Abstract #4351

Deep learning-accelerated T2-weighted imaging of the female pelvis: reduced acquisition times and improved image quality

Jing Ren¹, Yonglan He¹, Chong Liu¹, Shifei Liu ¹, Jinxia Zhu², Marcel Dominik Nickel³, Zhengyu Jin¹, and Huadan Xue¹

¹Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China, ²MR collaboration, Siemens Healthineers Ltd., Beijing, China, ³MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany

Synopsis

Novel deep learning (DL) reconstruction methods may accelerate female pelvis MRI protocols keeping high image quality. The value of a novel DL reconstruction of T2-weighted (T2_DLR) turbo spin-echo (TSE) sequences for female pelvis MRI in three orthogonal planes was evaluated. We evaluated examination times, image quality, and lesion conspicuity of benign uterine disease. The T2_DLR quantitative parameters remained similar or were significantly improved compared with that of standard T2 TSE (T2_S), allowing for a 62.7% reduction in acquisition times. Applying this novel T2_DLR sequence achieved better image quality and shorter acquisition time than T2_S.

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