Department of Radiation Oncology, Washington University in Saint Louis, Saint Louis, MO, USA.
Department of Physics and Astronomy, University of Missouri, Columbia, MO, USA.
Med Phys. 2017 Nov;44(11):5859-5872. doi: 10.1002/mp.12526. Epub 2017 Sep 13.
An image processing procedure was developed in this study to detect large quantity of landmark pairs accurately in pairs of volumetric medical images. The detected landmark pairs can be used to evaluate of deformable image registration (DIR) methods quantitatively.
Landmark detection and pair matching were implemented in a Gaussian pyramid multi-resolution scheme. A 3D scale-invariant feature transform (SIFT) feature detection method and a 3D Harris-Laplacian corner detection method were employed to detect feature points, i.e., landmarks. A novel feature matching algorithm, Multi-Resolution Inverse-Consistent Guided Matching or MRICGM, was developed to allow accurate feature pairs matching. MRICGM performs feature matching using guidance by the feature pairs detected at the lower resolution stage and the higher confidence feature pairs already detected at the same resolution stage, while enforces inverse consistency.
The proposed feature detection and feature pair matching algorithms were optimized to process 3D CT and MRI images. They were successfully applied between the inter-phase abdomen 4DCT images of three patients, between the original and the re-scanned radiation therapy simulation CT images of two head-neck patients, and between inter-fractional treatment MRIs of two patients. The proposed procedure was able to successfully detect and match over 6300 feature pairs on average. The automatically detected landmark pairs were manually verified and the mismatched pairs were rejected. The automatic feature matching accuracy before manual error rejection was 99.4%. Performance of MRICGM was also evaluated using seven digital phantom datasets with known ground truth of tissue deformation. On average, 11855 feature pairs were detected per digital phantom dataset with TRE = 0.77 ± 0.72 mm.
A procedure was developed in this study to detect large number of landmark pairs accurately between two volumetric medical images. It allows a semi-automatic way to generate the ground truth landmark datasets that allow quantitatively evaluation of DIR algorithms for radiation therapy applications.
本研究开发了一种图像处理程序，以在体积医学图像对中准确检测大量的标志点对。所检测到的标志点对可用于定量评估变形图像配准（DIR）方法。
在高斯金字塔多分辨率方案中实现了标志点检测和配对匹配。采用三维尺度不变特征变换（SIFT）特征检测方法和三维 Harris-Laplace 角点检测方法来检测特征点，即标志点。开发了一种新颖的特征匹配算法，即多分辨率逆一致引导匹配或 MRICGM，以实现准确的特征对匹配。MRICGM 通过在较低分辨率阶段检测到的特征对和在同一分辨率阶段已经检测到的更高置信度特征对的引导来执行特征匹配，同时强制逆一致性。
所提出的特征检测和特征对匹配算法经过优化，可用于处理 3D CT 和 MRI 图像。它们成功地应用于三位患者的间相位腹部 4DCT 图像之间、两位头颈部患者的原始和重新扫描的放射治疗模拟 CT 图像之间，以及两位患者的间分次治疗 MRI 之间。所提出的方法平均能够成功检测和匹配超过 6300 对特征点。自动检测到的标志点对由人工验证，错配的对被拒绝。在手动错误拒绝之前，自动特征匹配的准确率为 99.4%。还使用具有组织变形已知真实值的七个数字体模数据集评估了 MRICGM 的性能。平均而言，每个数字体模数据集检测到 11855 对特征点，TRE = 0.77 ± 0.72mm。
本研究开发了一种程序，用于在两幅体积医学图像之间准确检测大量的标志点对。它允许以半自动的方式生成地面真实标志点数据集，从而可以定量评估用于放射治疗应用的 DIR 算法。
