锥束ct
计算机断层摄影术
影像引导放射治疗
图像配准
锥束ct
核医学
计算机视觉
断层摄影术
计算机科学
人工智能
医学
医学物理学
放射科
图像(数学)
作者
Yabo Fu,Yang Lei,Yingzi Liu,Tonghe Wang,Walter J. Curran,Tian Liu,Pretesh Patel,Xiaofeng Yang
摘要
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.
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