DeepRelaxo: Fast Mono-Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self-Supervised Deep Learning.

PURPOSE: We introduce DeepRelaxo, a fast and generalizable deep learning method for estimating brain R2* maps from multi-echo gradient echo (ME-GRE) acquisitions with arbitrary echo configurations, including shortened echo trains for accelerated scans. METHODS: DeepRelaxo is a cascaded two-stage self-supervised network comprising: (1) a voxel-wise Transformer-MLP for initial R2* estimation, and (2) a patch-based 3D U-Net for denoising. Both stages are trained entirely on synthetic ME-GRE data simulated at 3 T with a varied number of echoes, echo times, and noise levels. We evaluate on simulated and in vivo brain datasets, comparing it against conventional non-linear least squares (NLLS) and the standalone Transformer-MLP. Experiments assess robustness under increased noise and shortened TEs. RESULTS: In simulations, DeepRelaxo consistently outperforms NLLS and Transformer-MLP, particularly in accelerated conditions. For example, with 4× scan time reduction at low SNR (= 10), DeepRelaxo improves SSIM by 13.5% and reduces RMSE by 76% compared with baseline methods. In in vivo 3 T and 7 T data, DeepRelaxo produces consistent R2* values in deep gray matter and preserves anatomical detail, even with only two short echoes. CONCLUSION: DeepRelaxo effectively models ME-GRE decay, leveraging temporal and spatial context to deliver accurate, robust, and computationally efficient R2* mapping. It enables reliable reconstruction under accelerated protocols, making it suitable for time-sensitive workflows.