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Keywords: artificial intelligence, image reconstruction, perfusion Cardiac magnetic resonance first-pass contrast-enhanced myocardial perfusion imaging is valuable for evaluating coronary artery disease1. 2D Cartesian perfusion imaging using compressed sensing (CS)-based reconstructions such as L1-SENSE2 enables fast and high-resolution imaging, but whole-heart coverage cannot be achieved without simultaneous multi-slice (SMS) acquisitions and the CS-based iterative reconstruction takes ~30 minutes per slice. To address this, we have developed a deep learning-based motion-corrected rapid image reconstruction for high-resolution Cartesian perfusion imaging at 3 Tesla, for both 2D and SMS MB=2 acquisitions, which provides fast and high-quality motion-corrected reconstruction and makes rapid online reconstruction feasible.
Wang et al. (Wed,) studied this question.