Fully Automated Segmentation and Quantification of Abdominal Adipose Tissue Compartments in Mouse MRI

Obesity currently affects 25% of Canadians and is strongly associated with many diseases including diabetes, cardiovascular disease, and cancer. However, only Intra-Abdominal Adipose Tissue (IAAT) is an important predictor of obesity associated disease and mortality. Magnetic Resonance Imaging (MRI) is an effective modality for imaging fat and methods have been developed to automate segmentation of IAAT. Currently existing techniques for automated segmentation in mice require acquisition using high magnetic field MRI equipment and use image acquisition techniques with low precision for fat quantification. We demonstrate a new fully automated technique for fat quantification in clinical strength mouse MRI through adaptation of an existing human fat quantification technique. We validated this method using images collected from mice in a 3 T clinical MRI against manual segmentation. Dice correlation coefficients revealed that 84% of voxels agreed for Subcutaneous Adipose Tissue (SAT) and 87% of voxels agreed for IAAT

Abdominal adipose tissue and liver fat imaging in very low birth weight adults born preterm : birth cohort with sibling-controls

Preterm birth at very low birth weight (VLBW, < 1500 g) is associated with an accumulation of cardiovascular and metabolic risk factors from childhood at least to middle age. Small-scale studies suggest that this could partly be explained by increased visceral or ectopic fat. We performed magnetic resonance imaging on 78 adults born preterm at VLBW in Finland between 1978 and 1990 and 72 term same-sex siblings as controls, with a mean age of 29 years. We collected T1-weighted images from the abdomen, and magnetic resonance spectra from the liver, subcutaneous abdominal adipose tissue, and tibia. The adipose tissue volumes of VLBW adults did not differ from their term siblings when adjusting for age, sex, and maternal and perinatal factors. The mean differences were as follows: subcutaneous - 0.48% (95% CI - 14.8%, 16.3%), visceral 7.96% (95% CI - 10.4%, 30.1%), and total abdominal fat quantity 1.05% (95% CI - 13.7%, 18.4%). Hepatic triglyceride content was also similar. VLBW individuals displayed less unsaturation in subcutaneous adipose tissue (- 4.74%, 95% CI - 9.2%, - 0.1%) but not in tibial bone marrow (1.68%, 95% CI - 1.86%, 5.35%). VLBW adults displayed similar adipose tissue volumes and hepatic triglyceride content as their term siblings. Previously reported differences could thus partly be due to genetic or environmental characteristics shared between siblings. The VLBW group displayed less unsaturation in subcutaneous abdominal adipose tissue, suggesting differences in its metabolic activity and energy storage.Peer reviewe

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

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

Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks

Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Therefore an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known hounsfield unit limits. The S{\o}rensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Our results show that fully-automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analysing body composition in the clinical routine