Model-unrolled fast MRI with weakly supervised lesion enhancement.
Authors
Affiliations (5)
Affiliations (5)
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China; Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China; Research Center for Intelligent Medical Equipment and Devices (IMED), Xi'an Jiaotong University, Xi'an 710049, China. Electronic address: [email protected].
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China; Research Center for Intelligent Medical Equipment and Devices (IMED), Xi'an Jiaotong University, Xi'an 710049, China. Electronic address: [email protected].
Abstract
The utility of Magnetic Resonance Imaging (MRI) in anomaly detection and disease diagnosis is well recognized. However, the current imaging protocol is often hindered by long scanning durations and a misalignment between the scanning process and the specific requirements of subsequent clinical assessments. While recent studies have actively explored accelerated MRI techniques, the majority have concentrated on improving overall image quality across all voxel locations, overlooking the attention to specific abnormalities that hold clinical significance. To address this discrepancy, we propose a model-unrolled deep-learning method, guided by weakly supervised lesion attention, for accelerated MRI oriented by downstream clinical needs. In particular, we construct a lesion-focused MRI reconstruction model, which incorporates customized learnable regularizations that can be learned efficiently by using only image-level labels to improve potential lesion reconstruction but preserve overall image quality. We then design a dedicated iterative algorithm to solve this task-driven reconstruction model, which is further unfolded as a cascaded deep network for lesion-focused fast imaging. Comprehensive experiments on two public datasets, i.e., fastMRI and Stanford Knee MRI Multi-Task Evaluation (SKM-TEA), demonstrate that our approach, referred to as Lesion-Focused MRI (LF-MRI), surpassed existing accelerated MRI methods by relatively large margins. Remarkably, LF-MRI led to substantial improvements in areas showing pathology. The source code and pretrained models will be publicly available at https://github.com/ladderlab-xjtu/LF-MRI.