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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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