Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA; Department of Biomedical Engineering, University of Miami College of Engineering, Coral Gables, FL, USA.
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA.
Phys Med. 2022 Nov;103:26-36. doi: 10.1016/j.ejmp.2022.09.010. Epub 2022 Oct 8.
To develop a new registration quality metric, based on the distance between image edges, for automated evaluation and comparison of DIR algorithms.
Canny filter is used to create binary gradient images from input images to be compared. A small subregion of one binary image is translated relative to the other. The translational distance maximizing overlap of edges in the subregion is the local edge gradient distance to agreement (EGDTA); repeating over all subregions provides an EGDTA map. The method was tested on phantom and pelvic CT images, by applying a known deformable vector field (DVF). The method was then applied to evaluate two DIR algorithms (SICLE and Demons) for pelvic CTs from five patients. Three SICLE variants were used: Grayscale-driven (G), Contour-driven (C), and Grayscale + Contour-driven (G + C). For each patient, a planning CT was registered to three repeat CTs using the above DIR algorithms. Mean EGDTA values in concentric ring regions of interest close to and far away from the pelvic organ contours were compared.
EGDTA maps and imposed DVF deformations on phantom and CT images demonstrated agreement. In comparison of the three variants of SICLE: C had lower EGDTA values close to the pelvic organs, while G showed much better performance in the regions distant from the organs compared to C; and G + C registration exhibited the lowest or comparable EGDTA value among three variants. Demons achieved the lowest EGDTA values.
The EGDTA metric shows potential as an automated means of evaluating and comparing DIR algorithms.
开发一种新的基于图像边缘距离的配准质量度量方法，用于自动评估和比较 DIR 算法。
使用 Canny 滤波器从要比较的输入图像创建二进制梯度图像。一个二进制图像的小子区域相对于另一个图像进行平移。子区域中边缘重叠度最大化的平移距离就是局部边缘梯度一致距离（EGDTA）；在所有子区域上重复此操作可得到 EGDTA 图。该方法在体模和盆腔 CT 图像上进行了测试，通过应用已知的可变形向量场（DVF）。然后，该方法用于评估来自五名患者的盆腔 CT 的两种 DIR 算法（SICLE 和 Demons）。使用了三种 SICLE 变体：灰度驱动（G）、轮廓驱动（C）和灰度+轮廓驱动（G+C）。对于每个患者，使用上述 DIR 算法将计划 CT 与三个重复 CT 进行配准。比较靠近和远离盆腔器官轮廓的同心感兴趣区域的平均 EGDTA 值。
EGDTA 图和施加在体模和 CT 图像上的 DVF 变形一致。在比较 SICLE 的三种变体时：C 在靠近盆腔器官的区域具有较低的 EGDTA 值，而 G 在远离器官的区域的性能明显优于 C；而 G+C 配准在三种变体中表现出最低或可比较的 EGDTA 值。Demons 实现了最低的 EGDTA 值。
EGDTA 度量方法具有作为自动评估和比较 DIR 算法的潜力。
