Deep learning-based deformable image registration of inter-fraction CBCT images for adaptive radiation therapy

Huiqiao Xie; Yang Lei; Tonghe Wang; Yabo Fu; Xiangyang Tang; Walter J. Curran; Pretesh Patel; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2581083

15 February 2021 Deep learning-based deformable image registration of inter-fraction CBCT images for adaptive radiation therapy

Huiqiao Xie, Yang Lei, Tonghe Wang, Yabo Fu, Xiangyang Tang, Walter J. Curran, Pretesh Patel, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962I (2021) https://doi.org/10.1117/12.2581083
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Cone-beam computed tomography (CBCT) has been widely used in image-guided radiation therapy. Deformable image registration (DIR) among inter-fraction CBCTs is essential in order to evaluate the geometric and anatomic changes in adaptive radiation therapy. However, the bulky data size and, more importantly, the poor quality of CBCT images, such as scattering/beam hardening artifacts, motion artifacts and CT number inaccuracy, impedes the fast and accurate image registration. In this study, we propose a novel unsupervised spatial transformation network (STN)-based DIR method for inter-fraction CBCT image registration. To evaluate the proposed method, datasets of abdominal CBCT patient images are retrospectively investigated. The preliminary results show that the proposed method, with average target registration error of 2.91±1.16 mm, has great potential in quantifying the daily anatomy changes for adaptive radiation therapy.

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Citation Download Citation

Huiqiao Xie, Yang Lei, Tonghe Wang, Yabo Fu, Xiangyang Tang, Walter J. Curran, Pretesh Patel, Tian Liu, and Xiaofeng Yang "Deep learning-based deformable image registration of inter-fraction CBCT images for adaptive radiation therapy", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962I (15 February 2021); https://doi.org/10.1117/12.2581083

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