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

Synopsis

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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