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And Tensor-Based Morphometry (UVTBM) Using Registration Confidence Western University Scholarship@Western Medical Biophysics Publications Medical Biophysics Department 1-2015 Unified voxel- and tensor-based morphometry (UVTBM) using registration confidence. Ali R Khan Western University, [email protected] Lei Wang Mirza Faisal Beg Follow this and additional works at: https://ir.lib.uwo.ca/biophysicspub Part of the Medical Biophysics Commons Citation of this paper: Khan, Ali R; Wang, Lei; and Beg, Mirza Faisal, "Unified voxel- and tensor-based morphometry (UVTBM) using registration confidence." (2015). Medical Biophysics Publications. 39. https://ir.lib.uwo.ca/biophysicspub/39 Neurobiology of Aging 36 (2015) S60eS68 Contents lists available at ScienceDirect Neurobiology of Aging journal homepage: www.elsevier.com/locate/neuaging High-dimensional morphometry Unified voxel- and tensor-based morphometry (UVTBM) using registration confidence Ali R. Khan a, Lei Wang b, Mirza Faisal Beg c,* a Department of Medical Biophysics, Robarts Research Institute, Western University, London, Ontario, Canada b Department of Psychiatry and Behavioral Sciences and Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA c School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada article info abstract Article history: Voxel-based morphometry (VBM) and tensor-based morphometry (TBM) both rely on spatial normali- Received 6 May 2013 zation to a template and yet have different requirements for the level of registration accuracy. VBM Received in revised form 16 April 2014 requires only global alignment of brain structures, with limited degrees of freedom in transformation, Accepted 24 April 2014 whereas TBM performs best when the registration is highly deformable and can achieve higher regis- Available online 30 August 2014 tration accuracy. In addition, the registration accuracy varies over the whole brain, with higher accuracy typically observed in subcortical areas and lower accuracy seen in cortical areas. Hence, even the Keywords: determinant of Jacobian of registration maps is spatially varying in their accuracy, and combining these MRI Brain registration with VBM by direct multiplication introduces errors in VBM maps where the registration is inaccurate. fi Morphometry We propose a uni ed approach to combining these 2 morphometry methods that is motivated by these Diffeomorphisms differing requirements for registration and our interest in harnessing the advantages of both. Our novel Atlases method uses local estimates of registration confidence to determine how to weight the influence of Voxel-based morphometry VBM- and TBM-like approaches. Results are shown on healthy and mild Alzheimer’s subjects (N ¼ 150) Tensor-based morphometry investigating age and group differences, and potential of differential diagnosis is shown on a set of Alzheimer’s disease (N ¼ 34) and frontotemporal dementia (N ¼ 30) patients compared against controls (N ¼ 14). These show that the group differences detected by our proposed approach are more descriptive than those detected from VBM, Jacobian-modulated VBM, and TBM separately, hence leveraging the advantages of both approaches in a unified framework. Ó 2015 Elsevier Inc. All rights reserved. 1. Introduction used to modulate the density maps. This procedure, known as Ja- cobian modulation, uses the determinant of the Jacobian derived Voxel-based morphometry (VBM) (Ashburner and Friston, 2000; from the transformations that register each brain to the common Mechelli et al., 2005) is a widely used whole-brain analysis method space (Davatzikos et al., 2001), thus dealing with gross-volume for characterizing morphologic shape differences using brain mag- differences that may exist and leading to a more natural interpre- netic resonance imaging (MRI) and has been useful for investigating tation of tissue volume with respect to the unwarped maps. This a wide range of neurological and psychiatric diseases, tracking spatial normalization commonly consists of an initial 12-parameter normal development of the brain, and assessing the structural effect affine registration followed by a nonlinear registration step. Because of neuroplasticity in the human brain. In essence, VBM is a voxel- the process of spatial normalization in VBM is meant to only globally wise comparison of tissue segmentation maps that have been align the brain, the registration is thus limited in the degrees of brought into global alignment (through spatial normalization) and freedom (dof) used to transform the images. Previous work has used smoothed with an isotropic Gaussian kernel. To deal with brain transformations modeled with a linear combination of smooth basis regions that may expand or contract through the spatial normali- functions, as in Ashburner and Friston (2000), or those spline pa- zation process, a local measure of contraction or expansion is often rameterizations with wide (>10 mm) control-point spacing, as in Douaud et al. (2007). Limiting the dof is essential to VBM without Jacobian modulation, because if the dof were high enough to allow LW and MFB have contributed equally to this work. for complete alignment of the anatomy, the differences would not * Corresponding author at: School of Engineering Science, Simon Fraser Univer- be detected in the gray-matter (GM) density maps (Thacker, 2005). sity, Burnaby, British Columbia, Canada V5A 1S6. Tel.: þ1 778 782 5696; fax: þ1778 782 4951. Note that depending on the type of anatomy being registered, E-mail address: [email protected] (M.F. Beg). complete alignment may or may not be achievable. For example, 0197-4580/$ e see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2014.04.036 A.R. Khan et al. / Neurobiology of Aging 36 (2015) S60eS68 S61 many regions in the cerebral cortex have individualized folding 2. Methods patterns that would confound complete alignment even with high dof, whereas many subcortical nuclei have simpler morphology and We will first describe our general framework for estimation of could be completely aligned given enough dof. registration confidence and unification of VBM and TBM based on A recent large-scale evaluation of brain registration carried out local measures of confidence in aligning tissue segmentations. by Klein et al. (2009) showed that methods with highly deform- Then, we will describe our implementation of this framework that able transformation models (high dof) perform significantly more involves a multistructure group-wise registration to an average accurate registration than those with limited dof. In particular, template. We applied our unified morphometry framework to an diffeomorphic registration algorithms that model the trans- open access set of 150 elderly subjects, healthy and Alzheimer’s formation as a smoothly varying fluid flow while ensuring disease (AD), and examined the effect of age and group, showing smoothness and invertibility (Avants et al., 2008; Beg et al., 2005; the results for various parameter choices. We also applied it to a Khan et al., 2013) have been shown to outperform competing database of frontotemporal dementia (FTD) and AD patients to methods in many situations. These methods in principle cannot be investigate the use of whole-brain morphometry in differential directly used with VBM because of the previous considerations; diagnosis of neurodegenerative disease patients. thus, morphometries based on these high dof registration tech- niques are deformation or tensor based, that is, statistics are 2.1. Estimating registration confidence performed on the deformation fields (deformation-based mor- phometry, Ashburner et al., 1998), or the Jacobian tensors of these In VBM and TBM, brain MRI is registered to a common tem- deformation fields (tensor-based morphometry [TBM], Davatzikos plate, where analysis of tissue density and deformation derivatives et al., 1996), and do not make use of the smoothed tissue seg- can take place. For a given set of subjects, we would like to esti- mentations. However, these techniques rely on a high degree of mate, at a spatially local level, the uncertainty in aligning the registration accuracy and may be underpowered in regions where anatomy, to provide an estimate of confidence. Estimating spatial registration accuracy is lower. uncertainty in registration is an open problem, and there has been It is known that issues such as the size, shape, and folding interest in recent years to better inform users in clinical applica- pattern of structures or local image contrast changes can effect the tions of registration. Hub et al. (2009) measured uncertainty by accuracy; the cortical regions of the brain pose significant chal- examining how random perturbations of the final registration lenges for even the most advanced brain registration algorithms transform affect a given cost function. Kybic (2010) used bootstrap (Klein et al., 2009), and, thus, uncertainty is typically higher in these resampling to estimate the uncertainty of the covariance matrix regions. Furthermore, in the case of disease-specific analysis, the and evaluated their approach on the simple case where trans- particular disease can potentially have very different spatial atro- formations are restricted to 2-dimensional translations. More phy profiles and, therefore, also influence the uncertainty in generally, spatial uncertainty in pairwise registrations can be registration as a function of location in the brain. In regions where modeled in a Bayesian framework as the variance in the posterior we have more uncertainty
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