King Martin, Sensakovic William F, Maxim Peter, Diehn Maximilian, Loo Billy W, Xing Lei
1 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
2 Department of Radiology, Florida Hospital, Orlando, FL, USA.
Technol Cancer Res Treat. 2018 Jan 1;17:1533034617749419. doi: 10.1177/1533034617749419.
The deformable registration of pulmonary computed tomography images before and after radiation therapy is challenging due to anatomic changes from radiation fibrosis. We hypothesize that a line-enhanced registration algorithm can reduce landmark error over the entire lung, including the irradiated regions, when compared to an intensity-based deformable registration algorithm.
Two intensity-based B-spline deformable registration algorithms of pre-radiation therapy and post-radiation therapy images were compared. The first was a control intensity-based algorithm that utilized computed tomography images without modification. The second was a line enhancement algorithm that incorporated a Hessian-based line enhancement filter prior to deformable image registration. Registrations were evaluated based on the landmark error between user-identified landmark pairs and the overlap ratio.
Twenty-one patients with pre-radiation therapy and post-radiation therapy scans were included. The median time interval between scans was 1.2 years (range: 0.3-3.3 years). Median landmark errors for the line enhancement algorithm were significantly lower than those for the control algorithm over the entire lung (1.67 vs 1.83 mm; P < .01), as well as within the 0 to 5 Gy (1.40 vs 1.57; P < .01) and >5 Gy (2.25 vs 3.31; P < .01) dose intervals. The median lung mask overlap ratio for the line enhancement algorithm (96.2%) was greater than that for the control algorithm (95.8%; P < .01). Landmark error within the >5 Gy dose interval demonstrated a significant inverse relationship with post-radiation therapy fibrosis enhancement after line enhancement filtration (Pearson correlation coefficient = -0.48; P = .03).
The line enhancement registration algorithm is a promising method for registering images before and after radiation therapy.
由于放射纤维化导致的解剖结构变化,放射治疗前后肺部计算机断层扫描图像的可变形配准具有挑战性。我们假设,与基于强度的可变形配准算法相比,线增强配准算法可以减少整个肺部(包括受照射区域)的地标误差。
比较了两种基于强度的放射治疗前和放射治疗后图像的B样条可变形配准算法。第一种是基于强度的对照算法,该算法使用未经修改的计算机断层扫描图像。第二种是线增强算法,该算法在可变形图像配准之前合并了基于Hessian矩阵的线增强滤波器。基于用户识别的地标对之间的地标误差和重叠率对配准进行评估。
纳入21例有放射治疗前和放射治疗后扫描的患者。扫描之间的中位时间间隔为1.2年(范围:0.3 - 3.3年)。线增强算法的中位地标误差在整个肺部显著低于对照算法(1.67对1.83毫米;P < 0.01),在0至5 Gy(1.40对1.57;P < 0.0(此处原文有误,推测为P < 0.01))和>5 Gy(2.25对3.31;P < 0.01)剂量间隔内也是如此。线增强算法的中位肺掩码重叠率(96.2%)高于对照算法(95.8%;P < 0.01)。在>5 Gy剂量间隔内的地标误差在线增强滤波后与放射治疗后纤维化增强呈显著负相关(Pearson相关系数 = -0.48;P = 0.03)。
线增强配准算法是一种用于放射治疗前后图像配准的有前景的方法。