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Lund N, Dahlqvist Leinhard O, Elliott JM, Peterson G, Borga M, Zsigmond P, Karlsson A, Peolsson A. Fatty infiltrate and neck muscle volume in individuals with chronic whiplash associated disorders compared to healthy controls - a cross sectional case-control study. BMC Musculoskelet Disord 2023; 24:181. [PMID: 36906537 PMCID: PMC10007742 DOI: 10.1186/s12891-023-06289-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/02/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND The underlying pathophysiological mechanisms of chronic Whiplash Associated Disorders (WAD) are not fully understood. More knowledge of morphology is needed to better understand the disorder, improve diagnostics and treatments. The aim was to investigate dorsal neck muscle volume (MV) and muscle fat infiltration (MFI) in relation to self-reported neck disability among 30 participants with chronic WAD grade II-III compared to 30 matched healthy controls. METHODS MV and MFI at spinal segments C4 through C7 in both sexes with mild- to moderate chronic WAD (n = 20), severe chronic WAD (n = 10), and age- and sex matched healthy controls (n = 30) was compared. Muscles: trapezius, splenius, semispinalis capitis and semispinalis cervicis were segmented by a blinded assessor and analyzed. RESULTS Higher MFI was found in right trapezius (p = 0.007, Cohen's d = 0.9) among participants with severe chronic WAD compared to healthy controls. No other significant difference was found for MFI (p = 0.22-0.95) or MV (p = 0.20-0.76). CONCLUSIONS There are quantifiable changes in muscle composition of right trapezius on the side of dominant pain and/or symptoms, among participants with severe chronic WAD. No other statistically significant differences were shown for MFI or MV. These findings add knowledge of the association between MFI, muscle size and self-reported neck disability in chronic WAD. TRIAL REGISTRATION NA. This is a cross-sectional case-control embedded in a cohort study.
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Affiliation(s)
- Nils Lund
- Department of Health, Medicine and Caring Sciences, Unit of Physiotherapy, Linköping University, Linköping, Sweden.
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - Olof Dahlqvist Leinhard
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - James M Elliott
- Faculty of Medicine and Health, School of Health Sciences, Northern Sydney Local Health District, The Kolling Institute, University of Sydney, St Leonards, NSW, Australia
- Feinberg School of Medicine, Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, USA
| | - Gunnel Peterson
- Department of Health, Medicine and Caring Sciences, Unit of Physiotherapy, Linköping University, Linköping, Sweden
- Centre for Clinical Research Sörmland, Uppsala University, Uppsala, Sweden
| | - Magnus Borga
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Peter Zsigmond
- Department of Neurosurgery and Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Anette Karlsson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Anneli Peolsson
- Department of Health, Medicine and Caring Sciences, Unit of Physiotherapy, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Occupational and Environmental Medicine Center, Department of Health, Medicine and Caring Sciences, Unit of Clinical Medicine, Linköping University, Linköping, Sweden
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Karlsson M, Indurain A, Romu T, Tunon P, Segelmark M, Uhlin F, Fernström A, Leinhard OD. Assessing Tissue Hydration Dynamics Based on Water/Fat Separated MRI. J Magn Reson Imaging 2023. [PMID: 36591977 DOI: 10.1002/jmri.28581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/13/2022] [Accepted: 12/13/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Optimal fluid status is an important issue in hemodialysis. Clinical evaluation of volume status and different diagnostic tools are used to determine hydration status in these patients. However, there is still no accurate method for this assessment. PURPOSE To propose and evaluate relative lean water signal (LWSrel ) as a water-fat MRI-based tissue hydration measurement. STUDY TYPE Prospective. POPULATION A total of 16 healthy subjects (56 ± 6 years, 0 male) and 11 dialysis patients (60.3 ± 12.3 years, 9 male; dialysis time per week 15 ± 3.5 hours, dialysis duration 31.4 ± 27.9 months). FIELD STRENGTH/SEQUENCE A 3 T; 3D spoiled gradient echo. ASSESSMENT LWSrel , a measurement of the water concentration of tissue, was estimated from fat-referenced MR images. Segmentations of total adipose tissue as well as thigh and calf muscles were used to measure LWSrel and tissue volumes. LWSrel was compared between healthy subjects and dialysis patients, the latter before and after dialysis. Bioimpedance-based body composition monitor over hydration (BCM OH) was also measured. STATISTICAL TESTS T-tests were used to compare differences between the healthy subjects and dialysis patients, as well as changes between before and after dialysis. Pearson correlation was calculated between MRI and non-MRI biomarkers. A P value <0.05 was considered statistically significant. RESULTS The LWSrel in adipose tissue was significantly higher in the dialysis cohort compared with the healthy cohort (246.8% ± 60.0% vs. 100.0% ± 10.8%) and decreased significantly after dialysis (246.8 ± 60.0% vs. 233.8 ± 63.4%). Thigh and calf muscle volumes also significantly decreased by 3.78% ± 1.73% and 2.02% ± 2.50% after dialysis. There was a significant correlation between changes in adipose tissue LWSrel and ultrafiltration volume (r = 87), as well as with BCM OH (r = 0.66). DATA CONCLUSION MRI-based LWSrel and tissue volume measurements are sensitive to tissue hydration changes occurring during dialysis. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 3.
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Affiliation(s)
| | - Ainhoa Indurain
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Department of Nephrology, Linköping University Hospital, Linköping, Sweden.,Department of Acute Internal Medicine and Geriatrics, Linköping University Hospital, Linköping, Sweden
| | - Thobias Romu
- AMRA Medical AB, Linköping, Sweden.,Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | | | - Mårten Segelmark
- Department of Clinical Sciences, Lund University, Lund, Sweden.,Division of Nephrology Lund, Skåne University Hospital, Lund, Sweden
| | - Fredrik Uhlin
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Department of Nephrology, Linköping University Hospital, Linköping, Sweden.,Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia
| | - Anders Fernström
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Department of Nephrology, Linköping University Hospital, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- AMRA Medical AB, Linköping, Sweden.,Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
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3
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Sundermann B, Billebaut B, Bauer J, Iacoban CG, Alykova O, Schülke C, Gerdes M, Kugel H, Neduvakkattu S, Bösenberg H, Mathys C. Practical Aspects of novel MRI Techniques in Neuroradiology: Part 1-3D Acquisitions, Dixon Techniques and Artefact Reduction. ROFO-FORTSCHR RONTG 2022; 194:1100-1108. [PMID: 35545104 DOI: 10.1055/a-1800-8692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Recently introduced MRI techniques offer improved image quality and facilitate examinations of patients even when artefacts are expected. They pave the way for novel diagnostic imaging strategies in neuroradiology. These methods include improved 3D imaging, movement and metal artefact reduction techniques as well as Dixon techniques. METHODS Narrative review with an educational focus based on current literature research and practical experiences of different professions involved (physicians, MRI technologists/radiographers, physics/biomedical engineering). Different hardware manufacturers are considered. RESULTS AND CONCLUSIONS 3D FLAIR is an example of a versatile 3D Turbo Spin Echo sequence with broad applicability in routine brain protocols. It facilitates detection of smaller lesions and more precise measurements for follow-up imaging. It also offers high sensitivity for extracerebral lesions. 3D techniques are increasingly adopted for imaging arterial vessel walls, cerebrospinal fluid spaces and peripheral nerves. Improved hybrid-radial acquisitions are available for movement artefact reduction in a broad application spectrum. Novel susceptibility artefact reduction techniques for targeted application supplement previously established metal artefact reduction sequences. Most of these techniques can be further adapted to achieve the desired diagnostic performances. Dixon techniques allow for homogeneous fat suppression in transition areas and calculation of different image contrasts based on a single acquisition. KEY POINTS · 3D FLAIR can replace 2 D FLAIR for most brain imaging applications and can be a cornerstone of more precise and more widely applicable protocols.. · Further 3D TSE sequences are increasingly replacing 2D TSE sequences for specific applications.. · Improvement of artefact reduction techniques increase the potential for effective diagnostic MRI exams despite movement or near metal implants.. · Dixon techniques facilitate homogeneous fat suppression and simultaneous acquisition of multiple contrasts.. CITATION FORMAT · Sundermann B, Billebaut B, Bauer J et al. Practical Aspects of novel MRI Techniques in Neuroradiology: Part 1-3D Acquisitions, Dixon Techniques and Artefact Reduction. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1800-8692.
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Affiliation(s)
- Benedikt Sundermann
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Germany.,Clinic for Radiology, University Hospital Münster, Germany
| | - Benoit Billebaut
- Clinic for Radiology, University Hospital Münster, Germany.,School for Radiologic Technologists, University Hospital Münster, Germany
| | - Jochen Bauer
- Clinic for Radiology, University Hospital Münster, Germany
| | - Catalin George Iacoban
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany
| | - Olga Alykova
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany
| | | | - Maike Gerdes
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany
| | - Harald Kugel
- Clinic for Radiology, University Hospital Münster, Germany
| | | | - Holger Bösenberg
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany
| | - Christian Mathys
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Germany.,Department of Diagnostic and Interventional Radiology, University of Düsseldorf, Germany
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Karlsson A, Peolsson A, Romu T, Dahlqvist Leinhard O, Spetz Holm AC, Thorell S, West J, Borga M. The effect on precision and T1 bias comparing two flip angles when estimating muscle fat infiltration using fat-referenced chemical shift-encoded imaging. NMR IN BIOMEDICINE 2021; 34:e4581. [PMID: 34232549 DOI: 10.1002/nbm.4581] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 05/26/2021] [Accepted: 06/15/2021] [Indexed: 06/13/2023]
Abstract
Investigation of the effect on accuracy and precision of different parameter settings is important for quantitative MRI. The purpose of this study was to investigate T1 bias and precision for muscle fat infiltration (MFI) measurements using fat-referenced chemical shift MFI measurements at flip angles of 5° and 10°. The fat-referenced measurements were compared with fat fractions, which is a more commonly used measure of MFI. This retrospective study was performed on data from a clinical intervention study including 40 postmenopausal women. Test and retest images were acquired with a 3-T scanner using four-point 3D spoiled gradient multiecho acquisition. Postprocessing included T2* correction and fat-referenced calibration, where the fat signal was calibrated using adipose tissue as reference. The mean MFI was calculated in six different muscle regions using both the fat-referenced fat signal and the fat fraction, defined as the fat signal divided by the sum of the fat and water signals. Both methods used the same fat and water images as input. The variance of the difference between mean MFI from test and retest was used as the measure of precision. The signal-to-noise ratio (SNR) characteristics were analyzed by measuring the full width at half maximum (FWHM) of the fat signal distribution. There was no difference in the mean MFI at different flip angles for the fat-referenced technique (p = 0.66), while the measured fat fractions were 3.3 percentage points larger for 10° compared with 5° (p < 0.001). No significant difference in the precision was found in any of the muscles analyzed. However, the FWHM of the fat signal distribution was significantly (p = 0.01) lower at 10°. This strenghtens the hypothesis that fat-referenced MFI is insensitive to flip angle-induced T1 bias in CSE-MRI, enabling usage of a higher and more SNR-effective flip angle. The lower FWHM in fat-referenced MFI at 10° indicates that high flip angle acquisition is advantageous even although no significant differences in precision were observed comparing 5° and 10°.
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Affiliation(s)
- Anette Karlsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Sciences and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Anneli Peolsson
- Center for Medical Image Sciences and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Health, Medicine and Caring Sciences, unit of Physiotherapy, Linköping University, Linköping, Sweden
| | | | - Olof Dahlqvist Leinhard
- Center for Medical Image Sciences and Visualization (CMIV), Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Anna-Clara Spetz Holm
- Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
| | - Sofia Thorell
- Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
| | - Janne West
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Sciences and Visualization (CMIV), Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
| | - Magnus Borga
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Sciences and Visualization (CMIV), Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
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5
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Huang J, Chen L, Chan KWY, Cai C, Cai S, Chen Z. Super-resolved water/fat image reconstruction based on single-shot spatiotemporally encoded MRI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 314:106736. [PMID: 32361511 DOI: 10.1016/j.jmr.2020.106736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 04/11/2020] [Accepted: 04/18/2020] [Indexed: 06/11/2023]
Abstract
Single-shot spatiotemporally encoded (SPEN) MRI has been validated to possess considerable performance in both spatial and temporal resolution. Water/fat separation is essential for MRI applications in which only water signal is needed. In this article, a super-resolved water/fat image reconstruction method (dubbed SWAF) combined prior knowledge was developed based on single-shot SPEN MRI. The point spread function of spatiotemporal encoding under multiple chemical shifts situation was derived and used for constructing an equation for SWAF image reconstruction. By processing the prior chemical shift information with filtering operation, an initial spin density profile of water/fat and a weighting matrix for water/fat residual artifacts suppression were obtained to guide the reconstruction process. A l1 norm minimization problem with regularization was exploited to reconstruct separated water/fat images with high spatial resolution and less residual/aliasing artifacts. Numeric simulation and experiments on water-oil phantom and rat abdomen/neck imaging demonstrated the effectiveness and robustness of this new method. The SWAF method proposed herein would promote the application of SPEN MRI in the cases where water/fat separation is required.
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Affiliation(s)
- Jianpan Huang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China; Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Kannie W Y Chan
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Congbo Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
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6
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Karlsson A, Peolsson A, Elliott J, Romu T, Ljunggren H, Borga M, Dahlqvist Leinhard O. The relation between local and distal muscle fat infiltration in chronic whiplash using magnetic resonance imaging. PLoS One 2019; 14:e0226037. [PMID: 31805136 PMCID: PMC6894804 DOI: 10.1371/journal.pone.0226037] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/17/2019] [Indexed: 12/13/2022] Open
Abstract
The objective of this study was to investigate the relationship between fat infiltration in the cervical multifidi and fat infiltration measured in the lower extremities to move further into understanding the complex signs and symptoms arising from a whiplash trauma. Thirty-one individuals with chronic whiplash associated disorders, stratified into a mild/moderate group and a severe group, together with 31 age- and gender matched controls were enrolled in this study. Magnetic resonance imaging was used to acquire a 3D volume of the neck and of the whole-body. Cervical multifidi was used to represent muscles local to the whiplash trauma and all muscles below the hip joint, the lower extremities, were representing widespread muscles distal to the site of the trauma. The fat infiltration was determined by fat fraction in the segmented images. There was a linear correlation between local and distal muscle fat infiltration (p<0.001, r2 = 0.28). The correlation remained significant when adjusting for age and WAD group (p = 0.009) as well as when correcting for age, WAD group and BMI (p = 0.002). There was a correlation between local and distal muscle fat infiltration within the severe WAD group (p = 0.0016, r2 = 0.69) and in the healthy group (p = 0.022, r2 = 0.17) but not in the mild/moderate group (p = 0.29, r2 = 0.06). No significant differences (p = 0.11) in the lower extremities’ MFI between the different groups were found. The absence of differences between the groups in terms of lower extremities’ muscle fat infiltration indicates that, in this particular population, the whiplash trauma has a local effect on muscle fat infiltration rather than a generalized.
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Affiliation(s)
- Anette Karlsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- * E-mail:
| | - Anneli Peolsson
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- Department of Medical and Health Sciences, Physiotherapy, Linköping University, Linköping, Sweden
| | - James Elliott
- Faculty of Health Sciences, The University of Sydney, Northern Sydney Local Health District, The Kolling Institute, St Leonards, NSW, Australia
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - Thobias Romu
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Helena Ljunggren
- Department of Medical and Health Sciences, Physiotherapy, Linköping University, Linköping, Sweden
| | - Magnus Borga
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
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7
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Guo Y, Liu Z, Wen Y, Spincemaille P, Zhang H, Jafari R, Zhang S, Eskreis-Winkler S, Gillen KM, Yi P, Feng Q, Feng Y, Wang Y. Quantitative susceptibility mapping of the spine using in-phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat-water separation. NMR IN BIOMEDICINE 2019; 32:e4156. [PMID: 31424131 DOI: 10.1002/nbm.4156] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 06/10/2023]
Abstract
Quantitative susceptibility mapping (QSM) of human spinal vertebrae from a multi-echo gradient-echo (GRE) sequence is challenging, because comparable amounts of fat and water in the vertebrae make it difficult to solve the nonconvex optimization problem of fat-water separation (R2*-IDEAL) for estimating the magnetic field induced by tissue susceptibility. We present an in-phase (IP) echo initialization of R2*-IDEAL for QSM in the spinal vertebrae. Ten healthy human subjects were recruited for spine MRI. A 3D multi-echo GRE sequence was implemented to acquire out-phase and IP echoes. For the IP method, the R2* and field maps estimated by separately fitting the magnitude and phase of IP echoes were used to initialize gradient search R2*-IDEAL to obtain final R2*, field, water, and fat maps, and the final field map was used to generate QSM. The IP method was compared with the existing Zero method (initializing the field to zero), VARPRO-GC (variable projection using graphcuts but still initializing the field to zero), and SPURS (simultaneous phase unwrapping and removal of chemical shift using graphcuts for initialization) on both simulation and in vivo data. The single peak fat model was also compared with the multi-peak fat model. There was no substantial difference on QSM between the single peak and multi-peak fat models, but there were marked differences among different initialization methods. The simulations demonstrated that IP provided the lowest error in the field map. Compared to Zero, VARPRO-GC and SPURS, the proposed IP method provided substantially improved spine QSM in all 10 subjects.
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Affiliation(s)
- Yihao Guo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Zhe Liu
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Yan Wen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Honglei Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Ramin Jafari
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Shun Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Sarah Eskreis-Winkler
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Kelly M Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Peiwei Yi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
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8
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Forsgren MF, Karlsson M, Dahlqvist Leinhard O, Dahlström N, Norén B, Romu T, Ignatova S, Ekstedt M, Kechagias S, Lundberg P, Cedersund G. Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort. PLoS Comput Biol 2019; 15:e1007157. [PMID: 31237870 PMCID: PMC6613709 DOI: 10.1371/journal.pcbi.1007157] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 07/08/2019] [Accepted: 06/06/2019] [Indexed: 12/11/2022] Open
Abstract
Estimation of liver function is important to monitor progression of chronic liver disease (CLD). A promising method is magnetic resonance imaging (MRI) combined with gadoxetate, a liver-specific contrast agent. For this method, we have previously developed a model for an average healthy human. Herein, we extended this model, by combining it with a patient-specific non-linear mixed-effects modeling framework. We validated the model by recruiting 100 patients with CLD of varying severity and etiologies. The model explained all MRI data and adequately predicted both timepoints saved for validation and gadoxetate concentrations in both plasma and biopsies. The validated model provides a new and deeper look into how the mechanisms of liver function vary across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate. These mechanisms are shared across many liver functions and can now be estimated from standard clinical images. Being able to accurately and reliably estimate liver function is important when monitoring the progression of patients with liver disease, as well as when identifying drug-induced liver injury during drug development. A promising method for quantifying liver function is to use magnetic resonance imaging combined with gadoxetate. Gadoxetate is a liver-specific contrast agent, which is taken up by the hepatocytes and excreted into the bile. We have previously developed a mechanistic model for gadoxetate dynamics using averaged data from healthy volunteers. In this work, we extended our model with a non-linear mixed-effects modeling framework to give patient-specific estimates of the gadoxetate transport-rates. We validated the model by recruiting 100 patients with liver disease, covering a range of severity and etiologies. All patients underwent an MRI-examination and provided both blood and liver biopsies. Our validated model provides a new and deeper look into how the mechanisms of liver function varies across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate.
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Affiliation(s)
- Mikael F. Forsgren
- Wolfram MathCore AB and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Markus Karlsson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Nils Dahlström
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Radiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Bengt Norén
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Thobias Romu
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Simone Ignatova
- Department of Clinical Pathology and Clinical Genetics, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Mattias Ekstedt
- Department of Gastroenterology and Hepatology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Stergios Kechagias
- Department of Gastroenterology and Hepatology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Radiation Physics, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
- * E-mail: (PL); (GC)
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- * E-mail: (PL); (GC)
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9
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Elliott JM, Cornwall J, Kennedy E, Abbott R, Crawford RJ. Towards defining muscular regions of interest from axial magnetic resonance imaging with anatomical cross-reference: part II - cervical spine musculature. BMC Musculoskelet Disord 2018; 19:171. [PMID: 29807530 PMCID: PMC5972401 DOI: 10.1186/s12891-018-2074-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 05/04/2018] [Indexed: 01/15/2023] Open
Abstract
Background It has been suggested that the quantification of paravertebral muscle composition and morphology (e.g. size/shape/structure) with magnetic resonance imaging (MRI) has diagnostic, prognostic, and therapeutic potential in contributing to overall musculoskeletal health. If this is to be realised, then consensus towards standardised MRI methods for measuring muscular size/shape/structure are crucial to allow the translation of such measurements towards management of, and hopefully improved health for, those with some musculoskeletal conditions. Following on from an original paper detailing methods for measuring muscles traversing the lumbar spine, we propose new methods based on anatomical cross-reference that strive towards standardising MRI-based quantification of anterior and posterior cervical spine muscle composition. Methods In this descriptive technical advance paper we expand our methods from the lumbar spine by providing a detailed examination of regional cervical spine muscle morphology, followed by a comprehensive description of the proposed technique defining muscle ROI from axial MRI. Cross-referencing cervical musculature and vertebral anatomy includes an innovative comparison between axial E12 sheet-plastinates derived from cadaveric material to a series of axial MRIs detailing commonly used sequences. These images are shown at different cervical levels to illustrate differences in regional morphology. The method for defining ROI for both anterior (scalenes group, sternocleidomastoid, longus colli, longus capitis) and posterior (multifidus, semispinalis cervicis, semispinalis capitis, splenius capitis) cervical muscles is then described and discussed in relation to existing literature. Results A series of steps towards standardising the quantification of cervical spine muscle quality are described, with concentration on the measurement of muscle volume and fatty infiltration (MFI). We offer recommendations for imaging parameters that should additionally inform a priori decisions when planning investigations of cervical muscle tissues with MRI. Conclusions The proposed method provides an option rather than a final position for quantifying cervical spine muscle composition and morphology using MRI. We intend to stimulate discussion towards establishing measurement consensus whereby data-pooling and meaningful comparisons between imaging studies (primarily MRI) investigating cervical muscle quality becomes available and the norm.
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Affiliation(s)
- James M Elliott
- Faculty of Health Sciences, The University of Sydney, Northern Sydney Local Health District, St Leonards, Australia 75 East Street Lidcombe NSW, Sydney, 2141, Australia. .,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, USA. .,Honorary Fellow School of Health and Rehabilitation Sciences, The University of Queensland, St. Lucia, Australia.
| | - Jon Cornwall
- Centre for Early Learning in Medicine, Otago Medical School, University of Otago, Dunedin, New Zealand
| | - Ewan Kennedy
- School of Physiotherapy, University of Otago, Dunedin, New Zealand
| | - Rebecca Abbott
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, USA
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10
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Abbott R, Peolsson A, West J, Elliott JM, Åslund U, Karlsson A, Leinhard OD. The qualitative grading of muscle fat infiltration in whiplash using fat and water magnetic resonance imaging. Spine J 2018; 18:717-725. [PMID: 28887274 PMCID: PMC8845185 DOI: 10.1016/j.spinee.2017.08.233] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/30/2017] [Accepted: 08/09/2017] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT The development of muscle fat infiltration (MFI) in the neck muscles is associated with poor functional recovery following whiplash injury. Custom software and time-consuming manual segmentation of magnetic resonance imaging (MRI) is required for quantitative analysis and presents as a barrier for clinical translation. PURPOSE The purpose of this work was to establish a qualitative MRI measure for MFI and evaluate its ability to differentiate between individuals with severe whiplash-associated disorder (WAD), mild or moderate WAD, and healthy controls. STUDY DESIGN/SETTING This is a cross-sectional study. PATIENT SAMPLE Thirty-one subjects with WAD and 31 age- and sex-matched controls were recruited from an ongoing randomized controlled trial. OUTCOME MEASURES The cervical multifidus was visually identified and segmented into eighths in the axial fat/water images (C4-C7). Muscle fat infiltration was assessed on a visual scale: 0 for no or marginal MFI, 1 for light MFI, and 2 for distinct MFI. The participants with WAD were divided in two groups: mild or moderate and severe based on Neck Disability Index % scores. METHODS The mean regional MFI was compared between the healthy controls and each of the WAD groups using the Mann-Whitney U test. Receiver operator characteristic (ROC) analyses were carried out to evaluate the validity of the qualitative method. RESULTS Twenty (65%) patients had mild or moderate disability and 11 (35%) were considered severe. Inter- and intra-rater reliability was excellent when grading was averaged by level or when frequency of grade II was considered. Statistically significant differences (p<.05) in regional MFI were particularly notable between the severe WAD group and healthy controls. The ROC curve, based on detection of distinct MFI, showed an area-under-the curve of 0.768 (95% confidence interval 0.59-0.94) for discrimination of WAD participants. CONCLUSIONS These preliminary results suggest a qualitative MRI measure for MFI is reliable and valid, and may prove useful toward the classification of WAD in radiology practice.
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Affiliation(s)
- Rebecca Abbott
- Department of Physical Therapy and Human Movement Sciences, NU-PTHMS, Feinberg School of Medicine, Northwestern University, 645 North Michigan Ave, Suite 1100, Chicago, IL, 60611 USA
| | - Anneli Peolsson
- Department of Medical and Health Sciences, Physiotherapy, IMH kansli, Sandbacksgatan 7, 3 tr, Campus US, Linköping University, 58183 Linköping, Sweden
| | - Janne West
- Department of Medical and Health Sciences and Center for Medical Image Science and Visualization (CMIV)/ Division of Radiological Services, IMH, Linköping University, SE-581 85 Linköping, Sweden
| | - James M. Elliott
- Department of Physical Therapy and Human Movement Sciences, NU-PTHMS, Feinberg School of Medicine, Northwestern University, 645 North Michigan Ave, Suite 1100, Chicago, IL, 60611 USA,School of Health and Rehabilitation Sciences, The University of Queensland, Australia,Zurich University of Applied Sciences, Gertrudstrasse 15, 8400 Winterthur, Switzerland
| | - Ulrika Åslund
- Department of Medical and Health Sciences, Physiotherapy, IMH kansli, Sandbacksgatan 7, 3 tr, Campus US, Linköping University, 58183 Linköping, Sweden
| | - Anette Karlsson
- Center for Medical Image Science and Visualization (CMIV)/ Department of Biomedical Engineering, Linköping University, SE 58183 Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- Department of Medical and Health Sciences, and Center for Medical Image Science and Visualization (CMIV)/Division of Radiological Sciences, IMH, Linköping University, SE-581 85 Linköping, Sweden.
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11
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Morales Drissi N, Romu T, Landtblom AM, Szakács A, Hallböök T, Darin N, Borga M, Leinhard OD, Engström M. Unexpected Fat Distribution in Adolescents With Narcolepsy. Front Endocrinol (Lausanne) 2018; 9:728. [PMID: 30574118 PMCID: PMC6292486 DOI: 10.3389/fendo.2018.00728] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 11/16/2018] [Indexed: 02/02/2023] Open
Abstract
Narcolepsy type 1 is a chronic sleep disorder with significantly higher BMI reported in more than 50% of adolescent patients, putting them at a higher risk for metabolic syndrome in adulthood. Although well-documented, the body fat distribution and mechanisms behind weight gain in narcolepsy are still not fully understood but may be related to the loss of orexin associated with the disease. Orexin has been linked to the regulation of brown adipose tissue (BAT), a metabolically active fat involved in energy homeostasis. Previous studies have used BMI and waist circumference to characterize adipose tissue increases in narcolepsy but none have investigated its specific distribution. Here, we examine adipose tissue distribution in 19 adolescent patients with narcolepsy type 1 and compare them to 17 of their healthy peers using full body magnetic resonance imaging (MRI). In line with previous findings we saw that the narcolepsy patients had more overall fat than the healthy controls, but contrary to our expectations there were no group differences in supraclavicular BAT, suggesting that orexin may have no effect at all on BAT, at least under thermoneutral conditions. Also, in line with previous reports, we observed that patients had more total abdominal adipose tissue (TAAT), however, we found that they had a lower ratio between visceral adipose tissue (VAT) and TAAT indicating a relative increase of subcutaneous abdominal adipose tissue (ASAT). This relationship between VAT and ASAT has been associated with a lower risk for metabolic disease. We conclude that while weight gain in adolescents with narcolepsy matches that of central obesity, the lower VAT ratio may suggest a lower risk of developing metabolic disease.
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Affiliation(s)
- Natasha Morales Drissi
- Department of Medical and Health Sciences (IMH), Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Thobias Romu
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
| | - Anne-Marie Landtblom
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- Department of Clinical and Experimental Medicine (IKE), Linköping University, Linköping, Sweden
- Department of Neuroscience, Uppsala University, Uppsala, Sweden
| | - Attilla Szakács
- Department of Pediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Tove Hallböök
- Department of Pediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Niklas Darin
- Department of Pediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Magnus Borga
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
- Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- Department of Medical and Health Sciences (IMH), Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
| | - Maria Engström
- Department of Medical and Health Sciences (IMH), Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- *Correspondence: Maria Engström
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12
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Middleton MS, Haufe W, Hooker J, Borga M, Dahlqvist Leinhard O, Romu T, Tunón P, Hamilton G, Wolfson T, Gamst A, Loomba R, Sirlin CB. Quantifying Abdominal Adipose Tissue and Thigh Muscle Volume and Hepatic Proton Density Fat Fraction: Repeatability and Accuracy of an MR Imaging-based, Semiautomated Analysis Method. Radiology 2017; 283:438-449. [PMID: 28278002 PMCID: PMC5410959 DOI: 10.1148/radiol.2017160606] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Purpose To determine the repeatability and accuracy of a commercially available magnetic resonance (MR) imaging-based, semiautomated method to quantify abdominal adipose tissue and thigh muscle volume and hepatic proton density fat fraction (PDFF). Materials and Methods This prospective study was institutional review board- approved and HIPAA compliant. All subjects provided written informed consent. Inclusion criteria were age of 18 years or older and willingness to participate. The exclusion criterion was contraindication to MR imaging. Three-dimensional T1-weighted dual-echo body-coil images were acquired three times. Source images were reconstructed to generate water and calibrated fat images. Abdominal adipose tissue and thigh muscle were segmented, and their volumes were estimated by using a semiautomated method and, as a reference standard, a manual method. Hepatic PDFF was estimated by using a confounder-corrected chemical shift-encoded MR imaging method with hybrid complex-magnitude reconstruction and, as a reference standard, MR spectroscopy. Tissue volume and hepatic PDFF intra- and interexamination repeatability were assessed by using intraclass correlation and coefficient of variation analysis. Tissue volume and hepatic PDFF accuracy were assessed by means of linear regression with the respective reference standards. Results Adipose and thigh muscle tissue volumes of 20 subjects (18 women; age range, 25-76 years; body mass index range, 19.3-43.9 kg/m2) were estimated by using the semiautomated method. Intra- and interexamination intraclass correlation coefficients were 0.996-0.998 and coefficients of variation were 1.5%-3.6%. For hepatic MR imaging PDFF, intra- and interexamination intraclass correlation coefficients were greater than or equal to 0.994 and coefficients of variation were less than or equal to 7.3%. In the regression analyses of manual versus semiautomated volume and spectroscopy versus MR imaging, PDFF slopes and intercepts were close to the identity line, and correlations of determination at multivariate analysis (R2) ranged from 0.744 to 0.994. Conclusion This MR imaging-based, semiautomated method provides high repeatability and accuracy for estimating abdominal adipose tissue and thigh muscle volumes and hepatic PDFF. © RSNA, 2017.
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Affiliation(s)
- Michael S. Middleton
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - William Haufe
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Jonathan Hooker
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Magnus Borga
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Thobias Romu
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Patrik Tunón
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Gavin Hamilton
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Tanya Wolfson
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Anthony Gamst
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Rohit Loomba
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
| | - Claude B. Sirlin
- From the Liver Imaging Group, Department of Radiology (M.S.M., W.H., J.H., G.H., C.B.S.), Computational and Applied Statistics Laboratory, San Diego Supercomputing Center (T.W., A.G.), and Department of Medicine, Division of Gastroenterology and Hepatology (R.L.), University of California, San Diego, 9500 Gilman Dr, MC 0888, San Diego, CA 92093-0888; Advanced MR Analytics AB, Linköping, Sweden (M.B., O.D.L., T.R., P.T.); and Center for Medical Image Science and Visualization (M.B., O.D.L., T.R.), Department of Biomedical Engineering (M.B., T.R.), and Department of Medicine and Health (O.D.L.), Linköping University, Linköping, Sweden
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