UCLA

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease with a current global prevalence of 25% to 40%. MASLD is associated with the metabolic syndrome and cardiovascular morbidity, and can progress to fibrosis and cirrhosis. Chronic liver diseases such as viral hepatitis and MASLD can also lead to hepatic iron overload. Magnetic resonance imaging (MRI) provides non-invasive evaluation of hepatic steatosis and iron overload by quantifying proton-density fat fraction (PDFF) and R2*. Conventional MRI techniques for liver PDFF and R2* quantification require breath-holding, which can be challenging for children and elderly patients. 3D stack-of-radial MRI techniques have been proposed for self-gated free-breathing liver PDFF and R2* quantification. However, several challenges remain, including residual streaking artifacts from system imperfections, long scan acquisition times, computationally expensive reconstructions, and insufficient modelling of non-rigid liver motion during free-breathing. Techniques to overcome these challenges are important for a wide clinical adoption of free-breathing MRI techniques for liver PDFF and R2* quantification. Additionally, in recent years, there has been an increased interest in lower-field MRI systems. A less expensive lower-field MRI system with a larger bore diameter may improve accessibility and comfort for populations with obesity and at risk for fatty liver diseases. However, the low signal-to-noise ratio problem can impact image quality and quantification accuracy. Therefore, noise reduction techniques are important to improve liver PDFF and R2* quantification in lower-field MRI systems.

This work focuses on developing MRI reconstruction techniques to improve liver PDFF and R2* quantification. First, this work developed a phase-preserving beamforming-based technique to effectively reduce radial streaking artifacts from system imperfections. This technique can be further integrated with motion-resolved reconstruction to improve self-gated free-breathing liver PDFF and R2* quantification. Second, this work developed an uncertainty-aware physics-driven deep learning network for rapid reconstruction of PDFF and R2* maps from self-gated free-breathing MRI. The uncertainty maps generated from the network can be used to predict quantification errors and improve reliability of deep learning reconstruction results. Third, this work developed a compressed sensing reconstruction model with non-rigid motion compensation to improve and accelerate self-gated free-breathing liver PDFF and R2* quantification. Last, this work developed and evaluated image and k-space denoising techniques that can improve quantification accuracy and precision of Cartesian-based liver PDFF and R2* quantification at 0.55T. These technical advancements can provide accurate and motion-robust liver fat and R2* quantification.