UCLA

Obesity affects around 2.1 billion people all over the world and is associated with type 2 diabetes, cardiovascular diseases, and metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty-liver disease (NAFLD). MASLD is defined as abnormal fat accumulation in the liver and can range from simple steatosis to steatohepatitis and fibrosis. Magnetic resonance imaging (MRI) is a non-invasive imaging modality that can quantify subcutaneous (SAT) and visceral adipose tissue (VAT). Increased SAT and VAT are highly associated with cardiometabolic diseases, and particularly MASLD. Conventional techniques to quantify SAT and VAT on MRI require manual annotations, which are time-consuming, not suitable for large-scale studies, and may suffer from intra/inter-rater variability. Although several deep-learning methods were proposed to automatically segment SAT/VAT, serious challenges remain including inability to fully capture detailed and disconnected VAT features, no studies performed in longitudinal MRI, not focusing on children with wide age ranges, or not using a more child-appropriate acquisition scheme such as free-breathing (FB) abdominal MRI. The first part of this thesis focuses on advanced deep-learning techniques to accurately and automatically segment SAT and VAT in longitudinal MRI in adults with obesity. Then SAT and VAT are also automatically segmented in children using the images from FB-MRI based on a motion-robust stack-of-radial sampling trajectory. These technical developments could enable detection and monitoring of risk for MASLD before the disease progresses into more severe forms. Additionally, MR elastography (MRE) can quantify liver stiffness, which is a biomarker to detect a more severe effect of MASLD: liver fibrosis. However conventional MRE requires breath-holding to mitigate motion artifacts, which could be particularly challenging in people with decreased lung capacity, the elderly, and children. In the second part of this thesis, we developed and evaluated a FB radial MRE tool that employed rapid encoding to reduce the scan time, fractional encoding to have sufficient SNR in the liver, and self-gating to compensate for breathing motion. The proposed radial FB-MRE could accurately quantify liver stiffness with acceptable repeatability when compared to conventional breath-holding (BH) MRE. Radial FB-MRE, which generates liver stiffness in excellent agreement with the conventional BH-MRE, is a promising tool that quantifies liver stiffness and can help sensitive populations such as children, where BH is challenging or not possible/feasible.