Abstract #5129

Deep-learning-based super-resolution technique for cine cardiac magnetic resonance

Satonori Tsuneta¹, Satoru Aono², Rina Kimura¹, Jihun Kwon³, Takuya Aoike², Masami Yoneyama³, Kinya Ishizaka², Noriyuki Fujima¹, and Kohsuke Kudo⁴

¹Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan, ²Department of Radiological Technology, Hokkaido University Hospital, Sapporo, Japan, ³Philips Japan, Ltd., Tokyo, Japan, ⁴Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan

Synopsis

Keywords: Myocardium, Data ProcessingCine cardiac magnetic resonance (CMR) imaging is an optimal cardiac volumetric analysis method because of its high contrast resolution. However, its spatial resolution is limited owing to prolonged scanning and breath-holding. Although compressed sensing–sensitivity encoding (Compressed SENSE; CS) and its deep-learning-based advancement (SmartSpeed AI; SSAI) can reduce the scan time, the spatial resolutions remain unchanged. Herein, we investigated the effect of a deep-learning-based super-resolution technique (SmartSpeed Precise Image; SSPI) on the cine CMR visual image quality in comparison with CS and SSAI with conventional zero-filling interpolation; resultantly, the SSPI significantly improved the visual image quality scores.

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