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Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method

机译:使用多子体积配准(MSR)方法评估刚性多模态图像配准一致性

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摘要

Registration of different imaging modalities such as CT, MRI, functional MRI (fMRI), positron (PET) and single photon (SPECT) emission tomography is used in many clinical applications. Determining the quality of any automatic registration procedure has been a challenging part because no gold standard is available to evaluate the registration. In this note we present a method, called the 'multiple sub-volume registration' (MSR) method, for assessing the consistency of a rigid registration. This is done by registering sub-images of one data set on the other data set, performing a crude non-rigid registration. By analysing the deviations (local deformations) of the sub-volume registrations from the full registration we get a measure of the consistency of the rigid registration. Registration of 15 data sets which include CT, MR and PET images for brain, head and neck, cervix, prostate and lung was performed utilizing a rigid body registration with nonnalized mutual information as the similarity measure. The resulting registrations were classified as good or bad by visual inspection. The resulting registrations were also classified using our MSR method. The results of our MSR method agree with the classification obtained from visual inspection for all cases (p < 0.02 based on ANOVA of the good and bad groups). The proposed method is independent of the registration algorithm and similarity measure. It can be used for multi-modality image data sets and different anatomic sites of the patient.
机译:CT,MRI,功能性MRI(fMRI),正电子(PET)和单光子(SPECT)发射断层扫描等不同成像方式的配准在许多临床应用中使用。由于没有黄金标准可用于评估注册,因此确定任何自动注册程序的质量一直是一项具有挑战性的工作。在本说明中,我们提出了一种称为“多个子卷配准”(MSR)方法的方法,用于评估刚性配准的一致性。这是通过将一个数据集的子图像注册到另一数据集上,执行粗略的非刚性注册来完成的。通过分析子体积配准与完全配准的偏差(局部变形),我们可以测量出刚性配准的一致性。使用刚体注册并使用未归纳的互信息作为相似性度量,进行15个数据集的注册,包括针对大脑,头部和颈部,子宫颈,前列腺和肺部的CT,MR和PET图像。通过视觉检查将所得注册分类为好或坏。产生的注册也使用我们的MSR方法进行分类。我们的MSR方法的结果与从所有情况下的目视检查得出的分类一致(基于优缺点组的ANOVA,p <0.02)。所提出的方法独立于配准算法和相似性度量。它可以用于多模式图像数据集和患者的不同解剖部位。

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