Cone-beam Computed Tomography (CBCT) and CT image registration aided by CBCT-based synthetic CT

Yabo Fu; Yang Lei; Yingzi Liu; Tonghe Wang; Walter J. Curran; Tian Liu; Pretesh Patel; Xiaofeng Yang

doi:10.1117/12.2549095

Translator Disclaimer

10 March 2020 Cone-beam Computed Tomography (CBCT) and CT image registration aided by CBCT-based synthetic CT

Yabo Fu, Yang Lei, Yingzi Liu, Tonghe Wang, Walter J. Curran, Tian Liu, Pretesh Patel, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113132U (2020) https://doi.org/10.1117/12.2549095
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

CBCT has been widely integrated into modern linear accelerators in radiation therapy for image guidance purpose due to its cost effectiveness and low dose to patients. Planning CT images are registered with CBCT images for patient setup, contour propagations and dose calculations. However, it is challenging to accurately register the two since CBCT images often contain much image artifacts and noise, and intensity between CT and CBCT is not consistent. Therefore, traditional DIRs with intensity-based image similarity measures such as sum of squared differences, mean absolute differences are not applicable to CT-CBCT image registration. To address this issue, we propose to synthesize a high quality CT from CBCT to reduce image artifacts and perform intensity correction prior to image registration. Traditional Demons registration method was used to register the CT images with the CBCT-based synthetic CT (sCT). CT-sCT registration was tested on 5 patients’ datasets. On average, the mean absolute error between the fixed and deformed images were reduced from 102.2 to 92.4 HU by replacing CBCT with sCT.

Citation Download Citation

Yabo Fu, Yang Lei, Yingzi Liu, Tonghe Wang, Walter J. Curran, Tian Liu, Pretesh Patel, and Xiaofeng Yang "Cone-beam Computed Tomography (CBCT) and CT image registration aided by CBCT-based synthetic CT", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132U (10 March 2020); https://doi.org/10.1117/12.2549095

ACCESS THE FULL ARTICLE