Quantitative Maps Synthesis from Magnetic Resonance Fingerprinting via Physical-guided Deep Generative Model

Motivation: Quantitative MRI techniques like MRF provide multi-parametric maps, but traditional dictionary-based methods face issues with model simplifications and quantification errors, resulting in parametric maps that often lack consistency with clinically relevant weighted contrast images. Goal(s): Our goal is to generate consistent quantitative maps with clinically applied weighted images from MRF sequences and extend to produce other weighted images. Approach: We proposed a physical-guided generative models with GAN to connect the quantitative maps to the weighted images. Results: This model generates accurate quantitative maps from MRF series by leveraging physical model constraints between quantitative maps and weighted images and can achieve other weighted images. Impact: Our proposed MRF sequence-based quantitative map generation model produces quantitative maps that better capture clinically relevant contrast details. It also enables the calculation of various weighted images from these more accurate quantitative maps, supporting more comprehensive clinical diagnosis.