Transfer generative adversarial network for multimodal CT image super-resolution (Conference Presentation)

Yao Xiao; Ruogu Fang

doi:10.1117/12.2549533

17 March 2020 Transfer generative adversarial network for multimodal CT image super-resolution (Conference Presentation)

Yao Xiao, Ruogu Fang

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131306 (2020) https://doi.org/10.1117/12.2549533
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Yao Xiao and Ruogu Fang "Transfer generative adversarial network for multimodal CT image super-resolution (Conference Presentation)", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131306 (17 March 2020); https://doi.org/10.1117/12.2549533

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