Department of Radiation Oncology, University of California, San Francisco, California 94143, USA.
Med Phys. 2013 Apr;40(4):041911. doi: 10.1118/1.4793723.
The accuracy of deformable image registration could have a significant dosimetric impact in radiation treatment planning. Various image registration algorithms have been developed for clinical application. However, validation of these algorithms in the current clinical setting remains subjective, relying on visual assessment and lacking a comparison to the ground-truth deformation. In this study, the authors propose a framework to quantitatively validate various image registration solutions by using patient-based synthetic quality assurance (QA) phantoms, which can be applied on a site-by-site basis.
The computer-simulated deformation was first generated with virtual deformation QA software and further benchmarked using a physical pelvic phantom that was modeled after real patient CT images. After the validity of the virtual deformation was confirmed, a set of synthetic deformable images was produced to simulate various anatomical movements during radiotherapy based on real patient CT images. Three patients with head-and-neck, prostate, and spine cancer were included. The transformations included bladder filling, soft tissue deformation, mandible, and vertebral body movement, etc., which provided various ground-truth images to validate deformable registration. Several clinically available deformable registration algorithms were tested on these images with multiple registration setups, such as global or regional and single-pass or multipass optimization. The generated deformation fields and the ground-truth deformation are compared using voxel-by-voxel based analysis as well as regional based analysis.
Performance of registration algorithms varies with clinical sites. The voxel-by-voxel analysis showed the intensity-based free-form deformation by MIM generated the greatest accuracy for low-contrast small regions that underwent significant deformation, such as bladder expansion for prostate. However, for large field deformations with strong contrast, this approach may increase errors, which is especially evident in the cranial spinal irradiation (CSI) case. Both single-pass and multipass B-spline registrations performed well for the head-and-neck patient and CSI patients.
QA for deformable image registration is essential to verify the cumulated dose for accurate adaptive radiotherapy. In this study, the authors develop a workflow that can validate image registration techniques for several different clinical sites and for various types of deformations using patient-based simulated deformations. This work could provide a reference for clinicians and radiation physicists on how to choose appropriate image registration algorithms for different situations.
变形图像配准的准确性可能对放射治疗计划的剂量学有重大影响。已经开发了各种图像配准算法用于临床应用。然而，在当前的临床环境中，这些算法的验证仍然是主观的，依赖于视觉评估，并且缺乏与真实变形的比较。在这项研究中，作者提出了一种通过使用基于患者的合成质量保证（QA）体模来定量验证各种图像配准解决方案的框架，该框架可以在站点基础上应用。
首先使用虚拟变形 QA 软件生成计算机模拟变形，然后使用基于真实患者 CT 图像建模的物理骨盆体模进行基准测试。在虚拟变形的有效性得到确认后，根据真实患者 CT 图像生成一组合成可变形图像，以模拟放射治疗过程中的各种解剖运动。包括头颈部、前列腺和脊柱癌的 3 名患者。包括膀胱充盈、软组织变形、下颌骨和椎体运动等，这些提供了各种真实变形图像来验证变形配准。在这些图像上测试了几种临床可用的变形配准算法，并使用多种配准设置，如全局或局部以及单步或多步优化。使用基于体素和基于区域的分析比较生成的变形场和真实变形。
配准算法的性能因临床部位而异。体素分析显示，MIM 的基于强度的自由变形在经历显著变形的低对比度小区域（如前列腺的膀胱膨胀）中产生了最大的准确性。然而，对于具有强对比度的大场变形，这种方法可能会增加误差，在颅脊髓照射（CSI）病例中尤为明显。对于头颈部患者和 CSI 患者，单步和多步 B 样条配准都表现良好。
变形图像配准的 QA 对于准确的自适应放射治疗的累积剂量验证至关重要。在这项研究中，作者开发了一种工作流程，该流程可以使用基于患者的模拟变形来验证用于几个不同临床部位和各种变形类型的图像配准技术。该工作可以为临床医生和放射物理学家提供如何为不同情况选择合适的图像配准算法的参考。
