Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada.
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada; Department of Radiology & Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada.
Comput Methods Programs Biomed. 2018 Oct;165:187-195. doi: 10.1016/j.cmpb.2018.08.002. Epub 2018 Aug 22.
Tracking mobile tumor regions during the treatment is a crucial part of image-guided radiation therapy because of two main reasons which negatively affect the treatment process: (1) a tiny error will lead to some healthy tissues being irradiated; and (2) some cancerous cells may survive if the beam is not accurately positioned as it may not cover the entire cancerous region. However, tracking or delineation of such a tumor region from magnetic resonance imaging (MRI) is challenging due to photometric similarities of the region of interest and surrounding area as well as the influence of motion in the organs. The purpose of this work is to develop an approach to track the center and boundary of tumor region by auto-contouring the region of interest in moving organs for radiotherapy.
We utilize a nonrigid registration method as well as a publicly available RealTITracker algorithm for MRI to delineate and track tumor regions from a sequence of MRI images. The location and shape of the tumor region in the MRI image sequence varies over time due to breathing. We investigate two approaches: the first one uses manual segmentation of the first frame during the pretreatment stage; and the second one utilizes manual segmentation of all the frames during the pretreatment stage.
We evaluated the proposed approaches over a sequence of 600 images acquired from 6 patients. The method that utilizes all the frames in the pretreatment stage with moving mesh based registration yielded the best performance with an average Dice Score of 0.89 ± 0.04 and Hausdorff Distance of 3.38 ± 0.10 mm.
This study demonstrates a promising boundary tracking tool for delineating the tumor region that can deal with respiratory movement and the constraints of adaptive radiation therapy.
在影像引导放射治疗过程中，跟踪移动肿瘤区域是至关重要的，原因有二：（1）微小的误差会导致部分健康组织受到照射；（2）如果射束不能准确定位，不能覆盖整个癌变区域，那么一些癌细胞可能存活。然而，由于感兴趣区域与周围区域的光度相似，以及器官运动的影响，从磁共振成像（MRI）中跟踪或描绘这样的肿瘤区域具有挑战性。本研究旨在开发一种方法，通过自动描绘运动器官中感兴趣区域的边界来跟踪肿瘤区域的中心和边界，以进行放射治疗。
我们利用非刚性配准方法和一个公开的 RealTITracker 算法，从一系列 MRI 图像中描绘和跟踪肿瘤区域。由于呼吸，肿瘤区域在 MRI 图像序列中的位置和形状随时间变化。我们研究了两种方法：第一种方法在预处理阶段使用手动分割第一帧；第二种方法在预处理阶段使用所有帧的手动分割。
我们在从 6 名患者采集的 600 个图像序列上评估了所提出的方法。在预处理阶段使用所有帧并采用基于移动网格的配准的方法表现最佳，平均骰子分数为 0.89±0.04，Hausdorff 距离为 3.38±0.10 毫米。
本研究展示了一种有前途的边界跟踪工具，用于描绘肿瘤区域，可以处理呼吸运动和自适应放射治疗的限制。
