Model-Assisted Deep Learning-Based Reconstruction of Accelerated Golden-Angle Radial Data for Free-Breathing Dynamic Contrast-Enhanced MRI

Motivation: GRASP allows for free-breathing DCE-MRI with high spatial and temporal resolution. However, the current 4D iterative reconstruction is slow and still suffers from streaking artifacts, limiting clinical use. Goal(s): Develop a DL solution that significantly reduces the reconstruction time and improves image quality. Approach: A model-assisted DL reconstruction combining a sparsity model with an efficient 3D spatiotemporal network for fast and robust reconstruction of accelerated scans with high resolution. Results: A sparsity-constrained DL-based can provide robust and fast reconstructions with improved image quality, evidenced by the superior quantitative metrics and the qualitative analysis of cases under-represented in the training data. Impact: GRASP offers high-resolution 4D free-breathing DCE-MRI; however, it still suffers from under-sampling artifacts and long reconstruction times. A model-assisted DL reconstruction can reduce the reconstruction time, improve image quality, and increase system robustness—essential in translating to clinical practice.