Motivation: Most research on accelerated MRI techniques focuses on improving overall image quality at the voxel level, neglecting specific abnormalities that are clinically significant. Goal(s): This work achieves accelerated MRI reconstruction in a weakly supervised setting while also localizing the lesion areas and improving the reconstruction quality at those lesion locations. Approach: We built a task-specific MRI reconstruction model that includes customized learnable regularization, which is solved by unfolding into a network using alternating optimization. Results: Tests on public medical datasets show that our method significantly outperforms current benchmark approaches and demonstrates substantial improvements in the field of pathology. Impact: In a weakly supervised setting, our method uses only MRI image-level labels to achieve accelerated MRI reconstruction while localizing the lesion areas and improving their reconstruction quality. This has significant implications for clinical applications.
Ju et al. (Tue,) studied this question.