Purdue School of Engineering and Technology, Indiana University Purdue University Indianapolis, 799 W Michigan St, Indianapolis, IN 46202, USA.
J Appl Clin Med Phys. 2012 Mar 8;13(2):3629. doi: 10.1120/jacmp.v13i2.3629.
True 3D CT dataset for treatment planning of an oversized patient is difficult to acquire due to the bore size and field of view (FOV) reconstruction. This project aims to provide a simple approach to reconstruct true CT data for oversize patients using CT scanner with limited FOV by acquiring double partial CT (left and right side) images. An efficient line profile-based method has been developed to minimize the difference of the CT numbers in the overlapping region between the right and left images and to generate a complete true 3D CT dataset in the natural state. New image processing modules have been developed and integrated to the Insight Segmentation & Registration Toolkit (ITK 3.6) package. For example, different modules for image cropping, line profile generation, line profile matching, and optimized partial image fusion have been developed. The algorithm has been implemented for images containing the bony structure of the spine and tested on 3D CT planning datasets from both phantom and real patients with satisfactory results in both cases. The proposed optimized line profile-based partial registration method provides a simple and accurate method for acquiring a complete true 3D CT dataset for an oversized patient using CT scanning with small bore size, that can be used for accurate treatment planning.
由于孔径大小和视野（FOV）重建的原因，很难获得用于超大患者治疗计划的真实 3D CT 数据集。本项目旨在通过获取双侧部分 CT（左侧和右侧）图像，为具有有限 FOV 的 CT 扫描仪提供一种简单的方法，用于重建超大患者的真实 CT 数据。已经开发了一种基于高效线轮廓的方法，以最小化右侧和左侧图像之间重叠区域中的 CT 值差异，并生成自然状态下的完整真实 3D CT 数据集。已经开发并集成了新的图像处理模块到 Insight Segmentation & Registration Toolkit（ITK 3.6）包中。例如，已经开发了用于图像裁剪、线轮廓生成、线轮廓匹配和优化部分图像融合的不同模块。该算法已针对包含脊柱骨结构的图像进行了实现，并在来自幻影和真实患者的 3D CT 计划数据集上进行了测试，两种情况下的结果都令人满意。所提出的基于优化线轮廓的部分配准方法为使用小孔径 CT 扫描获取超大患者完整真实 3D CT 数据集提供了一种简单而准确的方法，可用于精确治疗计划。
