Clinical validation of the normalized mutual information method for registration of CT and MR images in radiotherapy of brain tumors.
Clinical validation of the normalized mutual information method for registration of CT and MR images in radiotherapy of brain tumors.
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DOI：
10.1120/jacmp.2021.25277
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发表时间：
2004-01-01
影响因子：
2.1
通讯作者：
Huizenga, Henk
中科院分区：
文献类型：
--
作者：
Veninga, Theo;Huisman, Henkjan;Huizenga, Henk
图像配准整合了不同成像方式的信息，有可能改善放射治疗计划中靶区的确定。本文描述了脑肿瘤放射治疗计划过程中3D全自动配准程序的实施和验证。 15 名患有各种脑肿瘤的患者在放疗开始前接受了 CT 和 MR 脑成像。归一化互信息（NMI）方法用于图像配准。通过对 CT 和 MR 解剖标志沿 x、y 和 z 轴的坐标差异进行统计分析来估计配准精度。其次，开发了视觉验证协议来验证各个配准解决方案的质量，并且该协议在一系列 36 个 CT-MR 配准程序中进行了测试，并故意施加了配准错误。 CT 和 MR 标志点沿 x 轴和 y 轴的平均坐标差一般在 0.5 毫米以内。沿 z 轴的平均坐标差在 1.0 mm 以内，与扫描中应用的切片厚度具有相同的幅度。其次，通过采用标准化视觉验证协议来检测有意应用的配准错误，从而导致低假阴性和低假阳性率。 NMI方法在大脑中的应用具有出色的自动配准精度，并且该方法已纳入我们研究所的日常工作中。提出了标准化验证协议，通过高灵敏度和特异性检测配准错误来确保个体配准的质量。该协议是为了验证其他线性配准方法而提出的。
Image registration integrates information of different imaging modalities and has the potential to improve target volume determination in radiotherapy planning. This paper describes the implementation and validation of a 3D fully automated registration procedure in the process of radiotherapy treatment planning of brain tumors. 15 Patients with various brain tumors received CT and MR brain imaging before the start of radiotherapy. The normalized mutual information (NMI) method was used for image registration. Registration accuracy was estimated by performing statistical analysis of coordinate differences between CT and MR anatomical landmarks along the x-, y- and z-axes. Second, a visual validation protocol was developed to validate the quality of individual registration solutions and this protocol was tested in a series of 36 CT-MR registration procedures with intentionally applied registration errors. The mean coordinate differences between CT and MR landmarks along the x- and y-axes were in general within 0.5 mm. The mean coordinate differences along the z-axis were within 1.0 mm, which is of the same magnitude as the applied slice thickness in scanning. Second, the detection of intentionally applied registration errors by employment of a standardized visual validation protocol resulted in low false-negative and low false-positive rates. Application of the NMI method for the brain results in excellent automatic registration accuracy and the method has been incorporated in daily routine within our institute. A standardized validation protocol is proposed that ensures the quality of individual registrations by detecting registration errors with high sensitivity and specificity. This protocol is proposed for the validation of other linear registration methods.