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Proceedings Paper

Synthetic CT-aided MRI-CT image registration for head and neck radiotherapy
Author(s): Yabo Fu; Yang Lei; Jun Zhou; Tonghe Wang; David S. Yu; Jonathan J. Beitler; Walter J. Curran; Tian Liu; Xiaofeng Yang
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Paper Abstract

In this study, we propose a synthetic CT (sCT) aided MRI-CT deformable image registration for head and neck radiotherapy. An image synthesis network, cycle consistent generative adversarial network (CycleGAN), was first trained using 25 pre-aligned CT-MRI image pairs. Using the MR head and neck images, the trained CycleGAN then predicts sCT images, which were used as MRI’s surrogate in MRI-CT registration. Demons registration algorithm was used to perform the sCT-CT registration on 5 separate datasets. For comparison, the original MRI and CT images were registered using mutual information as similarity metric. Our results showed that the target registration errors after registration were on average 1.31 mm and 1.02 mm for MRI-CT and sCT-CT registration, respectively. The mean normalized cross correlation between the sCT and CT after registration was 0.97, indicating that the proposed method is a viable way to perform MRI-CT image registration for head neck patients.

Paper Details

Date Published: 28 February 2020
PDF: 7 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131728 (28 February 2020); doi: 10.1117/12.2549092
Show Author Affiliations
Yabo Fu, Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Jun Zhou, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
David S. Yu, Emory Univ. (United States)
Jonathan J. Beitler, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)

Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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