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

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.