Magnetic resonance imaging of obesity and metabolic disorders: Summary from the 2019 ISMRM Workshop

More than 100 attendees from Australia, Austria, Belgium, Canada, China, Germany, Hong Kong, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Republic of Korea, Singapore, Sweden, Switzerland, the United Kingdom, and the United States convened in Singapore for the 2019 ISMRM-sponsored workshop on MRI of Obesity and Metabolic Disorders. The scientific program brought together a multidisciplinary group of researchers, trainees, and clinicians and included sessions in diabetes and insulin resistance; an update on recent advances in water–fat MRI acquisition and reconstruction methods; with applications in skeletal muscle, bone marrow, and adipose tissue quantification; a summary of recent findings in brown adipose tissue; new developments in imaging fat in the fetus, placenta, and neonates; the utility of liver elastography in obesity studies; and the emerging role of radiomics in population-based “big data” studies. The workshop featured keynote presentations on nutrition, epidemiology, genetics, and exercise physiology. Forty-four proffered scientific abstracts were also presented, covering the topics of brown adipose tissue, quantitative liver analysis from multiparametric data, disease prevalence and population health, technical and methodological developments in data acquisition and reconstruction, newfound applications of machine learning and neural networks, standardization of proton density fat fraction measurements, and X-nuclei applications. The purpose of this article is to summarize the scientific highlights from the workshop and identify future directions of work

A method for body fat composition analysis in abdominal magnetic resonance images via self-organizing map neural network

Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in axial magnetic resonance (MR) images of the abdomen. Materials and Methods: A self-organizing map (SOM) neural network was designed to segment the adipose tissue from other tissues in the MR images. The segmentation of SAT and VAT was accomplished using a new level set method called distance regularized level set evolution (DRLSE). To evaluate the suggested method, the whole-body abdominal MRI was performed on 23 subjects, and three slices were selected for each case. Results: The results of the automatic segmentation were compared with those of the manual segmentation and previous artificial intelligent methods. According to the results, there was a significant correlation between the automatic and manual segmentation results of VAT and SAT. Conclusion: As the findings indicated, the suggested method improved detection of body fat. In this study, a fully automated abdominal adipose tissue segmentation algorithm was suggested, which used the SOM neural network and DRLSE level set algorithm. The proposed methodology was concluded to be accurate and robust with a significant advantage over the manual and previous segmentation methods in terms of speed and accuracy. © 2018, Mashhad University of Medical Sciences

Phenotyping Ethnic Differences in Body Fat Depots

There are remarkable ethnic differences in the incidence of metabolic syndrome associated features; including insulin resistance, type 2 diabetes, hypertension and cardiovascular diseases. Studies have suggested that South Asians (SA) present an unfavourable body fat phenotype, which includes a pattern of elevated visceral adipose tissue (VAT), and liver fat content; depots strongly associated with the progression of metabolic dysregulation. However, there are a limited number of studies examining body fat composition by ethnicity. The purpose of this thesis was to comprehensively phenotype VAT, abdominal subcutaneous adipose tissue (ASAT) and liver fat content in Caucasian (Cau), SA and Black African (BA) individuals from a large number of distinct populations. Here, I include data from three adult cohorts: the UK Biobank (n=9533) of mixed ethnicities, the DIRECT cohort (n=1553) of Cau pre-diabetic individuals and The West London Observation (TWLO) cohort (n=747) of mixed ethnicities. In addition, I present data from Pune Maternal Nutrition study (PMNS) cohort; comprising 423 young adults of SA descent in India. Analyses of body fat phenotype in Cau pre-diabetic populations showed higher VAT (mean differences= 0.5 litre, p<0.0001) and liver fat content (mean differences= 0.6%, p<0.0001), but lower ASAT (mean differences= -0.2 litre, p<0.0001) compared to Cau from the general population (free-living). I also observed negative associations between VAT, ASAT, liver fat content and day to day physical activity in both pre-diabetic and general populations (pre-diabetic; VAT; r= -0.296, ASAT; r= -0.163, liver fat: r= -0.186 and general population; VAT; r= -0.185, ASAT: r= -0.374, liver fat: r= -0.139, p<0.001 for all). Analysis of both the TWLO and UK Biobank revealed no differences in VAT or liver fat in SA in UK compared to other ethnic groups (TWLO; VAT: SA: 3.0 ± 1.6 litres, Cau: 3.3 ± 2.1 litres; liver fat: SA= 6.4 ± 11.1%, Cau= 6.5 ± 13.6%, p=ns - UK Biobank; VAT: SA: 3.6 ± 1.6 litres, Cau: 3.8 ± 1.5 litres; liver fat: SA: 4.6 ± 4.6%, Cau: 4.2 ± 4.6%, p=ns). Analysis of both these cohorts also revealed a more favourable body fat phenotype with BA males presenting significantly less VAT than SA and Cau males (p<0.05 for both). Data from the PMNS cohort revealed high levels of VAT in 18 year old India-based SA population. A high proportion (58.7%) of these lean individuals also presented with the thin-outside fat inside (TOFI) phenotype (a ratio of VAT to ASAT). A key finding is the lack of an unfavourable body fat phenotype in UK based SA. Therefore, the increased incidence of metabolic syndrome associated features in the SA population may arise via a mechanism unrelated to elevated levels of VAT or liver fat

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Nonalcoholic Fatty Liver Disease in Obesity and Type 2 Diabetes - Studies using 1H MRS and PET

Siirretty Doriast

MECHANICAL METRICS OF THE PROXIMAL FEMUR ARE PRECISE AND ASSOCIATED WITH HIP MUSCLE PROPERTIES: A MAGNETIC RESONANCE BASED FINITE ELEMENT STUDY

Proximal femoral (hip) fractures are a life-threatening injury which affects 30,000 Canadians annually. Improved muscle and bone strength assessment methods may reduce fracture occurrence rates in the future. Magnetic resonance (MR) imaging has potential to assess proximal femoral bone strength in vivo through usage of finite element (FE) modeling. Though, to precisely assess bone strength, knowledge of a technique’s measurement error is needed. Hip muscle properties (e.g., lean muscle and fat area) are intrinsically linked to proximal femoral bone strength; however, it is unclear which muscles and properties are most closely associated with bone strength. This thesis is focused on MR-based FE modeling (MR-FE) of the proximal femur and surrounding muscle properties (e.g., hip abductor fat area, hip extensor muscle area). The specific objectives of this research were 1) to characterize the short-term in vivo measurement precision of MR-FE outcomes (e.g., failure load) of the proximal femur for configurations simulating fall and stance loading, and 2) explore associations between upper thigh muscle and fat properties (e.g., hip abductor fat area, knee extensor muscle area) with MR-FE failure loads of the proximal femur. In vivo precision errors (assessed via root mean square coefficient of variation, CV%RMS from repeated measures) of MR-FE outcomes ranged from 3.3-11.8% for stress and strain outcomes, and 6.0-9.5% for failure loads. Hip adductor muscle area and total muscle area correlated with failure load of the fracture-prone neck and intertrochanteric region under both fall and stance loading (correlation coefficients ranged from 0.416-0.671). This is the first study to report the in vivo short-term precision errors of MR-FE outcomes at the proximal femur. Also, this is the first study to relate upper-thigh muscle and fat properties with MR-FE derived failure loads. Results indicate that MR-FE outcomes have comparable precision to computed tomography (CT) based FE outcomes and are related to hip muscle area

CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions

Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular (LV) coverage in a single breath-hold. However, 3D imaging remains limited to anisotropic resolution and long reconstruction times. Recently deep learning has shown promising results for computationally efficient reconstructions of highly accelerated 2D CINE imaging. In this work, we propose a novel 4D (3D + time) deep learning-based reconstruction network, termed 4D CINENet, for prospectively undersampled 3D Cartesian CINE imaging. CINENet is based on (3 + 1)D complex-valued spatio-temporal convolutions and multi-coil data processing. We trained and evaluated the proposed CINENet on in-house acquired 3D CINE data of 20 healthy subjects and 15 patients with suspected cardiovascular disease. The proposed CINENet network outperforms iterative reconstructions in visual image quality and contrast (+ 67% improvement). We found good agreement in LV function (bias ± 95% confidence) in terms of end-systolic volume (0 ± 3.3 ml), end-diastolic volume (- 0.4 ± 2.0 ml) and ejection fraction (0.1 ± 3.2%) compared to clinical gold-standard 2D CINE, enabling single breath-hold isotropic 3D CINE in less than 10 s scan and ~ 5 s reconstruction time