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Abstract #4117

Rapid submillimeter high-resolution prostate T2 mapping with a deep learning constrained Compressed SENSE reconstruction

Masami Yoneyama1, Takashige Yoshida2, Jihun Kwon1, Kohei Yuda2, Yuki Furukawa2, Nobuo Kawauchi2, Johannes M Peeters3, and Marc Van Cauteren3
1Philips Japan, Tokyo, Japan, 2Radiology, Tokyo Metropolitan Police Hospital, Tokyo, Japan, 3Philips Healthcare, Best, Netherlands

Compressed SENSE-AI, based on Adaptive-CS-Net, clearly reduces noise artifacts and significantly improves the accuracy and robustness of T2 values in submillimeter (0.7mm) high-resolution prostate multi-echo turbo spin-echo T2 mapping compared with conventional SENSE and Compressed SENSE techniques, without any penalty for scan parameters. This technique may prove of value in better discriminating prostate cancer from healthy tissues.

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