Synthesizing Proton-Density Fat Fraction and \(R_2^\) from 2-point Dixon MRI with Generative Machine Learning

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 to demonstrate the first large-scale \(R_2^*\) imputation from two-point Dixon MRIs. Using our proposed approach, we synthesize PDFF and \(R_2^*\) maps that show significantly greater correlation with ground-truth than conventional voxel-wise baselines.