Juei-Shan Chang, H. Tai, Ching-Jung Wu, K. Hua, Yu-Jen Chen
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Methods: For the image sets with format of Digital Imaging and Communications in Medicine (DICOM) standard, we wrote a program in MATLAB language (Version R2016a) to read and convert CBCT images into cross-sectional (tomographic) images similar to those obtained via planning CT. For image enhancement, the active contouring by using Chan-Vese model with level set formulation was applied. To overcome the variations in spatial location of these two sets of CT images, the iterative closest point (ICP) algorithm was used for 3D model registration. The deformable image registration (DIR) with Double force Demons algorithm was performed for auto-transformation of contours from planning CT to CBCT images. Results: The customized program accurately converted the format of CBCT to planning CT. Image enhancement was achieved by our modified active contour model which solved the energy minimization problem. In 3D model registration, the variations in spatial location of the CBCT and planning CT images were corrected. After selection of most similar images, the planning CT images were registered to corresponding CBCT images. The registered images were clearer than CBCT images with removal of other confounding structures outside body contours. Conclusions: The planning CT and CBCT images could be precisely registered by using a novel established technique consisting of active contouring with 3D model and DIR. This technique would enable the on-line radiation treatment planning for adaptive radiotherapy.","PeriodicalId":93236,"journal":{"name":"Therapeutic radiology and oncology","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Active contouring and 3D model deformable registration of radiotherapy planning and cone-beam computed tomography images\",\"authors\":\"Juei-Shan Chang, H. Tai, Ching-Jung Wu, K. Hua, Yu-Jen Chen\",\"doi\":\"10.21037/tro.2019.07.06\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: To ensure high accuracy during radiation therapy (RT), the image-guided RT (IGRT) technique uses on-board cone-beam computed tomography (CBCT) scanning as an image guidance procedure for target localization before and during treatment. Adaptive RT aiming to modify RT target volumes according to kinetic changes in tumor shape during RT course is based on registration of CBCT and planning CT images. However, the re-contouring and re-planning procedures are extensively time and cost consuming. We developed a novel automatic contouring and image registration method to replace the manual re-contouring with accurate image registration. Methods: For the image sets with format of Digital Imaging and Communications in Medicine (DICOM) standard, we wrote a program in MATLAB language (Version R2016a) to read and convert CBCT images into cross-sectional (tomographic) images similar to those obtained via planning CT. For image enhancement, the active contouring by using Chan-Vese model with level set formulation was applied. To overcome the variations in spatial location of these two sets of CT images, the iterative closest point (ICP) algorithm was used for 3D model registration. The deformable image registration (DIR) with Double force Demons algorithm was performed for auto-transformation of contours from planning CT to CBCT images. Results: The customized program accurately converted the format of CBCT to planning CT. Image enhancement was achieved by our modified active contour model which solved the energy minimization problem. In 3D model registration, the variations in spatial location of the CBCT and planning CT images were corrected. After selection of most similar images, the planning CT images were registered to corresponding CBCT images. The registered images were clearer than CBCT images with removal of other confounding structures outside body contours. Conclusions: The planning CT and CBCT images could be precisely registered by using a novel established technique consisting of active contouring with 3D model and DIR. 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引用次数: 0
摘要
背景:为了确保放射治疗(RT)期间的高精度,图像引导RT (IGRT)技术使用板载锥形束计算机断层扫描(CBCT)扫描作为治疗前和治疗期间目标定位的图像引导程序。自适应RT是基于CBCT和规划CT图像的配准,旨在根据RT过程中肿瘤形态的动力学变化来修改RT靶体积。然而,重新轮廓和重新规划的过程是广泛的时间和成本消耗。我们开发了一种新的自动轮廓和图像配准方法,以精确的图像配准取代人工重新轮廓。方法:针对DICOM (Digital Imaging and Communications in Medicine)标准格式的图像集,用MATLAB语言(Version R2016a)编写程序,读取CBCT图像并将其转换为与规划CT相似的横断面(断层)图像。在图像增强方面,采用基于水平集的Chan-Vese模型进行主动轮廓。为了克服这两组CT图像在空间位置上的差异,采用迭代最近点(ICP)算法进行三维模型配准。采用双力Demons算法实现了可变形图像配准,实现了规划CT到CBCT图像的轮廓自动转换。结果:定制程序准确地将CBCT格式转换为规划CT。改进的活动轮廓模型解决了能量最小化问题,实现了图像增强。在三维模型配准中,对CBCT和规划CT图像的空间位置变化进行了校正。选择最相似的图像后,将规划CT图像配准到相应的CBCT图像上。配准图像比CBCT图像更清晰,去除了身体轮廓外的其他混淆结构。结论:采用一种由三维模型主动轮廓和DIR组成的新技术可以精确地配准规划CT和CBCT图像。该技术可实现自适应放射治疗的在线放射治疗计划。
Active contouring and 3D model deformable registration of radiotherapy planning and cone-beam computed tomography images
Background: To ensure high accuracy during radiation therapy (RT), the image-guided RT (IGRT) technique uses on-board cone-beam computed tomography (CBCT) scanning as an image guidance procedure for target localization before and during treatment. Adaptive RT aiming to modify RT target volumes according to kinetic changes in tumor shape during RT course is based on registration of CBCT and planning CT images. However, the re-contouring and re-planning procedures are extensively time and cost consuming. We developed a novel automatic contouring and image registration method to replace the manual re-contouring with accurate image registration. Methods: For the image sets with format of Digital Imaging and Communications in Medicine (DICOM) standard, we wrote a program in MATLAB language (Version R2016a) to read and convert CBCT images into cross-sectional (tomographic) images similar to those obtained via planning CT. For image enhancement, the active contouring by using Chan-Vese model with level set formulation was applied. To overcome the variations in spatial location of these two sets of CT images, the iterative closest point (ICP) algorithm was used for 3D model registration. The deformable image registration (DIR) with Double force Demons algorithm was performed for auto-transformation of contours from planning CT to CBCT images. Results: The customized program accurately converted the format of CBCT to planning CT. Image enhancement was achieved by our modified active contour model which solved the energy minimization problem. In 3D model registration, the variations in spatial location of the CBCT and planning CT images were corrected. After selection of most similar images, the planning CT images were registered to corresponding CBCT images. The registered images were clearer than CBCT images with removal of other confounding structures outside body contours. Conclusions: The planning CT and CBCT images could be precisely registered by using a novel established technique consisting of active contouring with 3D model and DIR. This technique would enable the on-line radiation treatment planning for adaptive radiotherapy.