Keywords: Image Reconstruction, Atherosclerosis, Plaque, Black-blood MRI, Trajectory
Motivation: The two-shot SNAP MRI is effective for carotid plaque diagnosis with extended scan time. To accelerate the scan, under-sampling reconstruction and optimization of sampling locations are considered.
Goal(s): To optimize the sampling masks for IR-TFE and REF-TFE of SNAP MRI respectively and to reconstruct the under-sampled images with higher quality.
Approach: After the parameterization of ky-kz sampling locations for the two shots, a model-based deep learning framework was utilized to achieve the goals.
Results: The framework demonstrated superior performance compared with other under-sampling reconstruction methods. Distinct sampling masks were generated for the two shots after the training process.
Impact: The optimized sampling masks facilitate the acquisition of SNAP MRI with more crucial information. Combined with high-quality under-sampling reconstruction, the utilization of the framework could enhance the clinical applicability, flexibility, and versatility of SNAP MRI.
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