| Paper ID | BIO-7.1 |
| Paper Title |
IMPROVED SUPERVISED TRAINING OF PHYSICS-GUIDED DEEP LEARNING IMAGE RECONSTRUCTION WITH MULTI-MASKING |
| Authors |
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller, Mehmet Akcakaya, University of Minnesota, United States |
| Session | BIO-7: Medical Image Formation and Reconstruction |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [CIS-MI] Medical Imaging: Image formation, reconstruction, restoration |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
