Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method
Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method
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DOI：
10.1088/0031-9155/50/10/n01
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发表时间：
2005-05-21
影响因子：
3.5
通讯作者：
Kotte, ANTJ
中科院分区：
文献类型：
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作者：
Ceylan, C;van der Heide, UA;Kotte, ANTJ
在许多临床应用中使用不同成像模态（诸如CT、MRI、功能性MRI（fMRI）、正电子（PET）和单光子（SPECT）发射断层扫描）的配准。确定任何自动配准程序的质量一直是一个具有挑战性的部分，因为没有金标准可用于评估配准。在本说明中，我们提出了一种方法，称为“多子体积配准”（MSR）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配准到另一个数据集上来完成的，执行粗略的非刚性配准。通过分析子体积配准与完全配准的偏差（局部变形），我们得到刚性配准一致性的度量。15个数据集，其中包括CT，MR和PET图像的大脑，头部和颈部，子宫颈，前列腺和肺部的注册进行利用刚体配准与nonnalized互信息作为相似性度量。通过目视检查将所得配准分为良好或不良。由此产生的注册也使用我们的MSR方法进行了分类。我们的MSR方法的结果与所有病例的目视检查所得分类一致（基于好组和坏组的ANOVA，p < 0.02）。该方法不依赖于配准算法和相似性度量。它可用于多模态图像数据集和患者的不同解剖部位。
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.