Abstract #2643

Highly-Accelerated, Free-Breathing, Time-Resolved 4D Golden-Angle Radial MRI with Self-Supervised Learning

Haoyang Pei^1,2,3, Ding Xia⁴, Yao Wang³, Hersh Chandarana^1,2, Daniel K Sodickson^1,2, and Li Feng^1,2

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ²Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ³Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, United States, ⁴Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, United States

Synopsis

Keywords: AI/ML Image Reconstruction, Image Reconstruction, Dynamic Imaging

Motivation: Time-resolved 4D MRI with sub-second temporal resolution is a robust technique for free-breathing imaging, while standard iterative reconstruction for time-resolved 4D MRI requires long acquisition times and high computational demand.

Goal(s): This work proposes DeepGrasp, a self-supervised learning-based approach for high-quality free-breathing, time-resolved 4D MRI reconstruction with shortened scan times and reconstruction speed.

Approach: DeepGrasp was developed based on self-supervised learning using an unrolled network that incorporates a low-rank subspace model-assisted training strategy and a temporal total variation constraint, enabling improved image reconstruction quality and training/inference speed.

Results: DeepGrasp enables accurate 4D MRI reconstruction at high acceleration rates and fast reconstruction speed

Impact: The proposed DeepGrasp technique allows for shorten data acquisition and efficient image reconstruction without requiring reference images for network training, providing significant potential for different clinical applications such as DCE-MRI or MRI-guided radiotherapy.

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