An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy
An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy
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DOI：
10.1088/0031-9155/51/19/005
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发表时间：
2006-10-07
影响因子：
3.5
通讯作者：
Bendl, R.
中科院分区：
文献类型：
--
作者：
Malsch, U.;Thieke, C.;Bendl, R.
图像配准在诊断、治疗计划和治疗中有许多医学应用。特别是对于时间自适应放射治疗，非常期望对针对治疗计划以及在实际治疗时采集的图像进行有效且准确的弹性配准。因此，我们开发了一种全自动和快速的块匹配算法，该算法在3D CT数据集中识别一组解剖标志，并通过在频域中最大化局部相关系数将它们重新定位在另一个CT数据集中。为了转换完整的数据集，通过具有局部影响的修改的薄板样条计算地标之间的平滑插值。该算法的概念允许单独处理图像不连续性，如肠道或直肠中随时间变化的气腔。结果是完全转换的3D规划数据集（规划CT以及肿瘤和危险器官的描绘）到验证CT，允许基于当前患者解剖结构进行评估和（如有必要）更改治疗计划，而无需耗时的手动重新勾画轮廓。通常，总计算时间小于5分钟，这允许在采集验证图像和输送剂量分数之间使用配准工具进行在线校正。我们提出了验证的算法为五个不同的患者数据集与不同的肿瘤位置（前列腺，椎旁和头颈部）通过比较结果与手动选择的地标，视觉评估和一致性测试。事实证明，配准的平均误差优于体素分辨率（2 x 2 x 3 mm（3））。总之，我们提出了一种算法，全自动弹性图像配准，是精确和快速的在线校正，在自适应分割放射治疗过程中。
Image registration has many medical applications in diagnosis, therapy planning and therapy. Especially for time-adaptive radiotherapy, an efficient and accurate elastic registration of images acquired for treatment planning, and at the time of the actual treatment, is highly desirable. Therefore, we developed a fully automatic and fast block matching algorithm which identifies a set of anatomical landmarks in a 3D CT dataset and relocates them in another CT dataset by maximization of local correlation coefficients in the frequency domain. To transform the complete dataset, a smooth interpolation between the landmarks is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows separate processing of image discontinuities like temporally changing air cavities in the intestinal track or rectum. The result is a fully transformed 3D planning dataset (planning CT as well as delineations of tumour and organs at risk) to a verification CT, allowing evaluation and, if necessary, changes of the treatment plan based on the current patient anatomy without time-consuming manual re-contouring. Typically the total calculation time is less than 5min, which allows the use of the registration tool between acquiring the verification images and delivering the dose fraction for online corrections. We present verifications of the algorithm for five different patient datasets with different tumour locations ( prostate, paraspinal and head-and-neck) by comparing the results with manually selected landmarks, visual assessment and consistency testing. It turns out that the mean error of the registration is better than the voxel resolution (2 x 2 x 3 mm(3)). In conclusion, we present an algorithm for fully automatic elastic image registration that is precise and fast enough for online corrections in an adaptive fractionated radiation treatment course.