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  • 1.
    Agebratt, Christian
    et al.
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Ström, Edvin
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    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.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Leandersson, Per
    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, Occupational and Environmental Medicine Center.
    Nyström, Fredrik H.
    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 Endocrinology.
    A Randomized Study of the Effects of Additional Fruit and Nuts Consumption on Hepatic Fat Content, Cardiovascular Risk Factors and Basal Metabolic Rate2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 1, p. e0147149-Article in journal (Refereed)
    Abstract [en]

    Background

    Fruit has since long been advocated as a healthy source of many nutrients, however, the high content of sugars in fruit might be a concern.

    Objectives

    To study effects of an increased fruit intake compared with similar amount of extra calories from nuts in humans.

    Methods

    Thirty healthy non-obese participants were randomized to either supplement the diet with fruits or nuts, each at +7 kcal/kg bodyweight/day for two months. Major endpoints were change of hepatic fat content (HFC, by magnetic resonance imaging, MRI), basal metabolic rate (BMR, with indirect calorimetry) and cardiovascular risk markers.

    Results

    Weight gain was numerically similar in both groups although only statistically significant in the group randomized to nuts (fruit: from 22.15±1.61 kg/m2 to 22.30±1.7 kg/m2, p = 0.24 nuts: from 22.54±2.26 kg/m2 to 22.73±2.28 kg/m2, p = 0.045). On the other hand BMR increased in the nut group only (p = 0.028). Only the nut group reported a net increase of calories (from 2519±721 kcal/day to 2763±595 kcal/day, p = 0.035) according to 3-day food registrations. Despite an almost three-fold reported increased fructose-intake in the fruit group (from 9.1±6.0 gram/day to 25.6±9.6 gram/day, p<0.0001, nuts: from 12.4±5.7 gram/day to 6.5±5.3 gram/day, p = 0.007) there was no change of HFC. The numerical increase in fasting insulin was statistical significant only in the fruit group (from 7.73±3.1 pmol/l to 8.81±2.9 pmol/l, p = 0.018, nuts: from 7.29±2.9 pmol/l to 8.62±3.0 pmol/l, p = 0.14). Levels of vitamin C increased in both groups while α-tocopherol/cholesterol-ratio increased only in the fruit group.

    Conclusions

    Although BMR increased in the nut-group only this was not linked with differences in weight gain between groups which potentially could be explained by the lack of reported net caloric increase in the fruit group. In healthy non-obese individuals an increased fruit intake seems safe from cardiovascular risk perspective, including measurement of HFC by MRI.

  • 2.
    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.

  • 3.
    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)
  • 4.
    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.

  • 5.
    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).

  • 6.
    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, IK.
    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, 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. 

  • 7.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    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.
    Karlsson, Anette
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    High resolution isotropic whole-­‐body symmetrically sampled two point Dixon acquisition imaging at 3T2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Other academic)
  • 8.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    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.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Gjellan, Solveig
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Zanjani, Sepehr
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Endocrinology.
    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.
    Validation of whole-­‐body adipose tissue quantification using air displacement plethysmometry2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Other academic)
  • 9.
    Haufe, William
    et al.
    Department of Radiology, University of California, San Diego, San Diego, CA, United states.
    Hooker, Jonathan
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Schlein, Alexandra
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Szeverenyi, Nikolaus
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Advanced MR Analytics AB, Linköping, Sweden.
    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.
    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). Advanced MR Analytics AB, Linköping, Sweden.
    Tunón, Patrik
    Advanced MR Analytics AB, Linköping, Sweden.
    Horgan, Santiago
    Surgery, University of California, San Diego, San Diego, CA, United States.
    Jacobsen, Garth
    Surgery, University of California, San Diego, San Diego, CA, United States.
    Schwimmer, Jeffrey B
    University of California, San Diego, San Diego, CA, United States.
    Reeder, Scott B
    University of Wisconsin, Madison, Madison, WI, United States.
    Sirlin, Claude B.
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Feasibility of an automated tissue segmentation technique in a longitudinal weight loss study2016Conference paper (Other academic)
    Abstract [en]

    To address the problems inherent in manual methods, a novel, semi-automated tissue segmentation image analysis technique has been developed. The purpose of this study was to demonstrate the feasibility and describe preliminary observations of applying this technique to quantify and monitor longitudinal changes in abdominal adipose tissue and thigh muscle volume in obese adults during weight loss. Abdominal adipose tissue and thigh muscle volume decreased during weight loss. As a proportion of body weight, adipose tissue volumes decreased during weight loss. By comparison, as a proportion of body weight, thigh muscle volume increased.

  • 10.
    Karlsson, Anette
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    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.
    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.
    Automated Whole Body Muscle Segmentation & Classification2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Other academic)
  • 11.
    Karlsson, Anette
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Vallin, Anna
    Linköping University, Center for Medical Image Science and Visualization, CMIV.
    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.
    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.
    Automated Whole Body Muscle Quantification Based on a 10 min MR-Exam2012In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), 2012Conference paper (Other academic)
  • 12.
    Karlsson, Anette
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Rosander, Johannes
    Advanced MR Analytics AB, Linköping, Sweden.
    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).
    Tallberg, Joakim
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Grönqvist, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water–fat MRI2015In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 41, no 6, p. 1558-1569Article in journal (Refereed)
    Abstract [en]

    Purpose

    To develop and demonstrate a rapid whole-body magnetic resonance imaging (MRI) method for automatic quantification of total and regional skeletal muscle volume.

    Materials and Methods

    The method was based on a multi-atlas segmentation of intensity corrected water–fat separated image volumes. Automatic lean muscle tissue segmentations were achieved by nonrigid registration of atlas datasets with 10 different manually segmented muscle groups. Ten subjects scanned at 1.5 T and 3.0 T were used as atlases, initial validation and optimization. Further validation used 11 subjects scanned at 3.0 T. The automated and manual segmentations were compared using intraclass correlation, true positive volume fractions, and delta volumes.

    Results

    For the 1.5 T datasets, the intraclass correlation, true positive volume fractions (mean ± standard deviation, SD), and delta volumes (mean ± SD) were 0.99, 0.91 ± 0.02, −0.10 ± 0.70L (whole body), 0.99, 0.93 ± 0.02, 0.01 ± 0.07L (left anterior thigh), and 0.98, 0.80 ± 0.07, −0.08 ± 0.15L (left abdomen). The corresponding values at 3.0 T were 0.97, 0.92 ± 0.03, −0.17 ± 1.37L (whole body), 0.99, 0.93 ± 0.03, 0.03 ± 0.08L (left anterior thigh), and 0.89, 0.90 ± 0.04, −0.03 ± 0.42L (left abdomen). The validation datasets showed similar results.

    Conclusion

    The method accurately quantified the whole-body skeletal muscle volume and the volume of separate muscle groups independent of field strength and image resolution. 

  • 13.
    Karlsson, Anette
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Rosander, Johannes
    Advanced MR Analytics AB, Linköping, Sweden.
    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).
    Tallberg, Joakim
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Grönqvist, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Automatic and Quantitative Assessment of Total and Regional Muscle Tissue Volume using Multi-Atlas Segmentation2014Conference paper (Other academic)
    Abstract [en]

    Accurate and precise assessment of human muscle tissue is important for further understanding of different muscle diseases and syndromes. We present a rapid whole body MR method for automatic quantification of total and regional muscle volume. The method is based on multi-atlas segmentation of intensity corrected water-fat separated images. The method was validated with a leave-one-out approach, using manually segmented atlases from 10 subjects as ground truth. The result gave a coefficient of variation on total muscle volume equal to 1.25±1.35 % (mean ± standard deviation). The method enables cost-efficient large-scale studies, investigating conditions such as sarcopenia and muscular dystrophies.

  • 14.
    Karlsson, Anette
    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.
    Rosander, Johannes
    Tallberg, Joakim
    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.
    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, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Whole Body Muscle Classification using Multiple Prototype Voting2013Conference paper (Other academic)
  • 15.
    Lidell, Martin E.
    et al.
    Medicinsk genetik, Göteborgs universitet.
    Betz, Matthias J.
    Medicinsk genetik, Göteborgs universitet.
    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.
    Heglind, Mikael
    Medicinsk genetik, Göteborgs universitet.
    Elander, Louise
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Slawik, Marc
    Klinikum der Ludwig Maximilians University (LMU), Munich, Germany.
    Mussack, Thomas
    Klinikum der LMU, Munich, Germany.
    Nilsson, Daniel
    Medicinsk genetik, Göteborgs universitet.
    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.
    Nuutila, Pirjo
    University of Turku, Turku, Finland.
    Virtanen, Kirsi A.
    University of Turku, Turku, Finland.
    Beuschlein, Felix
    Klinikum der Ludwig Maximilians University (LMU), Munich, Germany.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Enerbäck, Sven
    Medicinsk genetik, Göteborgs universitet.
    Evidence for two types of brown adipose tissue in humans2013In: Nature Medicine, ISSN 1078-8956, E-ISSN 1546-170X, Vol. 19, no 5, p. 631-634Article in journal (Refereed)
    Abstract [en]

    The previously observed supraclavicular depot of brown adipose tissue (BAT) in adult humans was commonly believed to be the equivalent of the interscapular thermogenic organ of small mammals. This view was recently disputed on the basis of the demonstration that this depot consists of beige (also called brite) brown adipocytes, a newly identified type of brown adipocyte that is distinct from the classical brown adipocytes that make up the interscapular thermogenic organs of other mammals. A combination of high-resolution imaging techniques and histological and biochemical analyses showed evidence for an anatomically distinguishable interscapular BAT (iBAT) depot in human infants that consists of classical brown adipocytes, a cell type that has so far not been shown to exist in humans. On the basis of these findings, we conclude that infants, similarly to rodents, have the bona fide iBAT thermogenic organ consisting of classical brown adipocytes that is essential for the survival of small mammals in a cold environment.

  • 16.
    Middleton, Michael
    et al.
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Haufe, William
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Hooker, Jonathan
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Advanced MR Analytics AB, Linköping, Sweden.
    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. Advanced MR Analytics AB, Linköping, Sweden.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Advanced MR Analytics AB, Linköping, Sweden.
    Tunón, Patrik
    Advanced MR Analytics AB, Linköping, Sweden.
    Szeverenyi, Nick
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Hamilton, Gavin
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Wolfson, Tanya
    Department of Radiology, University of California, San Diego, San Diego, CA, United States; Computational and Applied Statistics Laboratory (CASL), University of California, San Diego, San Diego, CA, United States.
    Gamst, Anthony
    Department of Radiology, University of California, San Diego, San Diego, CA, United States; Computational and Applied Statistics Laboratory (CASL), University of California, San Diego, San Diego, CA, United States.
    Loomba, Rohit
    Department of Medicine, University of California, San Diego, San Diego, CA, United States.
    Sirlin, Claude B.
    Department of Radiology, University of California, San Diego, San Diego, CA, United States.
    Repeatability and accuracy of a novel, MRI-based, semi-automated analysis method for quantifying abdominal adipose tissue and thigh muscle volumes2016Conference paper (Other academic)
    Abstract [en]

    Current MRI methods to estimate body tissue compartment volumes rely on manual segmentation, which is laborious, expensive, not widely available outside specialized centers, and not standardized. To address these concerns, a novel, semi-automated image analysis method has been developed. Image acquisition takes about six minutes, and uses widely available MRI pulse sequences. We found that this method permits comprehensive body compartment analysis and provides high repeatability and accuracy. Current and future clinical and drug development studies may benefit from this methodology, as may clinical settings where monitoring change in these measures is desired.

  • 17.
    Moreno, Rodrigo
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    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.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    de Muinck, Ebo
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Effects of denoising in the estimation of T2* from images acquired through Dixon imaging2013Conference paper (Other academic)
  • 18.
    Newman, David
    et al.
    Department of Radiology, Norfolk & Norwich University Hospital, UK.
    Kelly-Morland, Christian
    Department of Radiology, Norfolk & Norwich University Hospital, UK.
    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.
    Kasmai, Bahman
    Department of Radiology, Norfolk & Norwich University Hospital, UK.
    Greenwood, Richard
    Department of Radiology, Norfolk & Norwich University Hospital, UK.
    Malcolm, Paul
    Department of Radiology, Norfolk & Norwich University Hospital, UK.
    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).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Toms, Andoni
    Department of Radiology, Norfolk & Norwich University Hospital, UK.
    Test–retest reliability of rapid whole body and compartmental fat volume quantification on a widebore 3T MR system in normal-weight, overweight, and obese subjects2016In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 44, no 6, p. 1464-1473Article in journal (Refereed)
    Abstract [en]

    Purpose

    To measure the test–retest reliability of rapid (<15 min) whole body and visceral fat volume quantification in normal and obese subjects on a widebore 3T MR system and compare it with conventional manual segmentation.

    Materials and Methods

    Thirty participants (body mass index [BMI] 20.1–48.6 kg/m2) underwent two whole-body magnetic resonance imaging (MRI) examinations on a widebore 3T machine using a 2-point Dixon technique. Phase sensitive reconstruction and intensity inhomogeneity correction produced quantitative datasets of total adipose tissue (TAT), abdominal subcutaneous adipose tissue (ASAT), and visceral adipose tissue (VAT). The quantification was performed automatically using nonrigid atlas-based segmentation and compared with manual segmentation (SliceOmatic).

    Results

    The mean TAT was 31.74 L with a coefficient of variation (CV) of 0.79% and a coefficient of repeatability (CR) of 0.49 L. The ASAT was 7.92 L with a CV of 2.98% and a CR of 0.46 L. There was no significant difference in the semiautomated and manually segmented VAT (P = 0.73) but there were differences in the reliability of the two techniques. The mean semiautomated VAT was 2.56 L, CV 1.8%, and CR 0.09 L compared to the mean manually segmented VAT of 3.12 L, where the CV was 6.3% and the CR was 0.39 L.

    Conclusion

    Rapid semiautomated whole body and compartmental fat volume quantification can be derived from a widebore 3T system, for a range of body sizes including obese patients, with “almost perfect” test–retest reliability.

  • 19.
    Norén, Bengt
    et al.
    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.
    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.
    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.
    Dahlström, Nils
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    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.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Gastroenterology and Hepatology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Prospective Evaluation of a Novel Quantification Method for the Discrimination of Mild and Severe Hepatic Fibrosis Using Gd-EOB-DTPA2012Conference paper (Other academic)
  • 20.
    Norén, Bengt
    et al.
    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.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Dahlström, Nils
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    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.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Gastroenterology and Hepatology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Separation of advanced from mild hepatic fibrosis by quantification of the hepatobiliary uptake of Gd-EOB-DTPA2012Conference paper (Other academic)
  • 21.
    Norén, Bengt
    et al.
    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.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Dahlström, Nils
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    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.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Quantification of the hepatobiliary uptake of Gd-EOB-DTPA can separate advanced from mild fibrosis2012Conference paper (Other academic)
  • 22.
    Norén, Bengt
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Forsgren, Mikael Fredrik
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Dahlström, Nils
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    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.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Gastroentorology.
    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 Gastroentorology.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Separation of advanced from mild hepatic fibrosis by quantification of the hepatobiliary uptake of Gd-EOB-DTPA2013In: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 23, no 1, p. 174-181Article in journal (Refereed)
    Abstract [en]

    Objectives

    To apply dynamic contrast-enhanced (DCE) MRI on patients presenting with elevated liver enzymes without clinical signs of hepatic decompensation in order to quantitatively compare the hepatocyte-specific uptake of Gd-EOB-DTPA with histopathological fibrosis stage.

    Methods

    A total of 38 patients were prospectively examined using 1.5-T MRI. Data were acquired from regions of interest in the liver and spleen by using time series of single-breath-hold symmetrically sampled two-point Dixon 3D images (non-enhanced, arterial and venous portal phase; 3, 10, 20 and 30 min) following a bolus injection of Gd-EOB-DTPA (0.025 mmol/kg). The signal intensity (SI) values were reconstructed using a phase-sensitive technique and normalised using multiscale adaptive normalising averaging (MANA). Liver-to-spleen contrast ratios (LSC_N) and the contrast uptake rate (KHep) were calculated. Liver biopsy was performed and classified according to the Batts and Ludwig system.

    Results

    Area under the receiver-operating characteristic curve (AUROC) values of 0.71, 0.80 and 0.78, respectively, were found for KHep, LSC_N10 and LSC_N20 with regard to severe versus mild fibrosis. Significant group differences were found for KHep (borderline), LSC_N10 and LSC_N20.

    Conclusions

    Liver fibrosis stage strongly influences the hepatocyte-specific uptake of Gd-EOB-DTPA. Potentially the normalisation technique and KHep will reduce patient and system bias, yielding a robust approach to non-invasive liver function determination.

  • 23.
    Patra, Hirak Kumar
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge, UK; Wolfson College, University of Cambridge, Cambridge, UK.
    Azharuddin, Mohammad
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Chemistry. Linköping University, Faculty of Medicine and Health Sciences.
    Islam, Mohammad Mirazul
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Massachusetts Eye and Ear and Schepens Eye Research Institute, Department of Ophthalmology, Harvard Medical School, Boston, USA.
    Papapavlou, Georgia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Deb, Suryyani
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Department of Biochemistry, University of Calcutta, Calcutta, India; Department of Biotechnology, Maulana Abul Kalam Azad University of Technology (MAKAUT), West Bengal, India.
    Osterrieth, Johannes
    Department of Chemical Engineering and Biotechnology, Cambridge University, Philippa Fawcett Drive, Cambridge, UK.
    Zhu, Geyunjian Harry
    Department of Chemical Engineering and Biotechnology, Cambridge University, Philippa Fawcett Drive, Cambridge, UK.
    Romu, Thobias
    Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dhara, Ashis K.
    Centre for Image Analysis, Uppsala University, Uppsala, Sweden; Department of Electrical Engineering, National Institute of Technology Durgapur, West Bengal, India.
    Jafari, Mohammad Javad
    Linköping University, Department of Physics, Chemistry and Biology, Molecular Physics. Linköping University, Faculty of Science & Engineering.
    Gadheri, Amineh
    Department of Oncology‐Pathology, Karolinska Institute, Stockholm, Sweden.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Hematopoiesis and Developmental Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Rajan, Madhavan S.
    Department of Ophthalmology, Cambridge University Hospitals NHS Trust and Vision and Eye Research Institute (VERI), Anglia Ruskin University, Cambridge, UK.
    Slater, Nigel K. H.
    Department of Chemical Engineering and Biotechnology, Cambridge University, Philippa Fawcett Drive, Cambridge, UK.
    Rational Nanotoolbox with Theranostic Potential for Medicated Pro-Regenerative Corneal Implants2019In: Advanced Functional Materials, ISSN 1616-301X, E-ISSN 1616-3028, article id 1903760Article in journal (Refereed)
    Abstract [en]

    Cornea diseases are a leading cause of blindness and the disease burden is exacerbated by the increasing shortage around the world for cadaveric donor corneas. Despite the advances in the field of regenerative medicine, successful transplantation of laboratory‐made artificial corneas is not fully realized in clinical practice. The causes of failure of such artificial corneal implants are multifactorial and include latent infections from viruses and other microbes, enzyme overexpression, implant degradation, extrusion or delayed epithelial regeneration. Therefore, there is an urgent unmet need for developing customized corneal implants to suit the host environment and counter the effects of inflammation or infection, which are able to track early signs of implant failure in situ. This work reports a nanotoolbox comprising tools for protection from infection, promotion of regeneration, and noninvasive monitoring of the in situ corneal environment. These nanosystems can be incorporated within pro‐regenerative biosynthetic implants, transforming them into theranostic devices, which are able to respond to biological changes following implantation.

    The full text will be freely available from 2020-07-15 00:01
  • 24.
    Patra, Hirak Kumar
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Biosensors and Bioelectronics. Linköping University, The Institute of Technology.
    Khaliq, Nisar Ul
    Linköping University, Department of Physics, Chemistry and Biology, Biosensors and Bioelectronics. 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.
    Wiechec, Emilia
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology.
    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.
    Turner, Anthony P. F.
    Linköping University, Department of Physics, Chemistry and Biology, Biosensors and Bioelectronics. Linköping University, The Institute of Technology.
    Tiwari, Ashutosh
    Linköping University, Department of Physics, Chemistry and Biology, Biosensors and Bioelectronics. Linköping University, The Institute of Technology.
    MRI-Visual Order–Disorder Micellar Nanostructures for Smart Cancer Theranostics2014In: Advanced Healthcare Materials, ISSN 2192-2640, Vol. 3, no 4, p. 526-535Article in journal (Refereed)
    Abstract [en]

    The development of MRI-visual order–disorder structures for cancer nanomedicine explores a pH-triggered mechanism for theragnosis of tumor hallmark functions. Superparamagnetic iron oxide nanoparticles (SPIONs) stabilized with amphiphilic poly(styrene)-b-poly(acrylic acid)-doxorubicin with folic acid (FA) surfacing are employed as a multi-functional approach to specifically target, diagnose, and deliver drugs via a single nanoscopic platform for cancer therapy. The functional aspects of the micellar nanocomposite is investigated in vitro using human breast SkBr3 and colon cancer HCT116 cell lines for the delivery, release, localization, and anticancer activity of the drug. For the first time, concentration-dependent T2-weighted MRI contrast for a monolayer of clustered cancer cells is shown. The pH tunable order–disorder transition of the core–shell structure induces the relative changes in MRI contrast. The outcomes elucidate the potential of this material for smart cancer theranostics by delivering non-invasive real-time diagnosis, targeted therapy, and monitoring the course and response of the action before, during, and after the treatment regimen.

  • 25.
    Peterson, Pernilla
    et al.
    Skåne University Hospital, Sweden.
    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).
    Brorson, Hakan
    Lund University, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Mansson, Sven
    Skåne University Hospital, Sweden.
    Fat Quantification in Skeletal Muscle Using Multigradient-Echo Imaging: Comparison of Fat and Water References2016In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 43, no 1, p. 203-212Article in journal (Refereed)
    Abstract [en]

    Purpose: To investigate the precision, accuracy, and repeatability of water/fat imaging-based fat quantification in muscle tissue using a large flip angle (FA) and a fat reference for the calculation of the proton density fat fraction (FF). Comparison is made to a small FA water reference approach. Materials and Methods: An Intralipid phantom and both forearms of six patients suffering from lymphedema and 10 healthy volunteers were investigated at 1.5T. Two multigradient-echo sequences with eight echo times and FAs of 10 degrees and 85 degrees were acquired. For healthy volunteers, the acquisition of the right arm was performed twice with repositioning. From each set, water reference FF and fat reference FF images were reconstructed and the average FF and the standard deviation were calculated within the subfascial compartment. The small FA water reference was considered the reference standard. Results: A high agreement was found between the small FA water reference and large FA fat reference methods (FF bias=0.31%). In this study, the large FA fat reference approach also resulted in higher precision (38% smaller FF standard deviation in homogenous muscle tissue), but no significant difference in repeatability between the various methods was detected (coefficient of repeatability of small FA water reference approach 0.41%). Conclusion: The precision of fat quantification in muscle tissue can be increased with maintained accuracy using a larger flip angle, if a fat reference instead of a water reference is used.

  • 26.
    Romu, Thobias
    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).
    Fat-Referenced MRI: Quanitaive MRI for Tissue Characterizaion and Volume Measurement2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The amount and distribution of adipose and lean tissues has been shown to be predictive of mortality and morbidity in metabolic disease. Traditionally these risks are assessed by anthropometric measurements based on weight, length, girths or the body mass index (BMI). These measurements are predictive of risks on a population level, where a too low or a too high BMI indicates an increased risk of both mortality and morbidity. However, today a large part of the world’s population belongs to a group with an elevated risk according to BMI, many of which will live long and healthy lives. Thus, better instruments are needed to properly direct health-care resources to those who need it the most.

    Medical imaging method can go beyond anthropometrics. Tomographic modalities, such as magnetic resonance imaging (MRI), can measure how we have stored fat in and around organs. These measurements can eventually lead to better individual risk predictions. For instance, a tendency to store fat as visceral adipose tissue (VAT) is associated with an increased risk of diabetes type 2, cardio-vascular disease, liver disease and certain types of cancer. Furthermore, liver fat is associated with liver disease, diabetes type 2. Brown adipose tissue (BAT), is another emerging component of body-composition analysis. While the normal white adipose tissue stores fat, BAT burns energy to produce heat. This unique property makes BAT highly interesting, from a metabolic point of view.

    Magnetic resonance imaging can both accurately and safely measure internal adipose tissue compartments, and the fat infiltration of organs. Which is why MRI is often considered the reference method for non-invasive body-composition analysis. The two major challenges of MRI based body-composition analysis are, the between-scanner reproducibility and a cost-effective analysis of the images. This thesis presents a complete implementation of fat-referenced MRI, a technique that produces quantitative images that can increase both inter-scanner and automation of the image analysis.

    With MRI, it is possible to construct images where water and fat are separated into paired images. In these images, it easy to depict adipose tissue and lean tissue structures. This thesis takes water-fat MRI one step further, by introducing a quantitative framework called fat-referenced MRI. By calibrating the image using the subjects' own adipose tissue (paper II), the otherwise non-quantitative fat images are made quantitative. In these fat-referenced images it is possible to directly measure the amount of adipose tissue in different compartments. This quantitative property makes image analysis easy and accurate, as lean and adipose tissues can be separated on a sub-voxel level. Fat-referenced MRI further allows the quantification and characterization of BAT.

    This thesis work starts by formulating a method to produce water-fat images (paper I) based on two gradient recall images, i.e.\ 2-point Dixon images (2PD). It furthers shows that fat-referenced 2PD images can be corrected for T2*, making the 2PD body-composition measurements comparable with confounder-corrected Dixon measurements (paper III}).

    Both the water-fat separation method and fat image calibration are applied to BAT imaging. The methodology is first evaluated in an animal model, where it is shown that it can detect both BAT browning and volume increase following cold acclimatization (paper IV). It is then applied to postmortem imaging, were it is used to locate interscapular BAT in human infants (paper V). Subsequent analysis of biopsies, taken based on the MRI images, showed that the interscapular BAT was of a type not previously believed to exist in humans. In the last study, fat-referenced MRI is applied to BAT imaging of adults. As BAT structures are difficult to locate in many adults, the methodology was also extended with a multi-atlas segmentation methods (paper VI).

    In summary, this thesis shows that fat-referenced MRI is a quantitative method that can be used for body-composition analysis. It also shows that fat-referenced MRI can produce quantitative high-resolution images, a necessity for many BAT applications.

    List of papers
    1. Robust Water Fat Separated Dual-Echo MRI by Phase-Sensitive Reconstruction
    Open this publication in new window or tab >>Robust Water Fat Separated Dual-Echo MRI by Phase-Sensitive Reconstruction
    2017 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, no 3, p. 1208-1216Article in journal (Refereed) Published
    Abstract [en]

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

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

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

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

    Place, publisher, year, edition, pages
    Wiley-Blackwell, 2017
    Keywords
    Dixon Imaging; Phase correction; Segmentation; Water-fat separation; Whole-body imaging
    National Category
    Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-131134 (URN)10.1002/mrm.26488 (DOI)000407855700040 ()27775180 (PubMedID)
    Available from: 2016-09-12 Created: 2016-09-12 Last updated: 2019-06-14
    2. MANA - Multi scale adaptive normalized averaging
    Open this publication in new window or tab >>MANA - Multi scale adaptive normalized averaging
    2011 (English)In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE conference proceedings, 2011, p. 361-364Conference paper, Published paper (Refereed)
    Abstract [en]

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

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2011
    Series
    International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-7928
    National Category
    Medical Laboratory and Measurements Technologies Computer Vision and Robotics (Autonomous Systems) Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-67848 (URN)10.1109/ISBI.2011.5872424 (DOI)000298849400083 ()978-1-4244-4128-0 (ISBN)
    Conference
    IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, USA, March 30 2011-April 2 2011
    Available from: 2011-04-29 Created: 2011-04-29 Last updated: 2019-06-14
    3. Characterization of Brown Adipose Tissue by water-fat separated Magnetic Resonance Imaging
    Open this publication in new window or tab >>Characterization of Brown Adipose Tissue by water-fat separated Magnetic Resonance Imaging
    Show others...
    2015 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 6, p. 1639-1645Article in journal (Refereed) Published
    Abstract [en]

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

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

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

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

    Place, publisher, year, edition, pages
    John Wiley & Sons, 2015
    Keywords
    Brown Adipose Tissue; BAT; Fat Water MRI
    National Category
    Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-116931 (URN)10.1002/jmri.24931 (DOI)000368258100020 ()
    Funder
    Knowledge Foundation, 2011.0059
    Available from: 2015-04-09 Created: 2015-04-09 Last updated: 2019-06-14
    4. Evidence for two types of brown adipose tissue in humans
    Open this publication in new window or tab >>Evidence for two types of brown adipose tissue in humans
    Show others...
    2013 (English)In: Nature Medicine, ISSN 1078-8956, E-ISSN 1546-170X, Vol. 19, no 5, p. 631-634Article in journal (Refereed) Published
    Abstract [en]

    The previously observed supraclavicular depot of brown adipose tissue (BAT) in adult humans was commonly believed to be the equivalent of the interscapular thermogenic organ of small mammals. This view was recently disputed on the basis of the demonstration that this depot consists of beige (also called brite) brown adipocytes, a newly identified type of brown adipocyte that is distinct from the classical brown adipocytes that make up the interscapular thermogenic organs of other mammals. A combination of high-resolution imaging techniques and histological and biochemical analyses showed evidence for an anatomically distinguishable interscapular BAT (iBAT) depot in human infants that consists of classical brown adipocytes, a cell type that has so far not been shown to exist in humans. On the basis of these findings, we conclude that infants, similarly to rodents, have the bona fide iBAT thermogenic organ consisting of classical brown adipocytes that is essential for the survival of small mammals in a cold environment.

    Place, publisher, year, edition, pages
    Nature Publishing Group, 2013
    Keywords
    BAT, MRI
    National Category
    Medical Genetics Cell and Molecular Biology Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-91307 (URN)10.1038/nm.3017 (DOI)000318583000037 ()
    Funder
    Knut and Alice Wallenberg Foundation, 2011.0059
    Available from: 2013-04-21 Created: 2013-04-21 Last updated: 2019-06-14Bibliographically approved
    5. A randomized trial of cold-exposure on energy expenditure and supraclavicular brown adipose tissue volume in humans
    Open this publication in new window or tab >>A randomized trial of cold-exposure on energy expenditure and supraclavicular brown adipose tissue volume in humans
    Show others...
    2016 (English)In: Metabolism: Clinical and Experimental, ISSN 0026-0495, E-ISSN 1532-8600, Vol. 65, no 6, p. 926-934Article in journal (Refereed) Published
    Abstract [en]

    Objective

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

    Design

    Randomized, open, parallel-group trial.

    Methods

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

    Results

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

    Conclusions

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

    Place, publisher, year, edition, pages
    Elsevier, 2016
    Keywords
    Brown adipose tissue; Cold exposure; Magnetic resonance imaging; Metabolic rate
    National Category
    Pharmacology and Toxicology
    Identifiers
    urn:nbn:se:liu:diva-128200 (URN)10.1016/j.metabol.2016.03.012 (DOI)000376145100013 ()27173471 (PubMedID)
    Funder
    Knut and Alice Wallenberg Foundation
    Note

    Funding agencies: Linkoping University; County Council of Ostergotland (LUA-ALF), Sweden; Swedish Research Council [2013-4466, 2012-1652, 2014-2516]; Knut and Alice Wallenberg Foundation; Sahlgrenskas University Hospital (LUA-ALF); European Union grant (DIABAT) [HEALTH-F2-

    Available from: 2016-05-22 Created: 2016-05-22 Last updated: 2019-06-14
  • 27.
    Romu, Thobias
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    MANA - Multi scale adaptive normalized averaging2011In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE conference proceedings, 2011, p. 361-364Conference paper (Refereed)
    Abstract [en]

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

  • 28.
    Romu, Thobias
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Camilla, Vavruch
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Tallberg, Joakim
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Heglind, Mikael
    University of Gothenburg, Gothenburg, Sweden..
    Lidell, Martin
    University of Gothenburg, Gothenburg, Sweden..
    Enerbäck, Sven
    University of Gothenburg, Gothenburg, Sweden..
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology.
    A randomized trial of cold-exposure on energy expenditure and supraclavicular brown adipose tissue volume in humans2016In: Metabolism: Clinical and Experimental, ISSN 0026-0495, E-ISSN 1532-8600, Vol. 65, no 6, p. 926-934Article in journal (Refereed)
    Abstract [en]

    Objective

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

    Design

    Randomized, open, parallel-group trial.

    Methods

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

    Results

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

    Conclusions

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

  • 29.
    Romu, Thobias
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Forsgren, Mikael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Gastroenterology and Hepatology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Dahlström, Nils
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology and Gastroenterology UHL.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Fat Water Classification of Symmetrically Sampled Two-Point Dixon Images Using Biased Partial Volume Effects2011In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2011), 2011., 2011Conference paper (Refereed)
  • 30.
    Romu, Thobias
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Robust fat-­‐water separation of symmetrically sampled two point Dixon data2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Refereed)
  • 31.
    Romu, Thobias
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Elander, Louise
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    High resolution symmetrically sampled two point Dixon imaging2012In: Proceedings of the ISMRM Annual Meeting, 2012Conference paper (Other academic)
  • 32.
    Romu, Thobias
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Elander, Louise
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Lidell, Martin
    Göteborgs universitet.
    Betz, Matthias
    Klinikum der Ludwig Maximilians University, Munich.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Enerbäck, Sven
    Göteborgs universitet.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Characterization of Brown Adipose Tissue by water-fat separated Magnetic Resonance Imaging2015In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 6, p. 1639-1645Article in journal (Refereed)
    Abstract [en]

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

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

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

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

  • 33.
    Romu, Thobias
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    West, Janne
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Spetz, Anna-Clara
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Lindblom, Hanna
    Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences.
    Lindh Åstrand, Lotta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Hammar, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    The effect of flip-angle on body composition using calibrated water-fat MRI.2016Conference paper (Other academic)
    Abstract [en]

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

  • 34.
    Thunón, Patrik
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Zanjanis, Sepher
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Gjellan, Solveig
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Body Composition Volumetry by Whole-Body Water-Fat Separated MRI2014Conference paper (Other academic)
  • 35.
    West, Janne
    et al.
    Linköping University, Department of Medical and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Thomas, E. Louise
    Department of Life Sciences Faculty of Science and Technology, University of Westminster, London, UK.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bell, Jimmy
    Department of Life Sciences Faculty of Science and Technology, University of Westminster, London, UK.
    Body Composition Analysis In Large Scale Population Studies using Dixon Water-Fat Separated Imaging2016Conference paper (Other academic)
    Abstract [en]

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

  • 36.
    West, Janne
    et al.
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Spetz, Anna-Clara
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Lindblom, Hanna
    Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences.
    Lindh Åstrand, Lotta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Hammar, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Automatic combined whole-body muscle and fat volume quantification using water-fat separated MRI in postmenopausal women2015In: International Society for Magnetic Resonance in Medicine Annual Meeting: Proceedings, 2015Conference paper (Other academic)
    Abstract [en]

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

1 - 36 of 36
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