Ceylan C, van der Heide U A, Bol G H, Lagendijk J J W, Kotte A N T J
Department of Radiotherapy, University Medical Center Utrecht, The Netherlands.
Phys Med Biol. 2005 May 21;50(10):N101-8. doi: 10.1088/0031-9155/50/10/N01. Epub 2005 May 5.
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 normalized 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.
不同成像模态的配准,如CT、MRI、功能磁共振成像(fMRI)、正电子发射断层扫描(PET)和单光子发射断层扫描(SPECT),在许多临床应用中都有使用。由于没有金标准可用于评估配准,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。在本笔记中,我们提出了一种称为“多子体积配准”(MSR)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配准到另一个数据集上,执行粗略的非刚性配准来实现的。通过分析子体积配准与全配准的偏差(局部变形),我们得到了刚性配准一致性的度量。利用以归一化互信息为相似性度量的刚体配准,对包括脑、头颈部、子宫颈、前列腺和肺部的CT、MR和PET图像在内的15个数据集进行了配准。通过目视检查将得到的配准分为好或坏。还使用我们的MSR方法对得到的配准进行分类。我们的MSR方法的结果与所有病例通过目视检查获得的分类一致(基于好坏组的方差分析,p < 0.02)。所提出的方法独立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。