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Abstract #4616

Accelerated 3D PDWI in knee imaging: Quality and efficiency of a deep learning-based Compressed SENSE reconstruction

Lin Mu1, Ying Qiu2, Yun Pei3, Yi Zhu4, and Ke Jiang4
1Radiology, The First Hospital of Jilin University, Changchun, China, 2The First Hospital of Jilin University, Changchun, China, 3College of Electronic Science and Engineering, Jilin University, Changchun, China, 4Philips Healthcare, Beijing, China


The use of three dimensional (3D) volumetric acquisition in clinical settings has been limited due to long scan time. A deep learning-based reconstruction algorithm allows shortening of scan time and provide comparable overall image quality when compared with standard sequences. Adaptive-CS-Net, a deep neural network previously introduced at the 2019 fast MRI challenge, was expanded and presented here as a Compressed-SENSE Artificial Intelligence (CS-AI) reconstruction. The purpose of the study is to determine the feasibility of 3D PDWI accelerated with CS-AI for evaluating the knee image quality and compared with SENSE and standard Compressed-SENSE.

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