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Abstract #4133

Multi-Frame Compensated Super-Resolution for High Spatiotemporal Abdominal 4D-MRI

Yinghui Wang1, Lu Wang1, Tian Li1, and Jing Cai1
1The Hong Kong Polytechnic University, Hong Kong, Hong Kong

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, 4D-MRI\Super-resolution\Spatiatemporal Information

Motivation: Four-dimensional MRI (4D-MRI) holds significant potential for abdominal radiotherapy, yet it faces a persistent trade-off between spatial and temporal resolution, often leading to undersampled images with motion artifacts.

Goal(s): Existing super-resolution models struggle to recover fine details under these conditions. We introduce MCRNet, a multi-frame compensated network designed to enhance abdominal 4D-MRI by leveraging frame redundancy across respiratory cycles.

Approach: MCRNet integrates two key modules: the Frame Synergy Attention Module (FSAM) and the Structure-Aware Consolidation Module (SaCM), which together enhance anatomical feature extraction while suppressing artifacts.

Results: Comprehensive experiments demonstrate that MCRNet surpasses state-of-the-art methods, effectively restoring anatomical features with minimal artifacts.

Impact: MCRNet significantly improves 4D-MRI quality by achieving high spatiotemporal resolution, reducing noise and artifacts, and restoring anatomical structures.

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Keywords