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Proceedings Paper

Transfer generative adversarial network for multimodal CT image super-resolution (Conference Presentation)
Author(s): Yao Xiao; Ruogu Fang

Paper Abstract

Multimodal computed tomography (CT) scans, including non-contrast CT (NCCT), CT Perfusion (CTP), and CT Angiography (CTA) are widely used in acute stroke diagnosis and treatment planning. While each imaging modality is for different visualization purposes such as anatomical structures and functional information, image quality is obtained variously. In this work, we aim at enhancing the image quality for all modalities by using deep learning technology. Through our experiments, we demonstrate that by using transfer learning and generative adversarial network, NCCT images are beneficial for CTP image reconstruction, and CTP images are helpful for CTA image quality enhancement.

Paper Details

Date Published: 17 March 2020
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131306 (17 March 2020); doi: 10.1117/12.2549533
Show Author Affiliations
Yao Xiao, Univ. of Florida (United States)
Ruogu Fang, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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