Department of Radiation Oncology and Winship Cancer Institute of Emory University, Atlanta, Georgia 30322, USA.
J Appl Clin Med Phys. 2012 Jul 5;13(4):3789. doi: 10.1120/jacmp.v13i4.3789.
The purpose of this study was to develop and validate a technique for unsealed source radiotherapy planning that combines the segmentation and registration tasks of single-photon emission tomography (SPECT) and computed tomography (CT) datasets. The segmentation task is automated by an atlas registration approach that takes advantage of a hybrid scheme using a diffeomorphic demons algorithm to warp a standard template to the patient's CT. To overcome the lack of common anatomical features between the CT and SPECT datasets, registration is achieved through a narrow band approach that matches liver contours in the CT with the gradients of the SPECT dataset. Deposited dose is then computed from the SPECT dataset using a convolution operation with tracer-specific deposition kernels. Automatic segmentation showed good agreement with manual contouring, measured using the dice similarity coefficient and ranging from 0.72 to 0.87 for the liver, 0.47 to 0.93 for the kidneys, and 0.74 to 0.83 for the spinal cord. The narrow band registration achieved variations of less 0.5 mm translation and 1° rotation, as measured with convergence analysis. With the proposed combined segmentation-registration technique, the uncertainty of soft-tissue target localization is greatly reduced, ensuring accurate therapy planning.
本研究旨在开发和验证一种用于未密封源放射治疗计划的技术，该技术结合了单光子发射断层扫描（SPECT）和计算机断层扫描（CT）数据集的分割和配准任务。通过利用混合方案的图谱配准方法实现分割任务，该方案使用变形 demons 算法将标准模板变形到患者的 CT 上。为了克服 CT 和 SPECT 数据集之间缺乏共同解剖特征的问题，通过匹配 CT 中的肝脏轮廓与 SPECT 数据集的梯度的窄带方法实现配准。然后，使用示踪剂特异性沉积核的卷积运算从 SPECT 数据集计算沉积剂量。自动分割与手动轮廓测量具有良好的一致性，使用骰子相似系数进行测量，肝脏的范围为 0.72 至 0.87，肾脏的范围为 0.47 至 0.93，脊髓的范围为 0.74 至 0.83。通过收敛性分析测量，窄带配准实现了小于 0.5 毫米的平移和 1°的旋转变化。通过提出的组合分割-配准技术，大大降低了软组织靶定位的不确定性，确保了精确的治疗计划。
