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Borga, M., West, J., Bell, J., Harvey, N., Romu, T., Heymsfield, S. & Dahlqvist Leinhard, O. (2018). Advanced body composition assessment: From body mass index to body composition profiling. Journal of Investigative Medicine, 66, 887-895
Open this publication in new window or tab >>Advanced body composition assessment: From body mass index to body composition profiling
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2018 (English)In: Journal of Investigative Medicine, ISSN 1081-5589, E-ISSN 1708-8267, Vol. 66, p. 887-895Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2018
Keywords
Body-composition-analysis, MRI, UK Biobank
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-145624 (URN)10.1136/jim-2018-00072 (DOI)000435456400001 ()29581385 (PubMedID)
Available from: 2018-03-08 Created: 2018-03-08 Last updated: 2018-09-18Bibliographically approved
Linge, J., Borga, M., West, J., Tuthill, T., Miller, M., Dumitriu, A., . . . Dahlqvist Leinhard, O. (2018). Body Composition Profiling in the UK Biobank Imaging Study. Obesity
Open this publication in new window or tab >>Body Composition Profiling in the UK Biobank Imaging Study
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2018 (English)In: Obesity, ISSN 1930-7381, E-ISSN 1930-739XArticle in journal (Refereed) Accepted
Abstract [en]

Objective

To investigate the value of imaging-based multivariable body composition profiling by describing its association with coronary heart disease (CHD), type 2 diabetes (T2D), and metabolic health on individual and population levels.

 

Methods

The first 6,021 participants scanned by UK Biobank were included. Body composition profiles (BCPs) were calculated including abdominal subcutaneous adipose tissue,visceral adipose tissue (VAT), thigh muscle volume, liver fat, and muscle fat infiltration (MFI), determined using magnetic resonance imaging. Associations between BCP and metabolic status were investigated using matching procedures and multivariable statistical modelling.

 

Results

Matched control analysis showed higher VAT and MFI was associated with CHD and T2D (p<0.001). Higher liver fat was associated with T2D (p<0.001) and lower liver fat with CHD (p<0.05), matching on VAT. Multivariable modelling showed lower VAT and MFI was associated with metabolic health (p<0.001), liver fat was non-significant. Associations remained significant adjusting for sex, age, BMI, alcohol, smoking, and physical activity.

 

Conclusions

Body composition profiling enabled an intuitive visualization of body composition and showed the complexity of associations between fat distribution and metabolic status, stressing the importance of a multivariable approach. Different diseases were linked to different BCPs, which could not be described by a single fat compartment alone.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
"Body Composition Profile", BCP, MRI
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-147537 (URN)10.1002/oby.22210 (DOI)
Available from: 2018-05-22 Created: 2018-04-24 Last updated: 2018-05-17Bibliographically approved
Andersson, T., Borga, M. & Dahlqvist Leinhard, O. (2018). Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds. Pattern Recognition Letters, 112, 340-345
Open this publication in new window or tab >>Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds
2018 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 112, p. 340-345Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Atlas-based segmentation, Image registration, Manifold learning, MRI
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-148304 (URN)10.1016/j.patrec.2018.04.037 (DOI)000443950800049 ()
Available from: 2018-06-07 Created: 2018-06-07 Last updated: 2018-09-27Bibliographically approved
Borga, M. (2018). MRI adipose tissue and muscle composition analysis: a review of automation techniques. British Journal of Radiology, 91(1089), Article ID 20180252.
Open this publication in new window or tab >>MRI adipose tissue and muscle composition analysis: a review of automation techniques
2018 (English)In: British Journal of Radiology, ISSN 0007-1285, E-ISSN 1748-880X, Vol. 91, no 1089, article id 20180252Article, review/survey (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
London, United Kingdom: British Institute of Radiology, 2018
Keywords
MRI; adipose tissue; automated sgmentation
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-149809 (URN)10.1259/bjr.20180252 (DOI)000443131900031 ()30004791 (PubMedID)
Available from: 2018-07-25 Created: 2018-07-25 Last updated: 2018-09-21Bibliographically approved
Agebratt, C., Ström, E., Romu, T., Dahlqvist Leinhard, O., Borga, M., Leandersson, P. & Nyström, F. H. (2016). A Randomized Study of the Effects of Additional Fruit and Nuts Consumption on Hepatic Fat Content, Cardiovascular Risk Factors and Basal Metabolic Rate. PLoS ONE, 11(1), e0147149
Open this publication in new window or tab >>A Randomized Study of the Effects of Additional Fruit and Nuts Consumption on Hepatic Fat Content, Cardiovascular Risk Factors and Basal Metabolic Rate
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 1, p. e0147149-Article in journal (Refereed) Published
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.

Keywords
Fruits Basal metabolic rate measurement Fats Vitamin C Fructoses Diet Fatty liver Magnetic resonance imaging
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:liu:diva-124605 (URN)10.1371/journal.pone.0147149 (DOI)000368529100062 ()26788923 (PubMedID)
Note

Funding agencies: County Council of Ostergotland; Linkoping University, Department of Medical and Health Sciences

Available from: 2016-02-05 Created: 2016-02-05 Last updated: 2018-03-27
Romu, T., Camilla, V., Dahlqvist Leinhard, O., Tallberg, J., Dahlström, N., Persson, A., . . . Nyström, F. (2016). A randomized trial of cold-exposure on energy expenditure and supraclavicular brown adipose tissue volume in humans. Metabolism: Clinical and Experimental, 65(6), 926-934
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
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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: 2018-03-22
West, J., Dahlqvist Leinhard, O., Romu, T., Thomas, E. L., Borga, M. & Bell, J. (2016). Body Composition Analysis In Large Scale Population Studies using Dixon Water-Fat Separated Imaging. In: : . Paper presented at International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition, Singapore, May 7-13, 2016.
Open this publication in new window or tab >>Body Composition Analysis In Large Scale Population Studies using Dixon Water-Fat Separated Imaging
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2016 (English)Conference paper, Poster (with or without abstract) (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. 

National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-128987 (URN)
Conference
International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition, Singapore, May 7-13, 2016
Available from: 2016-06-07 Created: 2016-06-07 Last updated: 2016-06-22Bibliographically approved
Cros, O., Knutsson, H., Andersson, M., Pawels, E., Borga, M. & Gaihede, M. (2016). Determination of the mastoid surface area and volume based on micro-CT scanning of human temporal bone: Geometrical parameters dependence on scanning resolutions. Hearing Research, 340, 127-134
Open this publication in new window or tab >>Determination of the mastoid surface area and volume based on micro-CT scanning of human temporal bone: Geometrical parameters dependence on scanning resolutions
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2016 (English)In: Hearing Research, ISSN 0378-5955, E-ISSN 1878-5891, Vol. 340, p. 127-134Article in journal (Other academic) Published
Abstract [en]

The mastoid air cell system (MACS) with its large complex of interconnected air cells reflects an enhanced surface area (SA) relative to its volume (V), which may indicate that the MACS is adapted to gas exchange and has a potential role in middle ear pressure regulation. Thus, these geometric parameters of the MACS have been studied by high resolution clinical CT scanning. However, the resolution of these scans is limited to a voxel size of around 0.6 mm in all dimensions, and so, the geometrical parameters are also limited. Small air cells may appear below the resolution and cannot be detected. Such air cells may contribute to a much higher SA than the V, and thus, also the SA/V ratio. More accurate parameters are important for analysis of the function of the MACS including physiological modeling.

Our aim was to determine the SA, V, and SA/V ratio in MACS in human temporal bones at highest resolution by using micro-CT-scanning. Further, the influence of the resolution on these parameters was investigated by downsampling the data. Eight normally aerated temporal bones were scanned at the highest possible resolution (30-60 μm). The SA was determined using a triangular mesh fitted onto the segmented MACS. The V was determined by summing all the voxels containing air. Downsampling of the original data was applied four times by a factor of 2.

The mean SA was 194 cm2, the mean V was 9 cm3, and the mean SA/V amounted to 22 cm-1. Decreasing the resolution resulted in a non-linear decrement of SA and SA/V, whereas V was mainly independent of the resolution.

The current study found significantly higher SA and SA/V compared with previous studies using clinical CT scanning at lower resolutions. These findings indicate a separate role of the MACS compared with the tympanum, and the results are important for a more accurate modeling of the middle ear physiology.

Keywords
Mastoid air cells; medical imaging; micro-CT; surface area; volume
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-122176 (URN)10.1016/j.heares.2015.12.005 (DOI)000386417900016 ()
Available from: 2015-10-23 Created: 2015-10-23 Last updated: 2017-12-01Bibliographically approved
Haufe, W., Hooker, J., Schlein, A., Szeverenyi, N., Borga, M., Dahlqvist Leinhard, O., . . . Sirlin, C. B. (2016). Feasibility of an automated tissue segmentation technique in a longitudinal weight loss study. In: : . Paper presented at International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition, Singapore, May 7-13, 2016.
Open this publication in new window or tab >>Feasibility of an automated tissue segmentation technique in a longitudinal weight loss study
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2016 (English)Conference paper, Poster (with or without abstract) (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.

National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-128990 (URN)
Conference
International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition, Singapore, May 7-13, 2016
Available from: 2016-06-07 Created: 2016-06-07 Last updated: 2016-06-22Bibliographically approved
Middleton, M., Haufe, W., Hooker, J., Borga, M., Dahlqvist Leinhard, O., Romu, T., . . . Sirlin, C. B. (2016). Repeatability and accuracy of a novel, MRI-based, semi-automated analysis method for quantifying abdominal adipose tissue and thigh muscle volumes. In: : . Paper presented at International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition, Singapore, May 7-13, 2016.
Open this publication in new window or tab >>Repeatability and accuracy of a novel, MRI-based, semi-automated analysis method for quantifying abdominal adipose tissue and thigh muscle volumes
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2016 (English)Conference paper, Poster (with or without abstract) (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.

National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-128988 (URN)
Conference
International Society for Magnetic Resonance in Medicine Annual Meeting & Exhibition, Singapore, May 7-13, 2016
Available from: 2016-06-07 Created: 2016-06-07 Last updated: 2016-06-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9267-2191

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