Abstract #4862

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation

Akifumi Hagiwara¹, Yujiro Otsuka², Masaaki Hori², Yasuhiko Tachibana³, Kazumasa Yokoyama⁴, Shohei Fujita², Christina Andica², Koji Kamagata², Ryusuke Irie², Saori Koshino¹, Tomoko Maekawa², Lydia Chougar⁵, Akihiko Wada², Mariko Yoshida Takemura², Nobutaka Hattori⁴, and Shigeki Aoki²

¹Radiology, The University of Tokyo Hospital, Tokyo, Japan, ²Radiology, Juntendo University Hospital, Tokyo, Japan, ³Radiology, National Institute of Radiological Sciences, Chiba, Japan, ⁴Neurology, Juntendo University Hospital, Tokyo, Japan, ⁵Radiology, Hopital Saint-Joseph, Paris, France

Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversarial network training. Forty patients with MS were prospectively included and scanned to acquire synthetic MRI and conventional FLAIR images. Acquired data were divided into 30 training and 10 test datasets. Using deep learning, we improved the synthetic FLAIR image quality by generating FLAIR images that have contrast that is closer to that of conventional FLAIR images and fewer granular and swelling artifacts, while preserving the lesion contrast.

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