subject functional magnetic resonance imaging (fMRI) data from a tactile study, revealing that the central representation of gentle touch is spatially distributed in somatosensory, insular and visual regions.

  • 34.
    Björnsdotter, Malin
    et al.
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Göteborg, Sweden.
    Wessberg, Johan
    Department of Physiology, Institute of Neuroscience and Physiology, University of Gothenburg, Göteborg, Sweden.
    Clustered sampling improves random subspace brain mapping2012In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 6, p. 2035-2040Article in journal (Refereed)
    Abstract [en]

    Intuitive and efficient, the random subspace ensemble approach provides an appealing solution to the problem of the vast dimensionality of functional magnetic resonance imaging (fMRI) data for maximal-accuracy brain state decoding. Recently, efforts to generate biologically plausible and interpretable maps of brain regions which contribute information to the ensemble decoding task have been made and two approaches have been introduced: globally multivariate random subsampling and locally multivariate Monte Carlo mapping. Both types of maps reflect voxel-wise decoding accuracies averaged across repeatedly randomly sampled voxel subsets, highlighting voxels which consistently participate in high-classification subsets. We compare the mapping sensitivities of the approaches on realistic simulated data containing both locally and globally multivariate information and demonstrate that utilizing the inherent volumetric nature of fMRI through clustered Monte Carlo mapping yields dramatically improved performances in terms of voxel detection sensitivity and efficiency. These results suggest that, unless a priori information specifically dictates a global search, variants of clustered sampling should be the priority for random subspace brain mapping.

  • 35.
    Boito, Deneb
    et al.
    Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Eklund, Anders
    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). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Tisell, Anders
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine.
    Levi, Richard
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Rehabilitation Medicine. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Özarslan, Evren
    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).
    Blystad, Ida
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine.
    MRI with generalized diffusion encoding reveals damaged white matter in patients previously hospitalized for COVID-19 and with persisting symptoms at follow-up2023In: Brain Communications, E-ISSN 2632-1297, Vol. 5, no 6, article id fcad284Article in journal (Refereed)
    Abstract [en]

    There is mounting evidence of the long-term effects of COVID-19 on the central nervous system, with patients experiencing diverse symptoms, often suggesting brain involvement. Conventional brain MRI of these patients shows unspecific patterns, with no clear connection of the symptomatology to brain tissue abnormalities, whereas diffusion tensor studies and volumetric analyses detect measurable changes in the brain after COVID-19. Diffusion MRI exploits the random motion of water molecules to achieve unique sensitivity to structures at the microscopic level, and new sequences employing generalized diffusion encoding provide structural information which are sensitive to intravoxel features. In this observational study, a total of 32 persons were investigated: 16 patients previously hospitalized for COVID-19 with persisting symptoms of post-COVID condition (mean age 60 years: range 41–79, all male) at 7-month follow-up and 16 matched controls, not previously hospitalized for COVID-19, with no post-COVID symptoms (mean age 58 years, range 46–69, 11 males). Standard MRI and generalized diffusion encoding MRI were employed to examine the brain white matter of the subjects. To detect possible group differences, several tissue microstructure descriptors obtainable with the employed diffusion sequence, the fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, microscopic anisotropy, orientational coherence (Cc) and variance in compartment’s size (CMD) were analysed using the tract-based spatial statistics framework. The tract-based spatial statistics analysis showed widespread statistically significant differences (P < 0.05, corrected for multiple comparisons using the familywise error rate) in all the considered metrics in the white matter of the patients compared to the controls. Fractional anisotropy, microscopic anisotropy and Cc were lower in the patient group, while axial diffusivity, radial diffusivity, mean diffusivity and CMD were higher. Significant changes in fractional anisotropy, microscopic anisotropy and CMD affected approximately half of the analysed white matter voxels located across all brain lobes, while changes in Cc were mainly found in the occipital parts of the brain. Given the predominant alteration in microscopic anisotropy compared to Cc, the observed changes in diffusion anisotropy are mostly due to loss of local anisotropy, possibly connected to axonal damage, rather than white matter fibre coherence disruption. The increase in radial diffusivity is indicative of demyelination, while the changes in mean diffusivity and CMD are compatible with vasogenic oedema. In summary, these widespread alterations of white matter microstructure are indicative of vasogenic oedema, demyelination and axonal damage. These changes might be a contributing factor to the diversity of central nervous system symptoms that many patients experience after COVID-19.

  • 36.
    Bokhorst, John-Melle
    et al.
    Radboud Univ Nijmegen, Netherlands.
    Nagtegaal, Iris D.
    Radboud Univ Nijmegen, Netherlands.
    Fraggetta, Filippo
    Gravina Hosp, Italy.
    Vatrano, Simona
    Gravina Hosp, Italy.
    Mesker, Wilma
    Leids Univ, Netherlands.
    Vieth, Michael
    Friedrich Alexander Univ Erlangen Nuremberg, Germany.
    van der Laak, Jeroen
    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, Clinical pathology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Radboud Univ Nijmegen, Netherlands.
    Ciompi, Francesco
    Radboud Univ Nijmegen, Netherlands.
    Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images2023In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 8398Article in journal (Refereed)
    Abstract [en]

    In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple (n=14 ) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on .

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  • 37.
    Booij, Ronald
    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. Erasmus MC, Netherlands.
    Kammerling, Nina F.
    Linköping University, Department of Health, Medicine and Caring Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Oei, Edwin H. G.
    Erasmus MC, Netherlands.
    Persson, Anders
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Tesselaar, Erik
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Assessment of visibility of bone structures in the wrist using normal and half of the radiation dose with photon-counting detector CT2023In: European Journal of Radiology, ISSN 0720-048X, E-ISSN 1872-7727, Vol. 159, article id 110662Article in journal (Refereed)
    Abstract [en]

    Purpose: To quantitatively and qualitatively assess the visibility of bone structures in the wrist on photon-counting detector computed tomography (PCD-CT) images compared to state-of-the-art energy-integrating de-tector CT (EID-CT).Method: Four human cadaveric wrist specimens were scanned with EID-CT and PCD-CT at identical CTDIvolof 12.2 mGy and with 6.1 mGy (half dose PCD-CT). Axial images were reconstructed using the thinnest possible slice thickness, i.e. 0.4 mm on EID-CT and 0.2 mm on PCD-CT, with the largest image matrix size possible using reconstruction kernels optimized for bone (EID-CT: Ur68, PCD-CT: Br92). Quantitative evaluation was performed to determine contrast-noise ratio (CNR) of bone/ fat, cortical and trabecular sharpness. An observer study using visual grading characteristics (VGC) analysis was performed by six observers to assess the visibility of nutrient canals, trabecular architecture, cortical bone and the general image quality.Results: At equal dose, images obtained with PCD-CT had 39 +/- 6 % lower CNR (p = 0.001), 71 +/- 57 % higher trabecular sharpness in the radius (p = 0.02) and 42 +/- 8 % (p &lt; 0.05) sharper cortical edges than those obtained with EID-CT. This was confirmed by VGC analysis showing a superior visibility of nutrient canals, trabeculae and cortical bone area under the curve (AUC) &gt; 0.89) for PCD-CT, even at half dose.Conclusions: Despite a lower CNR and increased noise, the trabecular and cortical sharpness were twofold higher with PCD-CT. Visual grading analysis demonstrated superior visibility of cortical bone, trabeculae, nutrient canals and an overall improved image quality with PCD-CT over EID-CT. At half dose, PCD-CT also yielded superior image quality, both in quantitative measures and as evaluated by radiologists.

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  • 38.
    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).
    MRI adipose tissue and muscle composition analysis: a review of automation techniques2018In: British Journal of Radiology, ISSN 0007-1285, E-ISSN 1748-880X, Vol. 91, no 1089, article id 20180252Article, review/survey (Refereed)
    Abstract [en]

    MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.

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  • 39.
    Borga, Magnus
    et al.
    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). AMRA Medical AB, Linköping, Sweden.
    Ahlgren, André
    AMRA Medical AB, Linköping, Sweden.
    Weston, Sarah
    AMRA Medical AB, Linköping, Sweden; Edinburgh Imaging Academy, University of Edinburgh, Edinburgh, UK.
    MRI-Based Body Composition Analysis2022In: Basic Protocols in Foods and Nutrition / [ed] Cinthia Bau Betim Cazarin, New York, NY, United States: Springer Nature, 2022, 1, p. 307-334Chapter in book (Refereed)
    Abstract [en]

    Magnetic resonance imaging (MRI) is considered being state-of-the-art technology for body composition analysis. Compared to other indirect techniques such as scales, calipers, bioimpedance, and dual-energy X-ray absorptiometry (DXA), MRI offers direct and precise measurements of the volumes of different tissue compartments and also enables quantification of diffuse fat infiltration in organs. Here, we describe a protocol for acquiring of fat–water-separated MRI data and the image postprocessing required for the quantification of several body composition biomarkers relevant for metabolic research. This protocol has successfully been used in several clinical studies and also in the large UK Biobank population study.

  • 40.
    Borga, Magnus
    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.
    Andersson, Thord
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Swedish Defence Research Agency, Sweden.
    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.
    Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images2016In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2016, p. 3146-3149Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration. 

  • 41.
    Borga, Magnus
    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.
    Thomas, E. Louise
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    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).
    Rosander, Johannes
    Advanced MR Analytics AB, Linköping, Sweden.
    Fitzpatrick, Julie
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, 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 Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Bell, Jimmy D
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Validation of a Fast Method for Quantification of Intra-abdominal and Subcutaneous Adipose Tissue for Large Scale Human Studies2015In: NMR in Biomedicine, ISSN 1099-1492, Vol. 28, no 12, p. 1747-1753Article in journal (Refereed)
    Abstract [en]

    Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as magnetic resonance imaging (MRI) has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles for the use of MRI in large scale studies. In this study we assess the validity of the recently proposed fat-muscle-quantitation-system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images.  Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using SliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by both analysis methods, (Pearson correlation r = 0.97 p<0.001), with the AMRATM Profiler analysis being significantly faster (~3 mins) than the conventional SliceOmatic approach (~40 mins). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 vs SliceOmatic 4.73 ± 1.75 litres, p=0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6 % for IAAT and 1.1 % for ASAT, the inter-observer coefficient of variation was 1.4 % for IAAT and 1.2 % for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.

  • 42.
    Borga, Magnus
    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.
    Virtanen, Kirsi A.
    Turku PET Centre, University of Turku, Finland.
    Romu, Thobias
    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, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Persson, 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 Diagnostics, Department of Radiology in Linköping.
    Nuutila, Pirjo
    Turku PET Centre, University of Turku, Finland.
    Enerbäck, Sven
    Department of Biomedicine, University of Gothenburg, Sweden.
    Brown adipose tissue in humans: detection and functional analysis using PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging), and DECT (Dual Energy Computed Tomography)2014In: Methods in Enzymology: Methods of Adipose Tissue Biology / [ed] Ormond MacDougald, Elsevier, 2014, 1, p. 141-159Chapter in book (Other academic)
    Abstract [en]

    Research with the aim to translate findings of the beneficial effects induced by brown adipose tissue (BAT) on metabolism, as seen in various non-human experimental systems to also include human metabolism requires tools that accurately measure how BAT influences human metabolism. This review sets out to discuss such techniques, how they can be used, what they can measure and also some of their limitations. The focus is on detection and functional analysis of human BAT and how this can be facilitated by applying advanced imaging technology such as:  PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging), and DECT (Dual Energy Computed Tomography).

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  • 43.
    Borga, Magnus
    et al.
    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).
    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 Medicine and Health Sciences.
    Bell, Jimmy
    Westminster University, London, UK.
    Harvey, Nicholas
    University of Southampton, UK.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Heymsfield, Steven
    Pennington Biomedical Research Center, Baton Rouge, LA, US.
    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. Advanced MR Analytics AB, Linköping, Sweden.
    Advanced body composition assessment: From body mass index to body composition profiling2018In: Journal of Investigative Medicine, ISSN 1081-5589, E-ISSN 1708-8267, Vol. 66, no 5, p. 887-895Article, review/survey (Refereed)
    Abstract [en]

    This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative magnetic resonance imaging (MRI). Earlier published studies of this method are summarized, and a previously un-published validation study, based on 4.753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy x-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRI show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 % and 4.6 % for fat (computed from AT) and lean tissue respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of more than 20 %. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat in combination with rapid scanning protocols and efficient image analysis tools make quantitative MRI a powerful tool for advanced body composition assessment. 

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    Advanced body composition assessment: From body mass index to body composition profiling
  • 44.
    Brandl, Miriam B
    et al.
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
    Beck, Dominik
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
    Pham, Tuan D
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia .
    Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer2011Conference paper (Refereed)
    Abstract [en]

    The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

  • 45.
    Broad, A.
    et al.
    School of Computing, University of Leeds, Sir William Henry Bragg Building, Woodhouse Lane, UK, Leeds, LS2 9BW, United Kingdom; Leeds Institute for Data Analytics, University of Leeds, Level 11, Worsley Building, Clarendon Way, United Kingdom.
    Wright, A.I.
    Leeds Teaching Hospitals NHS Trust, Beckett St, Harehills, UK, Leeds, LS9 7TF, United Kingdom; Division of Pathology and Data Analytics, Leeds Institute of Medical Research, University of Leeds, St Jamess University Hospital, United Kingdom.
    de, Kamps M.
    School of Computing, University of Leeds, Sir William Henry Bragg Building, Woodhouse Lane, UK, Leeds, LS2 9BW, United Kingdom; Leeds Institute for Data Analytics, University of Leeds, Level 11, Worsley Building, Clarendon Way, UK, Leeds, LS2 9NL, United Kingdom; The Alan Turing Institute, 96 Euston Road, United Kingdom.
    Treanor, Darren
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Inflammation and Infection. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology. Leeds Teaching Hospitals NHS Trust, Beckett St, Harehills, United Kingdom; University of Leeds, United Kingdom.
    Attention-guided sampling for colorectal cancer analysis with digital pathology2022In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 13, article id 100110Article in journal (Refereed)
    Abstract [en]

    Improvements to patient care through the development of automated image analysis in pathology are restricted by the small image patch size that can be processed by convolutional neural networks (CNNs), when compared to the whole-slide image (WSI). Tile-by-tile processing across the entire WSI is slow and inefficient. While this may improve with future computing power, the technique remains vulnerable to noise from uninformative image areas. We propose a novel attention-inspired algorithm that selects image patches from informative parts of the WSI, first using a sparse randomised grid pattern, then iteratively re-sampling at higher density in regions where a CNN classifies patches as tumour. Subsequent uniform sampling across the enclosing region of interest (ROI) is used to mitigate sampling bias. Benchmarking tests informed the adoption of VGG19 as the main CNN architecture, with 79% classification accuracy. A further CNN was trained to separate false-positive normal epithelium from tumour epithelium, in a novel adaptation of a two-stage model used in brain imaging. These subsystems were combined in a processing pipeline to generate spatial distributions of classified patches from unseen WSIs. The ROI was predicted with a mean F1 (Dice) score of 86.6% over 100 evaluation WSIs. Several algorithms for evaluating tumour–stroma ratio (TSR) within the ROI were compared, giving a lowest root mean square (RMS) error of 11.3% relative to pathologists’ annotations, against 13.5% for an equivalent tile-by-tile pipeline. Our pipeline processed WSIs between 3.3x and 6.3x faster than tile-by-tile processing. We propose our attention-based sampling pipeline as a useful tool for pathology researchers, with the further potential for incorporating additional diagnostic calculations. © 2022 The Authors

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  • 46.
    Bromée, Ruben
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Diffusion models for anomaly detection in digital pathology2023Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Challenges within the field of pathology leads to a high workload for pathologists. Machine learning has the ability to assist pathologists in their daily work and has shown good performance in a research setting. Anomaly detection is useful for preventing machine learning models used for classification and segmentation to be applied on data outside of the training distribution of the model. The purpose of this work was to create an optimal anomaly detection pipeline for digital pathology data using a latent diffusion model and various image similarity metrics. An anomaly detection pipeline was created which used a partial diffusion process, a combined similarity metric containing the result of multiple other similarity metrics and a contrast matching strategy for better anomaly detection performance. The anomaly detection pipeline had a good performance in an out-of-distribution detection task with an ROC-AUC score of 0.90.

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  • 47.
    Bujack, Roxana
    et al.
    Leipzig University, Leipzig, Germany.
    Hotz, Ingrid
    Zuse Institute Berlin, Germany.
    Scheuermann, Gerik
    Leipzig University, Leipzig, Germany.
    Hitzer, Eckhard
    International Christian University, Tokyo, Japan.
    Moment Invariants for 2D Flow Fields Using Normalization in Detail2015In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 21, no 8, p. 916-929Article in journal (Refereed)
    Abstract [en]

    The analysis of 2D flow data is often guided by the search for characteristic structures with semantic meaning. One way to approach this question is to identify structures of interest by a human observer, with the goal of finding similar structures in the same or other datasets. The major challenges related to this task are to specify the notion of similarity and define respective pattern descriptors. While the descriptors should be invariant to certain transformations, such as rotation and scaling, they should provide a similarity measure with respect to other transformations, such as deformations. In this paper, we propose to use moment invariants as pattern descriptors for flow fields. Moment invariants are one of the most popular techniques for the description of objects in the field of image recognition. They have recently also been applied to identify 2D vector patterns limited to the directional properties of flow fields. Moreover, we discuss which transformations should be considered for the application to flow analysis. In contrast to previous work, we follow the intuitive approach of moment normalization, which results in a complete and independent set of translation, rotation, and scaling invariant flow field descriptors. They also allow to distinguish flow features with different velocity profiles. We apply the moment invariants in a pattern recognition algorithm to a real world dataset and show that the theoretical results can be extended to discrete functions in a robust way.

  • 48. Order onlineBuy this publication >>
    Bustamante, Mariana
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Automated Assessment of Blood Flow in the Cardiovascular System Using 4D Flow MRI2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Medical image analysis focuses on the extraction of meaningful information from medical images in order to facilitate clinical assessment, diagnostics and treatment. Image processing techniques have gradually become an essential part of the modern health care system, a consequence of the continuous technological improvements and the availability of a variety of medical imaging techniques.

    Magnetic Resonance Imaging (MRI) is an imaging technique that stands out as non-invasive, highly versatile, and capable of generating high quality images without the use of ionizing radiation. MRI is frequently performed in the clinical setting to assess the morphology and function of the heart and vessels. When focusing on the cardiovascular system, blood flow visualization and quantification is essential in order to fully understand and identify related pathologies. Among the variety of MR techniques available for cardiac imaging, 4D Flow MRI allows for full three-dimensional spatial coverage over time, also including three-directional velocity information. It is a very powerful technique that can be used for retrospective analysis of blood flow dynamics at any location in the acquired volume.

    In the clinical routine, however, flow analysis is typically done using two-dimensional imaging methods. This can be explained by their shorter acquisition times, higher in-plane spatial resolution and signal-to-noise ratio, and their relatively simpler post-processing requirements when compared to 4D Flow MRI. The extraction of useful knowledge from 4D Flow MR data is especially challenging due to the large amount of information included in these images, and typically requires substantial user interaction.

    This thesis aims to develop and evaluate techniques that facilitate the post-processing of thoracic 4D Flow MRI by automating the steps necessary to obtain hemodynamic parameters of interest from the data. The proposed methods require little to no user interaction, are fairly quick, make effective use of the information available in the four-dimensional images, and can easily be applied to sizable groups of data.The addition of the proposed techniques to the current pipeline of 4D Flow MRI analysis simplifies and expedites the assessment of these images, thus bringing them closer to the clinical routine.

    List of papers
    1. Improving left ventricular segmentation in four-dimensional flow MRI using intramodality image registration for cardiac blood flow analysis
    Open this publication in new window or tab >>Improving left ventricular segmentation in four-dimensional flow MRI using intramodality image registration for cardiac blood flow analysis
    Show others...
    2018 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 79, no 1, p. 554-560Article in journal (Refereed) Published
    Abstract [en]

    PurposeAssessment of blood flow in the left ventricle using four-dimensional flow MRI requires accurate left ventricle segmentation that is often hampered by the low contrast between blood and the myocardium. The purpose of this work is to improve left-ventricular segmentation in four-dimensional flow MRI for reliable blood flow analysis. MethodThe left ventricle segmentations are first obtained using morphological cine-MRI with better in-plane resolution and contrast, and then aligned to four-dimensional flow MRI data. This alignment is, however, not trivial due to inter-slice misalignment errors caused by patient motion and respiratory drift during breath-hold based cine-MRI acquisition. A robust image registration based framework is proposed to mitigate such errors automatically. Data from 20 subjects, including healthy volunteers and patients, was used to evaluate its geometric accuracy and impact on blood flow analysis. ResultsHigh spatial correspondence was observed between manually and automatically aligned segmentations, and the improvements in alignment compared to uncorrected segmentations were significant (Pamp;lt;0.01). Blood flow analysis from manual and automatically corrected segmentations did not differ significantly (Pamp;gt;0.05). ConclusionOur results demonstrate the efficacy of the proposed approach in improving left-ventricular segmentation in four-dimensional flow MRI, and its potential for reliable blood flow analysis. Magn Reson Med 79:554-560, 2018. (c) 2017 International Society for Magnetic Resonance in Medicine.

    Place, publisher, year, edition, pages
    WILEY, 2018
    Keywords
    four-dimensional flow MRI; image registration; blood flow analysis; cardiology; MRI; 4D flow MRI; image registration; blood flow analysis; cardiology; MRI
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-143887 (URN)10.1002/mrm.26674 (DOI)000417926300054 ()28303611 (PubMedID)
    Note

    Funding Agencies|ERC [310612]; 310612; Grant sponsor: Vetenskapsradet [621-2014-6191]; Hjart-Lungfonden [20140398]; Knut och Alice Wallenbergs Stiftelse [KAW 2013.0076]

    Available from: 2017-12-29 Created: 2017-12-29 Last updated: 2018-03-22
    2. Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification
    Open this publication in new window or tab >>Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification
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    2015 (English)In: Journal of Cardiovascular Magnetic Resonance, ISSN 1097-6647, E-ISSN 1532-429X, Vol. 17, no 87Article in journal (Refereed) Published
    Abstract [en]

    Background: Flow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases. Clinically, it is often based on semi-automatic segmentation of a vessel throughout the cardiac cycle in 2D cine phase-contrast Cardiovascular Magnetic Resonance (CMR) images. Three-dimensional (3D), time-resolved phase-contrast CMR with three-directional velocity encoding (4D flow CMR) permits assessment of net flow volumes and flow patterns retrospectively at any location in a time-resolved 3D volume. However, analysis of these datasets can be demanding. The aim of this study is to develop and evaluate a fully automatic method for segmentation and analysis of 4D flow CMR data of the great thoracic vessels. Methods: The proposed method utilizes atlas-based segmentation to segment the great thoracic vessels in systole, and registration between different time frames of the cardiac cycle in order to segment these vessels over time. Additionally, net flow volumes are calculated automatically at locations of interest. The method was applied on 4D flow CMR datasets obtained from 11 healthy volunteers and 10 patients with heart failure. Evaluation of the method was performed visually, and by comparison of net flow volumes in the ascending aorta obtained automatically (using the proposed method), and semi-automatically. Further evaluation was done by comparison of net flow volumes obtained automatically at different locations in the aorta, pulmonary artery, and caval veins. Results: Visual evaluation of the generated segmentations resulted in good outcomes for all the major vessels in all but one dataset. The comparison between automatically and semi-automatically obtained net flow volumes in the ascending aorta resulted in very high correlation (r(2) = 0.926). Moreover, comparison of the net flow volumes obtained automatically in other vessel locations also produced high correlations where expected: pulmonary trunk vs. proximal ascending aorta (r(2) = 0.955), pulmonary trunk vs. pulmonary branches (r(2) = 0.808), and pulmonary trunk vs. caval veins (r(2) = 0.906). Conclusions: The proposed method allows for automatic analysis of 4D flow CMR data, including vessel segmentation, assessment of flow volumes at locations of interest, and 4D flow visualization. This constitutes an important step towards facilitating the clinical utility of 4D flow CMR.

    Place, publisher, year, edition, pages
    BIOMED CENTRAL LTD, 2015
    Keywords
    4D flow cardiovascular magnetic resonance (4D flow CMR); Flow volume; Image segmentation; Image registration; Phase contrast
    National Category
    Clinical Medicine
    Identifiers
    urn:nbn:se:liu:diva-122197 (URN)10.1186/s12968-015-0190-5 (DOI)000362164100002 ()26438074 (PubMedID)
    Note

    Funding Agencies|Swedish Heart and Lung foundation; Swedish Research Council; European Research Council (HEART4FLOW) [310612]

    Available from: 2015-10-26 Created: 2015-10-23 Last updated: 2018-03-22
    3. Improving visualization of 4D flow cardiovascular magnetic resonance with four-dimensional angiographic data: generation of a 4D phase-contrast magnetic resonance CardioAngiography (4D PC-MRCA)
    Open this publication in new window or tab >>Improving visualization of 4D flow cardiovascular magnetic resonance with four-dimensional angiographic data: generation of a 4D phase-contrast magnetic resonance CardioAngiography (4D PC-MRCA)
    2017 (English)In: Journal of Cardiovascular Magnetic Resonance, ISSN 1097-6647, E-ISSN 1532-429X, Vol. 19, article id 47Article in journal (Refereed) Published
    Abstract [en]

    Magnetic Resonance Angiography (MRA) and Phase-Contrast MRA (PC-MRA) approaches used for assessment of cardiovascular morphology typically result in data containing information from the entire cardiac cycle combined into one 2D or 3D image. Information specific to each timeframe of the cardiac cycle is, however, lost in this process. This study proposes a novel technique, called Phase-Contrast Magnetic Resonance CardioAngiography (4D PC-MRCA), that utilizes the full potential of 4D Flow CMR when generating temporally resolved PC-MRA data to improve visualization of the heart and major vessels throughout the cardiac cycle. Using non-rigid registration between the timeframes of the 4D Flow CMR acquisition, the technique concentrates information from the entire cardiac cycle into an angiographic dataset at one specific timeframe, taking movement over the cardiac cycle into account. Registration between the timeframes is used once more to generate a time-resolved angiography. The method was evaluated in ten healthy volunteers. Visual comparison of the 4D PC-MRCAs versus PC-MRAs generated from 4D Flow CMR using the traditional approach was performed by two observers using Maximum Intensity Projections (MIPs). The 4D PC-MRCAs resulted in better visibility of the main anatomical regions of the cardiovascular system, especially where cardiac or vessel motion was present. The proposed method represents an improvement over previous PC-MRA generation techniques that rely on 4D Flow CMR, as it effectively utilizes all the information available in the acquisition. The 4D PC-MRCA can be used to visualize the motion of the heart and major vessels throughout the entire cardiac cycle.

    Place, publisher, year, edition, pages
    BIOMED CENTRAL LTD, 2017
    Keywords
    Computer-Assisted Image Analysis; 4D flow cardiovascular magnetic resonance (4D flow CMR); Phase-Contrast Magnetic Resonance Angiography (PC-MRA)
    National Category
    Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-139277 (URN)10.1186/s12968-017-0360-8 (DOI)000404063800001 ()28645326 (PubMedID)
    Note

    Funding Agencies|European Research Council [310612]; Swedish Heart and Lung foundation [20140398]; Swedish Research Council [621-2014-6191]

    Available from: 2017-07-07 Created: 2017-07-07 Last updated: 2018-03-22
    4. Creating Hemodynamic Atlases of Cardiac 4D Flow MRI
    Open this publication in new window or tab >>Creating Hemodynamic Atlases of Cardiac 4D Flow MRI
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    2017 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 46, no 5, p. 1389-1399Article in journal (Refereed) Published
    Abstract [en]

    Purpose: Hemodynamic atlases can add to the pathophysiological understanding of cardiac diseases. This study proposes a method to create hemodynamic atlases using 4D Flow magnetic resonance imaging (MRI). The method is demonstrated for kinetic energy (KE) and helicity density (Hd). Materials and Methods: Thirteen healthy subjects underwent 4D Flow MRI at 3T. Phase-contrast magnetic resonance cardioangiographies (PC-MRCAs) and an average heart were created and segmented. The PC-MRCAs, KE, and Hd were nonrigidly registered to the average heart to create atlases. The method was compared with 1) rigid, 2) affine registration of the PC-MRCAs, and 3) affine registration of segmentations. The peak and mean KE and Hd before and after registration were calculated to evaluate interpolation error due to nonrigid registration. Results: The segmentations deformed using nonrigid registration overlapped (median: 92.3%) more than rigid (23.1%, P amp;lt; 0.001), and affine registration of PC-MRCAs (38.5%, P amp;lt; 0.001) and affine registration of segmentations (61.5%, P amp;lt; 0.001). The peak KE was 4.9 mJ using the proposed method and affine registration of segmentations (P50.91), 3.5 mJ using rigid registration (P amp;lt; 0.001), and 4.2 mJ using affine registration of the PC-MRCAs (P amp;lt; 0.001). The mean KE was 1.1 mJ using the proposed method, 0.8 mJ using rigid registration (P amp;lt; 0.001), 0.9 mJ using affine registration of the PC-MRCAs (P amp;lt; 0.001), and 1.0 mJ using affine registration of segmentations (P50.028). The interpolation error was 5.262.6% at mid-systole, 2.863.8% at early diastole for peak KE; 9.669.3% at mid-systole, 4.064.6% at early diastole, and 4.964.6% at late diastole for peak Hd. The mean KE and Hd were not affected by interpolation. Conclusion: Hemodynamic atlases can be obtained with minimal user interaction using nonrigid registration of 4D Flow MRI. Level of Evidence: 2 Technical Efficacy: Stage 1

    Place, publisher, year, edition, pages
    WILEY, 2017
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:liu:diva-142968 (URN)10.1002/jmri.25691 (DOI)000412894800015 ()28295788 (PubMedID)
    Note

    Funding Agencies|ERC [Heart4flow, 310612]; Swedish Research Council [621-2014-6191]; Swedish Heart and Lung Foundation [20140398]

    Available from: 2017-11-13 Created: 2017-11-13 Last updated: 2018-03-22
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    Automated Assessment of Blood Flow in the Cardiovascular System Using 4D Flow MRI
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  • 49.
    Bustamante, Mariana
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Gupta, Vikas
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Forsberg, Daniel
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden.
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Automated multi-atlas segmentation of cardiac 4D flow MRI2018In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 49, p. 128-140Article in journal (Refereed)
    Abstract [en]

    Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time-resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b-SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi-atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four-dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left-ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end-systolic and end-diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b-SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters.

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  • 50.
    Bustamante, Mariana
    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. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Viola, Federica
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Carlhäll, Carljohan
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    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).
    Using Deep Learning to Emulate the Use of an External Contrast Agent in Cardiovascular 4D Flow MRI2021In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 54, no 3, p. 777-786Article in journal (Refereed)
    Abstract [en]

    Background Although contrast agents would be beneficial, they are seldom used in four-dimensional (4D) flow magnetic resonance imaging (MRI) due to potential side effects and contraindications. Purpose To develop and evaluate a deep learning architecture to generate high blood-tissue contrast in noncontrast 4D flow MRI by emulating the use of an external contrast agent. Study Type Retrospective. Subjects Of 222 data sets, 141 were used for neural network (NN) training (69 with and 72 without contrast agent). Evaluation was performed on the remaining 81 noncontrast data sets. Field Strength/Sequences Gradient echo or echo-planar 4D flow MRI at 1.5 T and 3 T. Assessment A cyclic generative adversarial NN was trained to perform image translation between noncontrast and contrast data. Evaluation was performed quantitatively using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), structural similarity index (SSIM), mean squared error (MSE) of edges, and Dice coefficient of segmentations. Three observers performed a qualitative assessment of blood-tissue contrast, noise, presence of artifacts, and image structure visualization. Statistical Tests The Wilcoxon rank-sum test evaluated statistical significance. Kendalls concordance coefficient assessed interobserver agreement. Results Contrast in the regions of interest (ROIs) in the NN enhanced images increased by 88%, CNR increased by 63%, and SNR improved by 48% (all P &lt; 0.001). The SSIM was 0.82 +/- 0.01, and the MSE of edges was 0.09 +/- 0.01 (range [0,1]). Segmentations based on the generated images resulted in a Dice similarity increase of 15.25%. The observers managed to differentiate between contrast MR images and our results; however, they preferred the NN enhanced images in 76.7% of cases. This percentage increased to 93.3% for phase-contrast MR angiograms created from the NN enhanced data. Visual grading scores were blood-tissue contrast = 4.30 +/- 0.74, noise = 3.12 +/- 0.98, and presence of artifacts = 3.63 +/- 0.76. Image structures within and without the ROIs resulted in scores of 3.42 +/- 0.59 and 3.07 +/- 0.71, respectively (P &lt; 0.001). Data Conclusion The proposed approach improves blood-tissue contrast in MR images and could be used to improve data quality, visualization, and postprocessing of cardiovascular 4D flow data. Evidence Level 3 Technical Efficacy Stage 1

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