Keywords: AI/ML Image Reconstruction, Data Processing
Motivation: For magnetic resonance imaging (MRI) applications, rapid imaging and automatic segmentation of target tissues are critical. However, most existing methods barely consider MR image segmentation in fast imaging scenarios.
Goal(s): Our goal is to simultaneously achieve high scanning acceleration and accurate multi-class tissue segmentation results under a unified framework.
Approach: We propose a novel multi-task method with a novel interaction module to reconstruct undersampled MR images based on modified ISTA-Net and simultaneously segment tissues based on lightweight U-Net.
Results: Experiments on cardiac and knee datasets demonstrate that our method outperforms existing state-of-the-art multi-task approaches for joint MR image reconstruction and segmentation.
Impact: The proposed multi-task interaction method can effectively achieve high scanning acceleration and accurate segmentation results simultaneously, which can further expand the application of MR in clinical disease diagnosis.
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