Deep learning-based longitudinal CT registration for anatomy variation assessment during radiotherapy

Yabo Fu; Yang Lei; Zhen Tian; Tonghe Wang; Xianjin Dai; Jun Zhou; Mark McDonald; Jeffrey D. Bradley; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2611901

4 April 2022 Deep learning-based longitudinal CT registration for anatomy variation assessment during radiotherapy

Yabo Fu, Yang Lei, Zhen Tian, Tonghe Wang, Xianjin Dai, Jun Zhou, Mark McDonald, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203321 (2022) https://doi.org/10.1117/12.2611901
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

In proton therapy, quality assurance (QA) CT is often acquired along the treatment course to evaluate the dosimetric change caused by the patient anatomy variation and, if needed, replan the treatment on the new anatomy, particularly for Headand-Neck (HN) cancer which often involves many organs-at-risks (OARs) in close proximity to the targets and has a high replan rate around 45.6% after week 4. For this purpose, it is required to contour the OARs on all the acquired QA CT sets for dose-volume-histogram analysis and deform the QA CT to the planning CT to evaluate the anatomy variation and the accumulated dose over the treatment course. To facilitate this process, in this study, we have proposed deep learning based method for groupwise HN QACT deformable image registration to deform mutual image deformation between planning CT and QA CT in a single shot. A total of 30 patients’ datasets with one planning CT and 3 QA CT throughout the treatment were collected. The network was trained to register the CT images in both directions, namely registering the planning CT to each QACT and each QACT to the planning CT. The proposed mutual image registration framework can greatly improve the image registration accuracy as compared to the initial rigid image registration. The mean absolute error (MAE) and structural similarity index (SSIM) were calculated to evaluate the performance of the trained network. On average, The MAE 133±29 HU and 88±15 HU for the rigid and the proposed registration, respectively. The SSIM was on average 0.92±0.01 and 0.94±0.01 for the rigid and the proposed registration, respectively.

Citation Download Citation

Yabo Fu, Yang Lei, Zhen Tian, Tonghe Wang, Xianjin Dai, Jun Zhou, Mark McDonald, Jeffrey D. Bradley, Tian Liu, and Xiaofeng Yang "Deep learning-based longitudinal CT registration for anatomy variation assessment during radiotherapy", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203321 (4 April 2022); https://doi.org/10.1117/12.2611901

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