Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, China; Institute of Medical Technology, Peking University Health Science Center, China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, China.
Phys Med. 2022 Oct;102:33-45. doi: 10.1016/j.ejmp.2022.08.013. Epub 2022 Sep 8.
We presented TIGRE-VarianCBCT, an open-source toolkit Matlab-GPU for Varian on-board cone-beam CT with particular emphasis to address challenges in raw data preprocessing, artifacts correction, tomographic reconstruction and image post-processing. The aim of this project is to provide not only a tool to bridge the gap between clinical usage of CBCT scan data and research algorithms but also a framework that breaks down the imaging chain into individual processes so that research effort can be focused on a specific part. The entire imaging chain, module-based architecture, data flow and techniques used in the creation of the toolkit are presented. Raw scan data are first decoded to extract X-ray fluoro image series and set up the imaging geometry. Data conditioning operations including scatter correction, normalization, beam-hardening correction, ring removal are performed sequentially. Reconstruction is supported by TIGRE with FDK as well as a variety of iterative algorithms. Pixel-to-HU mapping is calibrated by a Catphan 504 phantom. Imaging dose in CTDIw is calculated in an empirical formula. The performance was validated on real patient scans with good agreement with respect to vendor-designed program. Case studies in scan protocol optimization, low dose imaging and iterative algorithm comparison demonstrated its substantial potential in performing scan data based clinical studies. The toolkit is released under the BSD license, imposing minimal restrictions on its use and distribution. The toolkit is accessible as a module at https://github.com/CERN/TIGRE.
我们提出了 TIGRE-VarianCBCT，这是一个用于 Varian 机载锥形束 CT 的开源 Matlab-GPU 工具包，特别强调解决原始数据预处理、伪影校正、层析重建和图像后处理中的挑战。该项目的目的不仅是提供一个工具来弥合 CBCT 扫描数据的临床应用和研究算法之间的差距，而且是提供一个将成像链分解为单个过程的框架，以便研究工作可以集中在特定部分。介绍了整个成像链、基于模块的架构、数据流程和创建工具包中使用的技术。原始扫描数据首先被解码以提取 X 射线荧光图像序列并设置成像几何形状。数据处理操作包括散射校正、归一化、束硬化校正、环形去除，依次进行。TIGRE 支持 FDK 以及各种迭代算法进行重建。通过 Catphan 504 体模对像素到 HU 的映射进行校准。通过经验公式计算 CTDIw 中的成像剂量。在真实患者扫描中对性能进行了验证，与供应商设计的程序具有很好的一致性。在扫描协议优化、低剂量成像和迭代算法比较的案例研究中，该工具包在进行基于扫描数据的临床研究方面表现出了巨大的潜力。该工具包根据 BSD 许可证发布，对其使用和分发施加了最小的限制。该工具包可在 https://github.com/CERN/TIGRE 作为模块访问。
