Neural Bloch-McConnell fitting (NBMF): unsupervised test-time learning of clinical semisolid MT/CEST MRF reconstruction

Motivation: MRF-based quantification of semi-solid MT/CEST proton-exchange requires a computationally demanding dictionary synthesis/matching. Recently reported unsupervised learning alternatives were incompatible with pulsed clinical CEST and multi-pool imaging. Goal(s): To develop a training-set-free MRF reconstruction method, learning directly from the acquired data via pulsed-saturation-compatible physical modeling. Approach: A differentiable multi-pool Bloch-McConnel simulator was designed and embedded within a test-time learning framework. Validation was performed using L-arginine phantoms and a human subject at 3T. Results: The method enabled quantitative MT/CEST reconstruction in ~1 minute. The resulting maps were highly correlated with ground-truth in-vitro (Pearson’s r>0.95). In-vivo, semi-solid volume fractions were in agreement with MRF-based maps (r~0.8). Impact: A one-stop-shop for semisolid MT and CEST MRF reconstruction was developed, enabling a training-set-free rapid quantification of exchange parameters on clinical scanners. This accessible approach could help a variety of Bloch-fitting applications to benefit from deep learning through differentiable spin-physics.