Improved Supervised Training of Physics-Guided Deep Learning Image Reconstruction with Multi-Masking
This is an incremental improvement for MRI image reconstruction, addressing robustness in supervised training for medical imaging applications.
The paper tackled the problem of improving image reconstruction in MRI by enhancing supervised training of physics-guided deep learning (PG-DL) methods, and the result was that the proposed multi-mask approach enhanced reconstruction performance compared to conventional methods, as shown in knee MRI results.
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.