Keywords: AI/ML Image Reconstruction, Image Reconstruction
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.
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