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Abstract #1848

RAUQ-4DRecon:A Robust Anatomy-based Ultra-Quality 4D MRI reconstruction workflow

peilin wang1, Weiwei Liu2, Weihu Wang2, Hao Wu2, Joseph Lai1, Yibao Zhang2, Jie Deng3, Jing Cai1, and Tian Li1
1the Hong Kong Polytechnic University, Hong Kong, Hong Kong, 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China, 3the University of Texas Southwestern Medical Center, Dallas, TX, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, AI/ML Image Reconstruction

Motivation: Current deep-learning and prior-image-based four-dimensional MRI (4D-MRI) reconstruction methods lack generalizability, limiting its clinical adoption.

Goal(s): To develop a generalizable deep-learning-based 4D-MRI reconstruction workflow.

Approach: We propose RAUQ-4DRecon, a workflow trained on XCAT-generated data with diverse contrasts and physiological parameters. Structural innovations include a segmentation-guided vertebra affine alignment network and a deformable motion estimation hypernetwork to capture respiratory motion of commercially acquired 4D-MRI and transfer it to pre-acquired ultra-quality 3D-MRI.

Results: RAUQ-4DRecon surpassed VoxelMorph and pTV on all XCAT validation metrics. In real-patient external validation, RAUQ-4DRecon achieved NMI of 0.382±0.071 and vertebra stability (LCC) of 0.347±0.061, compared to DDEM’s 0.328±0.085 and 0.275±0.082, respectively.

Impact: This study addresses data limitations in training 4D-MRI reconstruction workflows, significantly enhancing generalizability of 4D-MRI and expanding its potential clinical applications across various medical settings. The digital phantom-based training procedure also offers valuable insights for other medical image processing tasks.

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Keywords