Synthesizing Proton-Density Fat Fraction and $R_2^{\ast}$ from 2-Point Dixon MRI with Generative Machine Learning

作者：Suma Anand, Kaiwen Xu, Colm O'Dushlaine, Sumit Mukherjee · 年份：2025 · DOI：10.1109/isbi60581.2025.10981042 · 被引用次数：1 · 研究领域：Cardiovascular Disease and Adiposity、Advanced MRI Techniques and Applications、Radiomics and Machine Learning in Medical Imaging

Magnetic Resonance Imaging (MRI) is the gold standard for measuring fat and iron content non-invasively in the body via measures known as Proton Density Fat Fraction (PDFF) and$R_2^*$, respectively. However, conventional PDFF and$R_2^*$quantification methods operate on MR images voxel-wise and require at least three measurements to estimate three quantities: water, fat, and$R_2^*$. Alternatively, the two-point Dixon MRI protocol is widely used and fast because it acquires only two measurements; however, these cannot be used to estimate three quantities voxel-wise. Leveraging the fact that neighboring voxels have similar values, we propose using a generative machine learning approach to learn PDFF and$R_2^*$from Dixon MRI. We use paired Dixon-IDEAL data from UK Biobank in the liver and a Pix2Pix conditional GAN [1] to demonstrate the first large-scale$R_2^*$imputation from two-point Dixon MRIs. Using our proposed approach, we synthe-size PDFF and$R_2^*$maps that show significantly greater correlation with ground-truth than conventional voxel-wise baselines.