Abstract #2667

Myocardial T2-weighted black-blood imaging with a deep learning constrained Compressed SENSE reconstruction

KOHEI YUDA¹, Takashige Yoshida¹, Yuki Furukawa¹, Masami Yoneyama², Jihun Kwon², Nobuo Kawauchi¹, Johannes M. Peeters ³, and Marc Van Cauteren³

¹Radiology, Tokyo Metropolitan Police Hospital, nakanoku, Japan, ²Philips Japan, Tokyo, Japan, shinagwaku, Japan, ³Philips Healthcare, Best, Netherlands, Netherlands, Netherlands

In high-resolution dual inversion recovery myocardial T2-weighted black-blood (T2W-BB) imaging, using very high acceleration factors with very high resolution can result in degradation of image quality due to insufficient noise removal. In this study, we applied the Compressed-SENSE Artificial Intelligence (CS-AI) framework to further increase the spatial resolution and reduce the scan time. The purpose of this study was to acquire high-resolution myocardial T2W-BB with reduced scan time and compare the image quality between images reconstructed with CS-AI and conventional C-SENSE.

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