Keywords: Motion Correction, Perfusion, free breathing, myocardial perfusion, simultaneous multi-slice, prospective motion-correction, machine learning/artificial intelligence
Motivation: Simultaneous multi-slice-bSSFP shows promise for myocardial perfusion imaging with high spatial coverage/resolution. Free-breathing acquisitions are desirable but currently result in large through-plane motion.
Goal(s): To develop a free-breathing SMS-bSSFP myocardial perfusion technique with high spatial coverage/resolution and prospective through-plane motion correction.
Approach: Prospective slice-tracking using fastNAV was implemented into an SMS-bSSFP perfusion sequence. Image reconstruction used TGRAPPA combined with a deep learning-based complex-value image denoiser. This technique was evaluated in 10 patients undergoing two rest SMS perfusion scans with/without fastNAV.
Results: The proposed approach resulted in significant motion reduction, low noise-level reconstruction, and no degradation of myocardial sharpness.
Impact: This study demonstrates the feasibility of prospective slice tracking in an SMS perfusion sequence. Combined with the proposed deep learning-based reconstruction, it provides a myocardial perfusion protocol with increased spatial coverage, high spatial resolution, and feasible under free-breathing conditions.
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