MARVEL: Motion-Aware Reconstruction Via Embedded Learning of Motion Prior for Time-Resolved Cardiac CT

Abstract

Cardiac CT provides comprehensive structural and functional heart imaging, but motion artifacts remain a fundamental challenge. Although modern scanners have improved temporal resolution through hardware advancements and electrocardiogram-gated scan protocols, cardiac CT is still limited to specific cardiac phases and may fail in clinical practice. Here, to overcome this challenge, we propose the MARVEL (Motion-Aware Reconstruction Via Embedded Learning of motion prior) framework for time-resolved cardiac CT imaging. The MARVEL synergistically integrates a powerful data-driven model (MPNet) with learned Motion Prior into a robust model-based reconstruction process. This hybrid design preserves the interpretability and flexibility of model-based reconstruction methods, while benefiting from the high performance and computational efficiency of data-driven approaches. Specifically, the proposed MP-Net with specially designed mixed spatiotemporal convolutions extracts motion information from 4D pre-reconstructed images. Through an embedded learning strategy, the model learns to predict a reconstruction-oriented cardiac motion vector field (MVF). Then, a motion-aware reconstruction is implemented according to the MVF to compensate for cardiac motion. As a result, the MARVEL achieves effective reduction of motion artifacts throughout the entire heart and breaks through the conventional phase limitations of cardiac CT imaging. Furthermore, MARVEL integrates seamlessly into standard CT workflows, facilitating its potential for clinical application. Extensive evaluations, including qualitative and quantitative assessments on both simulated and clinical datasets, as well as blinded reader studies, demonstrate MARVEL's superiority over the comparison methods. Code and dynamic reconstruction demos will be available at https: //github.com/ZihengD/MARVEL_CardiacCT.