Optimization of Sampling Masks and Reconstruction of Under-sampled Images for SNAP MRI with Model Based Deep Learning Framework

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.