Improved Supervised Training of Physics-Guided Deep Learning Image Reconstruction with Multi-Masking

TL;DR

This paper introduces a multi-masking approach for supervised physics-guided deep learning in MRI image reconstruction, which improves robustness and performance by randomly selecting measurement subsets during training.

Contribution

It proposes a novel multi-masking training strategy that enhances the robustness and accuracy of physics-guided deep learning models for MRI reconstruction.

Findings

Multi-masking improves reconstruction quality over traditional methods.

Random measurement subset selection enhances model robustness.

Results demonstrate better performance on knee MRI data.

Abstract

Physics-guided deep learning (PG-DL) via algorithm unrolling has received significant interest for improved image reconstruction, including MRI applications. These methods unroll an iterative optimization algorithm into a series of regularizer and data consistency units. The unrolled networks are typically trained end-to-end using a supervised approach. Current supervised PG-DL approaches use all of the available sub-sampled measurements in their data consistency units. Thus, the network learns to fit the rest of the measurements. In this study, we propose to improve the performance and robustness of supervised training by utilizing randomness by retrospectively selecting only a subset of all the available measurements for data consistency units. The process is repeated multiple times using different random masks during training for further enhancement. Results on knee MRI show that the proposed multi-mask supervised PG-DL enhances reconstruction performance compared to conventional supervised PG-DL approaches.