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Abstract #1473

Deep Learning Reconstruction to Pelvis Multi-Shot DWI Improved Image Quality with Less Image Distortion: A Preliminary Study

Elaine Yuen Phin Lee1, Chia-Wei Li2, Patricia Lan3, Xinzeng Wang4, Arnaud Guidon5, and Chien-Yuan Lin2
1Department of Diagnostic Radiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong SAR, China, 2GE Healthcare, Taipei, Taiwan, 3GE Healthcare, Menlo Park, CA, United States, 4GE Healthcare, Houston, TX, United States, 5GE Healthcare, Boston, MA, United States

Synopsis

Keywords: Pelvis, Machine Learning/Artificial Intelligence, Deep learning reconstruction, Multi-shot DWIThis study introduced the deep learning reconstruction (DLRecon) to multi-shot DWI (MUSE-DWI) in the pelvis and aimed to investigate the changes in SNR and image quality with MUSE-DWI DLRecon. Compared with the MUSE-DWI non-DLRecon, the MUSE-DWI DLRecon showed higher SNR with stable ADC quantification. With that, DLRecon could reduce image distortion using higher shots MUSE-DWI with comparable SNR and scan time to clinically used 2-shot MUSE-DWI. This preliminary result showed the potential power of DLRecon in the pelvis allowing higher shots MUSE-DWI to be integrated in clinical practice to reduce image distortion.

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