Abstract

Motivation: Joint MAPLE is an MR parameter mapping technique with improved results which suffers from long processing times. Goal(s): We propose a fast version of Joint MAPLE as a self-supervised, model-based multi-parameter mapping technique capable of jointly mapping T1, T2*, frequency and proton density in a whole brain volume ~50 times faster than the original version, while retaining its parameter mapping performance. Approach: A fast whole brain reconstruction, transfer learning and a rapid initialization in optimization is incorporated. Results: Results show that fast Joint MAPLE retains the mapping performance of the original version and outperforms existing methods. Impact: Fast Joint MAPLE estimates T1, T2*, frequency and proton density of a volume ~50 times faster than the original version with the same performance. A fast volume reconstruction, transfer learning and a rapid initialization is incorporated for faster mapping.