Evaluation of Non-Rigid Image-Registration Algorithms Using DiscrepancyDistance Between Organ Contours

Y. Saito, K. Tateoka, K. Shima, T. Nakazawa, A. Nakata, T. Abe, M. Yano, K. Fujimoto, M. Someya, Kensei Nakata, M. Hori, M.Hareyama, K. Sakata
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Abstract

Purpose: Non-rigid image registration (NIR) is useful for adaptive radiotherapy. However, no method has been established for evaluating the quality of the algorithms used in NIR. To remedy this situation, we demonstrate herein a novel method to evaluate NIR algorithms. Methods: We define the NIR error as the discrepancy distance between (i) the organ contours obtained from computed tomography (CT) images acquired during the treatment period (reference contours) and (ii) the contours obtained from the treatment-planning CT images that are constructed by automated propagation during the treatment period (evaluation contours). However, the continuous positional relationship between the points where the reference contour intersects the evaluation contour is assumed to be maintained. In addition, we adapt the proposed method so that it can be applied to the contours of complex organs such as spherical and tubular organs. To demonstrate this method, we measure the contours of the prostate, right seminal vesicle, left seminal vesicle, urinary bladder, and rectum. The obtained NIR error presented in two-dimensional (2D) discrepancy maps. Results: The 2D discrepancy maps show the difference between the reference and evaluation contours from CT images. The proposed method measures the difference between the contours of spherical and tubular organs and evaluates the NIR error based on the positional relationship between the points constituting the contours. Conclusions: This study accounts for and measures the continuous positional relationship between corresponding points in the contours of complex-shaped spherical and tubular organs with irregularities and evaluates NIR algorithms based on these organ contours.
利用器官轮廓之间的距离差评价非刚性图像配准算法
目的:非刚性图像配准(NIR)用于自适应放疗。然而,目前还没有建立评价近红外算法质量的方法。为了纠正这种情况,我们在此展示了一种评估近红外算法的新方法。方法:我们将近红外误差定义为(i)从治疗期间获得的计算机断层扫描(CT)图像中获得的器官轮廓(参考轮廓)和(ii)从治疗期间通过自动传播构建的治疗计划CT图像中获得的轮廓(评估轮廓)之间的差异距离。但是,假设参考轮廓与评估轮廓相交的点之间保持连续的位置关系。此外,我们对所提出的方法进行了改进,使其可以应用于复杂器官的轮廓,如球形器官和管状器官。为了证明这种方法,我们测量了前列腺、右精囊、左精囊、膀胱和直肠的轮廓。得到的近红外误差以二维差异图的形式呈现。结果:二维差异图显示了参考轮廓与评价轮廓在CT图像上的差异。该方法测量球面和管状器官轮廓的差异,并基于构成轮廓的点之间的位置关系来评估近红外误差。结论:本研究考虑并测量了不规则复杂球形和管状器官轮廓中对应点之间的连续位置关系,并对基于这些器官轮廓的近红外算法进行了评价。
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