Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies

Introduction Quantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects. Materials and Methods 3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics. Results Of the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view. Discussion and Conclusions In conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource

Cultivate Quantitative Magnetic Resonance Imaging Methods to Measure Markers of Health and Translate to Large Scale Cohort Studies

Magnetic Resonance Imaging (MRI) is an indispensable tool in healthcare and research, with a growing demand for its services. The appeal of MRI stems from its non-ionizing radiation nature, ability to generate high-resolution images of internal organs and structures without invasive procedures, and capacity to provide quantitative assessments of tissue properties such as ectopic fat, body composition, and organ volume. All without long term side effects. Nine published papers are submitted which show the cultivation of quantitative measures of ectopic fat within the liver and pancreas using MRI, and the process of validating whole-body composition and organ volume measurements. All these techniques have been translated into large-scale studies to improve health measurements in large population cohorts. Translating this work into large-scale studies, including the use of artificial intelligence, is included. Additionally, an evaluation accompanies these published studies, appraising the evolution of these quantitative MRI techniques from the conception to their application in large cohort studies. Finally, this appraisal provides a summary of future work on crowdsourcing of ground truth training data to facilitate its use in wider applications of artificial intelligence.In conclusion, this body of work presents a portfolio of evidence to fulfil the requirements of a PhD by published works at the University of Salford

Acquisition and Reconstruction Techniques for Fat Quantification Using Magnetic Resonance Imaging

Quantifying the tissue fat concentration is important for several diseases in various organs including liver, heart, skeletal muscle and kidney. Uniquely, MRI can separate the signal from water and fat in-vivo, rendering it the most suitable imaging modality for non-invasive fat quantification. Chemical-shift-encoded MRI is commonly used for quantitative fat measurement due to its unique ability to generate a separate image for water and fat. The tissue fat concentration can be consequently estimated from the two images. However, several confounding factors can hinder the water/fat separation process, leading to incorrect estimation of fat concentration. The inhomogeneities of the main magnetic field represent the main obstacle to water/fat separation. Most existing techniques rely mainly on imposing spatial smoothness constraints to address this problem; however, these often fail to resolve large and abrupt variations in the magnetic field. A novel convex relaxation approach to water/fat separation is proposed. The technique is compared to existing methods, demonstrating its robustness to resolve abrupt magnetic field inhomogeneities. Water/fat separation requires the acquisition of multiple images with different echo-times, which prolongs the acquisition time. Bipolar acquisitions can efficiently acquire the required data in shorter time. However, they induce phase errors that significantly distort the fat measurements. A new bipolar acquisition strategy that overcomes the phase errors and provides accurate fat measurements is proposed. The technique is compared to the current clinical sequence, demonstrating its efficiency in phantoms and in-vivo experiments. The proposed acquisition technique is also applied on animal models to achieve higher spatial resolution than the current sequence. In conclusion, this dissertation describes a complete framework for accurate and precise MRI fat quantification. Novel acquisitions and reconstruction techniques that address the current challenges for fat quantification are proposed

Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia

Bone and muscle are two deeply interconnected organs and a strong relationship between them exists in their development and maintenance. The peak of both bone and muscle mass is achieved in early adulthood, followed by a progressive decline after the age of 40. The increase in life expectancy in developed countries resulted in an increase of degenerative diseases affecting the musculoskeletal system. Osteoporosis and sarcopenia represent a major cause of morbidity and mortality in the elderly population and are associated with a significant increase in healthcare costs. Several imaging techniques are currently available for the non-invasive investigation of bone and muscle mass and quality. Conventional radiology, dual energy X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound often play a complementary role in the study of osteoporosis and sarcopenia, depicting different aspects of the same pathology. This paper presents the different imaging modalities currently used for the investigation of bone and muscle mass and quality in osteoporosis and sarcopenia with special emphasis on the clinical applications and limitations of each technique and with the intent to provide interesting insights into recent advances in the field of conventional imaging, novel high-resolution techniques and fracture risk

Quantitative Magnetic Resonance Imaging Techniques for the Measurement of Organ Fat and Body Composition - Validation and Initial Clinical Utility

Ectopic fat is defined by excess deposition of triglycerides in non-adipose tissues that normally contain only small amounts of fat. Measuring the distribution of ectopic fat is important for understanding the pathogenesis of diseases such as obesity and type 2 diabetes mellitus (T2DM) and understanding variation in treatment response amongst patients. Body composition (the proportion of fat and lean mass in the body) is thought to influence both the development of T2DM and outcomes for treatments such as weight-loss surgery. It can also affect clinical outcomes in chronic diseases and malignancy. Quantitative magnetic resonance imaging (qMRI) enables objective measurements of tissue characteristics to be made directly from acquired data. In this thesis, a qMRI protocol based on chemical shift-encoded (CSE)-MRI, specifically the derived proton density fat fraction (PDFF) measurements, was validated against phantoms, and in volunteers and patients with obesity. A new, semi-automated tool for measurement of body composition from CSE-MRI images was developed and validated. CSE-MRI was used to quantify ectopic organ fat depots and body composition in diseases including obesity, T2DM and cancer. Specifically, differences in organ fat between patients with and without remission of T2DM after bariatric surgery was explored. Body composition was investigated in T2DM remission and it was also compared between patients with colorectal and lung cancer undergoing whole body MRI staging. Data from the pilot phase of a study investigating a new duodenal surfacing procedural treatment for T2DM (Revita-2) is presented, demonstrating the utility of hepatic fat content measured using PDFF as an endpoint in an international, multi-centre clinical trial. Finally, I describe the development of a novel technique for quantification of bone mineral density (BMD) using CSE-MRI techniques. The methodology and tools described in this thesis could be used to measure ectopic fat and body composition in future studies and have the potential for integration into clinical care pathways

Quantification of periaortic fat tissue in contrast-enhanced computed tomography

Objective. Periaortic fat tissue (PaFT) has been implicated in the progression of abdominal aortic aneurysms (AAAs). Therefore, its quantification as a prognostic marker for aneurysm expansion has attracted clinical interest. Most existing research on PaFT, however, is based on unenhanced aortic CT scans, whereas the CT diagnosis of aortic aneurysms is usually performed with enhanced CT angiographies. The objective of this study is to examine the feasibility of measuring abdominal periaortic fat tissue in enhanced aortic CT-scans using a new method based on the OsirixMD post-processing software and evaluate any methodological issues/considerations arising from it, in order to reliably quantify periaortic fat tissue from enhanced and unenhanced CT-scans. Methods. In a derivation cohort (n= 101), PaFT Volume and PaFT mean HU value were measured within a 5 mm-wide periaortic ring in arterial phases and compared to the same values from native scans. Fat tissue was defined within the range of -45 to -195 Hounsfield Units (HU). After testing their correlation, fat tissue values from both CT phases underwent linear regression through the origin to define a correction factor (slope of the line of best fit), allowing the conversion of arterial back to native scores. This conversion factor was then applied to fat tissue values in a different validation cohort (n=47) and the agreement of the corrected fat tissue values and values in the native scans was examined using Bland-Altman plots and Passing-Bablok regression. In a secondary study the pooled date sets from both studies (n=148) were stratified in an AAA and non-AAA group and the average fat tissue values for both groups (with PaFT volumes adjusted for aortic size) were calculated using both native and corrected arterial values. Results. In the derivation cohort, periaortic fat tissue Volume and mean HU value showed very high correlations between arterial and native scans (r> .99 and r= .95 respectively, p< .0001 both). Linear regression defined a conversion factor of 1.1057 for arterial periaortic fat tissue Volume and 1.0011 for arterial periaortic fat tissue mean HU. Potential confounding factors (mean intraluminal contrast density, aortic wall calcification, longitudinal contrast dispersion, aortic diameter, CT-tube voltage, slice thickness, image noise) showed no significant impact in multivariate regression. Application of the conversion factors in arterial scans of the validation study resulted in corrected arterial fat tissue values that showed very good agreement with PaFT values in native scans. Bland Altman analysis showed the following mean differences [95% confidence interval]: 0.36 [-0.01 to 0.73] for periaortic fat tissue Volume and 0.83 [-1.08 to 0.1] for periaortic fat tissue mean HU. Passing-Bablok regression confirmed minimal/no residual bias. Median periaortic fat tissue size-adjusted PaFT Volumes and Mean HU values from the Mann-Whitney test showed no significant difference between the AAA and non-AAA groups. Conclusion. Periaortic fat tissue Volume and mean HU values demonstrate only minimal variation between arterial and native scans and can be measured in enhanced aortic CT scans with very high reliability. Periaortic fat tissue Mean HU value, unlike Volume, is independent from the presence of paraaortic organs. Certain issues, like non-circular aortic discs, histological boundaries of periortic fat tissue and dependence from Body Mass Index and other fat tissue depots need to be explored further.1. ZUSAMMENFASSUNG Ziel. Das periaortale Fettgewebe spielt bei der Progression von Aortenaneurysmen eine Rolle, so dass seine Quantifizierung als prognostischer Marker für die Aneurysmaprogression von besonderem klinischem Interesse ist. Die aktuelle Forschung ist basiert jedoch fast ausschließlich auf nativen CTs, während Aortenaneurysmen üblicherweise nur mittels kontrastmittelverstärkten CT angiographien dargestellt werden. Das Ziel dieser Studie ist die methodische Überprüfung der Bestimmung vom abdominalen periaortalen Fettgewebe in kontrastmittelverstärkten CTs mit der frei verfügbaren OsirixMD Softwareanwendung und die Evaluation von potenziellen Faktoren, die eine zuverlässige periaortale Fettgewebsquantifikation in nativen und kontrastverstärkten CTs ermöglichen. Methodik. In einer Derivationsgruppe (n=101), wurde das Fettgewebsvolumen und die HU Mittelwerte innerhalb von einem 5 mm breiten periaortalen Ring in arteriellen CTs bestimmt und die Werte wurden mit entsprechenden Werten aus nativen CTs verglichen. Das Fettgewebe wurde als HU Werte -45 bis -195 HU definiert. Die Fettgewebswerte von beiden CT-Phasen wurden auf Korrelation überprüft und anschließend einer linearen Regressionsanalyse unterzogen, wobei ein Konversionsfaktor bestimmt wurde, um arterielle in nativen Fettgewebswerten zu konvertieren. Der Konversionsfaktor wurde danach in einer zweiten Validierungsgruppe (n=47) angewendet. Sodann wurde die Übereinstimmung von korrigierten arteriellen und nativen Fettgewebswerten mittels Bland-Altmann Plots und Passing-Bablok Regressionsanalyse überprüft. In einer Sekundärstudie, wurden die gepoolten Datasets beider Studien (n=148) in einer Bauchaortenaneurysma- und einer Nichtbauchaortenanerysmagruppe stratifiziert, um die Mittelwerte von Fettgewebsvolumen (adjustiert für Aortengröße) und HU Mittelwert in beiden Gruppen zu bestimmen. Ergebnisse. In der Derivationsgruppe, zeigte das Fettgewebsvolumen und der HU Mittelwert eine sehr hohe Korrelation zwischen kontrastverstärkten und nativen CTs (r > 0,99 und r= 0,95 entsprechend, p< 0,0001 für beide). Die lineare Regressionsanalyse ergab einen Konversionsfaktor von 1,1057 für das Fettgewebsvolumen und 1,0011 für den Fettgewebs-HU Mittelwert. Potenzielle Störfaktoren (intraluminale Kontrastmitteldichte, Aortenwandkalzifikation, longitudinale Kontrastmittelverteilung, Aortendiameter, CT-Röhrenspannung, Slicestärke, Größe der intraluminalen Kontrast-ROI, Bildrauschen) zeigten keinen signifikanten Einfluss in der multiplen Regressionsanalyse. In der Validierungsgruppe, zeigten die mittels Konversionsfaktor korrigierten Fettgewebswerte der arteriellen Phase eine sehr hohe Übereinstimmung mit den Fettgewebswerten der nativen CT-Phase. Die Bland-Altman Analyse ergab folgende mittlere Differenzen [95% Konfidenzintervall]: 0,36 [- 0,01 bis 0,73] fürs Volumen und 0,83 [-1,08 bis 0,1] für den HU Mittelwert. Die Passing-Bablok Regressionsanalyse bestätigte ein minimales bzw. kein residuales Bias. In der Sekundärstudie, zeigten die Mediane der Fettgewebswerte aus dem Mann-Whitney Test keinen signifikanten Unterschied zwischen der BAA und nicht-BAA Gruppe. Schlussfolgerung. Periaortales Fettgewebsvolumen und HU-Mittelwert zeigen eine minimale Variation zwischen arteriellen und nativen CTs und lassen sich in kontrastverstärkten Aorten-CTs sehr zuverlässig bestimmen. Der Fettgewebsmittelwert ist von der Präsenz anderer paraaortale Organe unabhängig. Gewisse Faktoren, z.B. nicht-zirkuläre aortalen Scheiben, histologische Grenzen des periaortalen Fettgewebes und seine Abhängigkeit vom Body Mass Index und anderen Fettgewebskompartimenten benötigen eine weitere Analyse

Measurement of treatment response and survival prediction in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is a rare cancer of the mesothelial cells of the visceral and parietal pleurae that is heterogeneous in terms of biology, prognosis and response to systemic anti-cancer therapy (SACT). The primary tumour forms an unusual, complex shape which makes survival prediction and response measurement uniquely challenging. Computed tomography (CT) imaging is the bedrock of radiological quantification and response assessment, but it has major limitations that translate into low sensitivity and high inter-observer variation when classifying response using Response Evaluation Classification In Solid Tumours (mRECIST) criteria. Magnetic resonance imaging (MRI) tools have been developed that overcome some of these problems but cost and availability of MRI mean that optimisation of CT and better use for data acquired by this method are important priorities in the short term. In this thesis, I conducted 3 studies focused on, 1) development of a semi-automated volumetric segmentation method for CT based on recently positive studies in MRI, 2) training and external validation of a deep learning artificial intelligence (AI) tool for fully automated volumetric segmentation based on CT data, and, 3) use of non-tumour imaging features available from CT related to altered body composition for development of new prognostic models, which could assist in selection of patients for treatment and improving tolerance to treatment by targeting the systemic consequences of MPM. The aim of Chapter 3 is to develop a semi-automated MPM tumour volume segmentation method that would serve as the ground truth for the training of a fully automated AI algorithm. A semi-automated approach to pleural tumour segmentation has been developed using MRI scans which calculated volumetric measurements from seed points - defined by differential tumour enhancement - placed within a pre-defined volume of pleural tumour. I extrapolated this MRI method using contrast-enhanced CT scans in 23 patients with MPM. Radiodensity values – defined by Hounsfield units (HU) - were calculated for the different thoracic tissues by placing regions of interest (ROI) on visible areas of pleural tumour with similar ROIs placed on other thoracic tissues. Pleural volume contours were drawn on axial CT slices and propagated throughout the volume by linear interpolation using volumetric software (Myrian Intrasense® software v2.4.3 (Paris, France)). Seed points based on the radiodensity range of pleural tumour were placed on representative areas of tumour with regions grown. There were similarities in median thoracic tissue HU values: pleural tumour, 52 [IQR 46 to 60] HU; intercostal muscle, 20.4 [IQR 11.9 to 32.3] HU; diaphragm, 40.4 [IQR 26.4 to 56.4] HU and pleural fluid, 11.8 [IQR 8.3 to 17.8] HU. There was also reduced definition between MPM tumour and neighbouring structures. The mean time taken to complete semi-automated volumetric segmentations for the 8 CT scans examined was 25 (SD 7) minutes. The semi-automated CT volumes were larger than the MRI volumes with a mean difference between MRI and CT volumes of -457.6 cm3 (95% limits of agreement -2741 to +1826 cm3). The complex shape of MPM tumour and overlapping thoracic tissue HU values precluded HU threshold-based region growing and meant that semi-automated volumetry using CT was not possible in this thesis. Chapter 4 describes a multicentre retrospective cohort study that developed and validated an automated AI algorithm – termed a deep learning Convolutional Neural Network (CNN) - for volumetric MPM tumour segmentation. Due to the limitations of the semi-automated approach described in Chapter 3, manually annotated tumour volumes were used to train the CNN. The manual segmentation method ensured that all the parietal pleural tumour was included in the respective volumes. Although the manual CT volumes were consistently smaller than semi-automated MRI volumes (average difference between AI and human volumes 74.8 cm3), they were moderately correlated (Pearson’s r=0.524, p=0.0103). There was strong correlation (external validation set r=0.851, p<0.0001) and agreement (external validation set mean AI minus human volume difference of +31 cm3 between human and AI tumour volumes). AI segmentation errors (4/60 external validation set cases) were associated with complex anatomical features. There was agreement between human and AI volumetric responses in 20/30 (67%) cases. There was agreement between AI volumetric and mRECIST classification responses in 16/30 (55%) cases. Overall survival (OS) was shorter in patients with higher AI-defined pre-chemotherapy tumour volumes (HR=2.40, 95% CI 1.07 to 5.41, p=0.0114). Survival prediction in MPM is difficult due to the heterogeneity of the disease. Previous survival prediction models have not included measures of body composition which are prognostic in other solid organ cancers. In Chapter 5, I explore the impact of loss of skeletal muscle and adipose tissue at the level of the third lumbar vertebra (L3) and the loss of skeletal muscle at the fourth thoracic (T4) vertebrae on survival and response to treatment in patients with MPM receiving chemotherapy. Skeletal and adipose muscle areas at L3 and T4 were quantified by manual delineation of relevant muscle and fat groups using ImageJ software (U.S. National Institutes of Health, Bethesda, MD) on pre-chemotherapy and response assessment CT scans, with normalisation for height. Sarcopenia at L3 was not associated with shorter OS at the pre-chemotherapy (HR 1.49, 95% CI 0.95 to 2.52, p=0.077) or response assessment time points (HR 1.48, 95% CI 0.97 to 2.26, p=0.0536). A higher visceral adipose tissue index (VFI) measured at L3 was associated with shorter OS (HR 1.95, 95% CI 1.05 to 3.62, p=0.0067). In multivariate analysis, obesity was associated with improved OS (HR 0.36, 95% CI 0.20 to 0.65, p<0.001) while interval VFI loss (HR 1.81, 95% CI 1.04 to 3.13, p=0.035) was associated with reduced OS. Overall loss of skeletal muscle index at the fourth thoracic vertebra (T4SMI) during treatment was associated with poorer OS (HR 2.79, 95% CI 1.22 to 6.40, p<0.0001). Skeletal muscle index on the ipsilateral side of the tumour at the fourth thoracic vertebra (Ipsilateral T4SMI) loss was also associated with shorter OS (HR 2.91, 95% CI 1.28 to 6.59, p<0.0001). In separate multivariate models, overall T4SMI muscle loss (HR 2.15, 95% CI 102 to 4.54, p=0.045) and ipsilateral T4SMI muscle loss (HR 2.85, 95% CI 1.17 to 6.94, p=0.021) were independent predictors of OS. Response to chemotherapy was not associated with decreasing skeletal muscle or adipose tissue indices

Bone marrow Fat - A Novel Quantification Method and Potential Clinical Applications

Ageing bone is characterised by increased marrow fat infiltration altering its composition and microstructure, thus predisposing the person to osteoporosis. Yet to date, non-invasive quantifications of marrow fat are limited to special MRI techniques, and clinical studies examining marrow fat in the ageing skeleton are scarce. Thus, the key aims of this thesis are to: · Validate a new non-invasive technique of marrow fat quantification using CT technology · Determine the effects of dietary fatty acids on marrow fat · Measure marrow fat content in different skeletal regions in healthy older men · Determine the effect of exercise and calcium on marrow fat. The imaging techniques employed in our animal and human studies were micro CT (µCT) and quantitative CT (QCT) respectively. All images were analysed with the imaging software Slice O Matic version 4.1 (Tomovision). Regions of interest [ROIs] were Volumes of interests (VOIs) of bone, fat and blood measured in µm3 or mm3. Individual tissue volumes, expressed as percentages of the total marrow volume, and ratios of tissue volumes were also used in the analysis. Global and local thresholds for individual tissue volumes were determined separately for µCT and QCT. Thresholds for µCT were those derived from the initial validation study, whereas those for QCT were based on previous published data. To account for partial volume averaging effects, further manual refinement of threshold ranges were undertaken by inspection of individual pixels and their neighbours. This manual process was carried out for both µCT and QCT to derive local thresholds for use in manual segmentation and computation of volumes. Our validation study showed that quantification of marrow fat using µCT was reliable and accurate compared to the gold standard technique- histology- when reliably defined thresholds were used. Good agreement between tissue volumes measured by histology and those computed by the imaging software was demonstrated. We applied this technique to quantify marrow fat in an animal model of senile osteoporosis, and showed that fatty acids (ω- 3 and ω-6) had dual effects on bone. With QCT studies, we confirmed the age related increase in marrow adiposity, and more significantly, different ratios between fat and bone in common fracture regions. Similarly, exercise affects marrow fat differently in different regions, and there was a trend to statistically significant changes to marrow fat with exercise. In conclusion, this body of work showed that quantification of marrow fat using CT is promising, and has future clinical implications. However, significantly more clinical studies are needed to confirm these findings and refine shortfalls in quantification capabilities

Detection of osteoporosis in lumbar spine [L1-L4] trabecular bone: a review article

The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy where as the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. This analysis is on the basis of bone mineral density (BMD) standards obtained through a variety of scientific methods experimented from different skeletal regions. The detection of osteoporosis in lumbar spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. This paper focuses on the advanced technology in imaging systems and fracture probability analysis of osteoporosis detection. The various segmentation techniques are explored to examine osteoporosis in particular region of the image and further significant attributes are extracted using different methods to classify normal and abnormal (osteoporotic) bones. The limitations of the reviewed papers are more in feature dimensions, lesser accuracy and expensive imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and DEXA. To overcome these limitations it is suggested to have less feature dimensions, more accuracy and cost-effective imaging modality like X-ray. This is required to avoid bone fractures and to improve BMD with precision which further helps in the diagnosis of osteoporosis

MRI assessment of changes in adipose tissue parameters after bariatric surgery

Bariatric surgery and other therapeutic options for obese patients are often evaluated by the loss of weight, reduction of comorbidities or improved quality of life. However, little is currently known about potential therapy-related changes in the adipose tissue of obese patients. The aim of this study was therefore to quantify fat fraction (FF) and T1 relaxation time by magnetic resonance imaging (MRI) after Roux-en-Y gastric bypass surgery and compare the resulting values with the preoperative ones. Corresponding MRI data were available from 23 patients (16 females and 7 males) that had undergone MRI before (M0) and one month after (M1) bariatric surgery. Patients were 22-59 years old (mean age 44.3 years) and their BMI ranged from 35.7-54.6 kg/m(2) (mean BMI 44.6 kg/m(2)) at M0. Total visceral AT volumes (VVAT-T, in L) were measured by semi-automatic segmentation of axial MRI images acquired between diaphragm and femoral heads. MRI FF and T1 relaxation times were measured in well-defined regions of visceral (VAT) and subcutaneous (SAT) adipose tissue using two custom-made analysis tools. Average BMI values were 45.4 kg/m(2) at time point M0 and 42.4 kg/m(2) at M1. Corresponding VVAT-T values were 5.94 L and 5.33 L. Intraindividual differences in both BMI and VVAT-T were highly significant (p<0.001). Average relaxation times T1 VAT were 303.7 ms at M0 and 316.9 ms at M1 (p<0.001). Corresponding T1(SAT) times were 283.2 ms and 280.7 ms (p = 0.137). Similarly, FFVAT differences (M0: 85.7%, M1: 83.4%) were significant (p <0.01) whereas FFSAT differences (M0: 86.1, M1: 85.9%) were not significant (p = 0.517). In conclusion, bariatric surgery is apparently not only related to a significant reduction in common parameters of adipose tissue distribution, here BMI and total visceral fat volume, but also significant changes in T1 relaxation time and fat fraction of visceral adipose tissue. Such quantitative MRI measures may potentially serve as independent biomarkers for longitudinal and cross-sectional measurements in obese patients