Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States of America. Department of Physics, Oakland University, Rochester, MI, United States of America.
Phys Med Biol. 2018 Mar 21;63(6):065017. doi: 10.1088/1361-6560/aab235.
Tumor response to radiation treatment (RT) can be evaluated from changes in metabolic activity between two positron emission tomography (PET) images. Activity changes at individual voxels in pre-treatment PET images (PET1), however, cannot be derived until their associated PET-CT (CT1) images are appropriately registered to during-treatment PET-CT (CT2) images. This study aimed to investigate the feasibility of using deformable image registration (DIR) techniques to quantify radiation-induced metabolic changes on PET images. Five patients with non-small-cell lung cancer (NSCLC) treated with adaptive radiotherapy were considered. PET-CTs were acquired two weeks before RT and 18 fractions after the start of RT. DIR was performed from CT1 to CT2 using B-Spline and diffeomorphic Demons algorithms. The resultant displacements in the tumor region were then corrected using a hybrid finite element method (FEM). Bitmap masks generated from gross tumor volumes (GTVs) in PET1 were deformed using the four different displacement vector fields (DVFs). The conservation of total lesion glycolysis (TLG) in GTVs was used as a criterion to evaluate the quality of these registrations. The deformed masks were united to form a large mask which was then partitioned into multiple layers from center to border. The averages of SUV changes over all the layers were 1.0 ± 1.3, 1.0 ± 1.2, 0.8 ± 1.3, 1.1 ± 1.5 for the B-Spline, B-Spline + FEM, Demons and Demons + FEM algorithms, respectively. TLG changes before and after mapping using B-Spline, Demons, hybrid-B-Spline, and hybrid-Demons registrations were 20.2%, 28.3%, 8.7%, and 2.2% on average, respectively. Compared to image intensity-based DIR algorithms, the hybrid FEM modeling technique is better in preserving TLG and could be useful for evaluation of tumor response for patients with regressing tumors.
肿瘤对放射治疗（RT）的反应可以通过两次正电子发射断层扫描（PET）图像之间代谢活性的变化来评估。然而，在对治疗期间的 PET-CT（CT2）图像进行适当配准之前，无法从治疗前的 PET 图像（PET1）中的各个体素的活性变化中得出。本研究旨在探讨使用变形图像配准（DIR）技术在 PET 图像上量化辐射诱导代谢变化的可行性。研究了 5 名接受自适应放疗的非小细胞肺癌（NSCLC）患者。在 RT 前两周和 RT 开始后 18 个分次采集 PET-CT。使用 B 样条和非刚性 Demons 算法从 CT1 到 CT2 进行 DIR。然后使用混合有限元方法（FEM）对肿瘤区域的位移进行校正。从 PET1 中的大体肿瘤体积（GTV）生成的位图掩模使用四个不同的位移矢量场（DVF）进行变形。GTV 中总病变糖酵解（TLG）的守恒作为评估这些配准质量的标准。变形的掩模被联合形成一个大掩模，然后从中心到边界将其划分为多个层。所有层的 SUV 变化平均值分别为 1.0 ± 1.3、1.0 ± 1.2、0.8 ± 1.3、1.1 ± 1.5，用于 B 样条、B 样条+FEM、Demons 和 Demons+FEM 算法。使用 B 样条、Demons、混合 B 样条和混合 Demons 配准映射前后的 TLG 变化平均分别为 20.2%、28.3%、8.7%和 2.2%。与基于图像强度的 DIR 算法相比，混合 FEM 建模技术在保留 TLG 方面更有优势，可用于评估肿瘤反应。
