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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