Keywords: Hyperpolarized MR (Non-Gas), Hyperpolarized MR (Non-Gas), Deep Learning, 13C, Joint-Optimization of Sampling and Reconstruction
Motivation: Low-rank recovery reconstruction enables partial fourier (PF) k-space encoding of 2D dynamic variable-resolution hyperpolarized (HP) [1-13C]pyruvate MRI. Asymmetrical PF encoding reduces T2* weighting, while mitigating the loss of resolution caused by under-sampled k-space.
Goal(s): Explore low-rank recovery reconstruction approaches for PF encoding in sparsely sampled matrices (32x32 or 16x16) in2D dynamic [1-13C]pyruvate MRI.
Approach: Retrospective and prospective [1-13C]pyruvate 2D-EPI brain and abdomen datasets were used to evaluate image fidelity of PF low-rank recovery reconstruction approaches.
Results: PF encoding enabled by low-rank recovery reconstruction algorithms increased SNR for HP metabolic images at both fine and coarser resolutions, all while maintaining image fidelity.
Impact: Asymmetrical PF encoding of 2D dynamic HP [1-13C]pyruvate MRI benefits from low-rank recovery reconstruction methods to ensure image fidelity, and to increase SNR by reducing T2* weighting.
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