Abstract #3718

Feasibility of Deep Learning Reconstruction in the Clinical Application of MRI for patients with Bladder Cancer： a preliminary prospective study

Xinxin Zhang¹, Yichen Wang¹, Sicong Wang², Min Li², Yan Chen¹, and Xinming Zhao¹

¹National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China, Beijing, China, ²GE Healthcare, MR Research China, Beijing, Beijing, China

Synopsis

Keywords: Urogenital, Bladder, Deep Learning ReconstructionThe application of DLR significantly shortened scan times and improved the overall image quality score and image artifacts score and SNR and CNR of FSE-T2WI. DLR fast FSE-T2WI demonstrated significantly higher SNR (256.7±102.9 VS 94.7±40.8, p < 0.05) and CNR (168.0±77.3 VS 59.6±29.8, p < 0.05) and overall image quality scores (median, 4.0 vs. 3.0 for reader1 and 4.0 vs. 3.5 for reader2) than those of conventional FSE-T2WI. DLR may be useful in reducing the acquisition time of bladder MRI without compromising image quality.

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