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28 February 2020 Synthetic CT-aided MRI-CT image registration for head and neck radiotherapy
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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.
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Yabo Fu, Yang Lei, Jun Zhou, Tonghe Wang, David S. Yu, Jonathan J. Beitler, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Synthetic CT-aided MRI-CT image registration for head and neck radiotherapy", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131728 (28 February 2020); https://doi.org/10.1117/12.2549092
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