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Abstract #1883

Initial clinical evaluation of deep-learning-based image synthesis and superresolution using a clinical dataset of patients with brain lesions

Ai Nakao1, Daiki Tamada2, Tomohiro Takamura2, Shintaro Ichikawa2, Utaroh Motosugi2, and Yasuhiko Terada1
1Institute of Applied Physics, University of Tsukuba, Tsukuba, Japan, 2Department of Radiology, University of Yamanashi, Chuo, Japan

Shortening scan time has been a long-standing goal in MRI, and image synthesis and superresolution using deep learning (DL) are promising tools for achieving this goal. However, most of studies use datasets with healthy volunteers for network training, and the clinical evaluation has not yet been fully performed. Here we trained networks using a large, clinical dataset of patients with brain lesions, and evaluated the generated images in terms of the diagnostic image quality and performance. Our results showed that FLAIR superresolution outperformed FLAIR image synthesis. Our results could also provide useful guidelines for evaluating diagnostic performance of DL-based networks.

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