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Abstract #3097

Clinical Feasibility Study of Accelerated 2D Magnetic Resonance shoulder Imaging Using Deep Learning-based Algorithm

Jing Liu1, Ke Xue2, Yongming Dai2, Peng Wu2, and Jianxing Qiu1
1Peking University First Hospital, Beijing, China, 2MR Collaboration, Central Research Instituteļ¼ŒUnited Imaging Healthcare, Shanghai, China


Deep Learning-based magnetic resonance imaging (DL-MRI) could accelerate 3D MRI scanning time. In this study, we investigate the feasibility of DL-MRI in 2D shoulder MRI. Totally 20 consecutive patients were enrolled for both conventional MRI and DL-MRI. Both qualitative and quantitative analyses were conducted to compare the image quality and lesion diagnosis on conventional MRI and DL-MRI. And our results revealed that DL-MRI was valuable for improving the overall workflow of shoulder MRI with scanning time saved and image quality improved.

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