Automated delineation of white matter fiber tracts with a multiple region-of-interest approach

White matter fiber bundles of the brain can be delineated by tractography utilizing multiple regions-of-interest (MROI) defined by anatomical landmarks. These MROI can be used to specify regions in which to seed, select, or reject tractography fibers. Manual identification of anatomical MROI enables the delineation of white matter fiber bundles, but requires considerable training to develop expertise, considerable time to carry out and suffers from unwanted inter- and intra-rater variability. In a study of 20 healthy volunteers, we compared three methodologies for automated delineation of the white matter fiber bundles. Using these methodologies, fiber bundle MROI for each volunteer were automatically generated. We assessed three strategies for inferring the automatic MROI utilizing nonrigid alignment of reference images and projection of template MROI. We assessed the bundle delineation error associated with alignment utilizing T1-weighted MRI, fractional anisotropy images, and full tensor images. We confirmed the smallest delineation error was achieved using the full tensor images. We then assessed three projection strategies for automatic determination of MROI in each volunteer. Quantitative comparisons were made using the root-mean-squared error observed between streamline density images constructed from fiber bundles identified automatically and by manually drawn MROI in the same subjects. We demonstrate that a multiple template consensus label fusion algorithm generated fiber bundles most consistent with the manual reference standard.     

Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework

We propose a novel approach for quantitative shape variability analysis in retinal optical coherence tomography images using the functional shape (fshape) framework. The fshape framework uses surface geometry together with functional measures, such as retinal layer thickness defined on the layer surface, for registration across anatomical shapes. This is used to generate a population mean template of the geometry-function measures from each individual. Shape variability across multiple retinas can be measured by the geometrical deformation and functional residual between the template and each of the observations. To demonstrate the clinical relevance and application of the framework, we generated atlases of the inner layer surface and layer thickness of the Retinal Nerve Fiber Layer (RNFL) of glaucomatous and normal subjects, visualizing detailed spatial pattern of RNFL loss in glaucoma. Additionally, a regularized linear discriminant analysis classifier was used to automatically classify glaucoma, glaucoma-suspect, and control cases based on RNFL fshape metrics.     

GRAM: A framework for geodesic registration on anatomical manifolds

Medical image registration is a challenging problem, especially when there is large anatomical variation in the anatomies. Geodesic registration methods have been proposed to solve the large deformation registration problem. However, analytically defined geodesic paths may not coincide with biologically plausible paths of registration, since the manifold of diffeomorphisms is immensely broader than the manifold spanned by diffeomorphisms between real anatomies. In this paper, we propose a novel framework for large deformation registration using the learned manifold of anatomical variation in the data. In this framework, a large deformation between two images is decomposed into a series of small deformations along the shortest path on an empirical manifold that represents anatomical variation. Using a manifold learning technique, the major variation of the data can be visualized by a low-dimensional embedding, and the optimal group template is chosen as the geodesic mean on the manifold. We demonstrate the advantages of the proposed framework over direct registration with both simulated and real databases of brain images.     

Variable precision registration via wavelets: optimal spatial scales for inter-subject registration of functional MRI

The detection of significantly activated brain regions in multi-subject functional magnetic resonance imaging (fMRI) studies almost invariably entails the coregistration of individual subjects' data in a standard space. Here, we investigate how sensitivity to detect loci of generic activation in such studies may be conditioned by the precision of anatomical registration. We describe a novel algorithm, implemented in the wavelet domain, for inhomogeneous deformation of individual images to match a template. The algorithm matches anatomical features in a coarse-to-fine fashion, first minimising a cost function in terms of relatively coarse spatial features and then proceeding iteratively to match the images in terms of progressively more detailed anatomical features. Applying the method to data acquired from two groups of 12 healthy volunteers (with mean age 27 and 70 years, respectively), during performance of a paired associate learning task, we show that geometrical overlap between template and individual images is monotonically improved, compared to an affine transform, by additional inhomogeneous deformations informed by more detailed features. Likewise, sensitivity to detect activated voxels can be substantially improved, by a factor of 4 or more, if wavelet-mediated deformations informed by medium-sized anatomical features are applied in addition to a preliminary affine transform. However, sensitivity to detect activated voxels was reduced by "over-registering" data or matching anatomical features at the finest scales of the wavelet transform. The benefits of variable precision registration are particularly salient for data acquired in older subjects, which showed evidence of greater inter-subject anatomic variability and generally required more extensive local deformation to achieve a satisfactory match to the template image. We conclude that major benefits in sensitivity to detect functional activation in multi-subject fMRI studies can be attained with an inhomogeneous deformation applied over appropriate spatial scales.     

Evaluation of fiber bundles across subjects through brain mapping and registration of diffusion tensor data

This paper presents a visualization and analysis framework for evaluating changes in structural organization of fiber bundles in human brain white matter. Statistical analysis of fiber bundle organization is conducted using an anisotropy measure, volume ratio (VR), which is ratio of anisotropic and isotropic components. Initially fiber bundles are tracked using a probabilistic algorithm starting from seed voxels. To ensure accurate selection of seed voxels and to prevent operator bias, a reference brain (MNI_152) is used when marking ROIs. Individual structural MRI brain scans are mapped to the reference using volumetric conformal parameterization. This mapping preserves topology and aligns features perfectly making it a robust and accurate registration technique. One-to-one mapping to the template allows ROI selection and subsequent transfer of ROI to structural MRI of subject. Affine registration coregisters structural MRI and DTI. Seed voxels are mapped to DTI using the resulting transformation parameters. To evaluate the proposed approach, MRI and DTI of 12 normal volunteers and 15 medial temporal lobe epilepsy patients are used. First, a statistical hypothesis testing is conducted to test for anisotropy changes in cingulum and fornix fiber bundles of epileptic patients. Experimental results reveal a 40% decrease in anisotropy levels of cingulum in patients compared to volunteers. They also show a 25% overall decrease in anisotropy of fornix. Secondly, shapes of the bundles are visualized in 3D illustrating that the bundles of epileptic patients are bumpy while those of normal volunteers are smooth.     

A hybrid approach to automatic clustering of white matter fibers

Recently, the tract-based white matter (WM) fiber analysis has been recognized as an effective framework to study the diffusion tensor imaging (DTI) data of human brain. This framework can provide biologically meaningful results and facilitate the tract-based comparison across subjects. However, due to the lack of quantitative definition of WM bundle boundaries, the complexity of brain architecture and the variability of WM shapes, clustering WM fibers into anatomically meaningful bundles is nontrivial. In this paper, we propose a hybrid top-down and bottom-up approach for automatic clustering and labeling of WM fibers, which utilizes both brain parcellation results and similarities between WM fibers. Our experimental results show reasonably good performance of this approach in clustering WM fibers into anatomically meaningful bundles.     

Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms

Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and intra-individual deformations. The paper first describes a statistical approach to capturing inter-individual variability of high-deformation fields from a number of examples (training samples). In this approach, Wavelet-Packet Transform (WPT) of the training deformations and their Jacobians, in conjunction with a Markov random field (MRF) spatial regularization, are used to capture both coarse and fine characteristics of the training deformations in a statistical fashion. Simulated deformations can then be constructed by randomly sampling the resultant statistical distribution in an unconstrained or a landmark-constrained fashion. The paper also describes a model for generating tissue atrophy or growth in order to simulate intra-individual brain deformations. Several sets of simulated deformation fields and respective images are generated, which can be used in the future for systematic and extensive validation studies of automated atlas-based segmentation and deformable registration methods. The code and simulated data are available through our Web site.     

RABBIT: Rapid alignment of brains by building intermediate templates

A brain image registration algorithm, referred to as RABBIT, is proposed to achieve fast and accurate image registration with the help of an intermediate template generated by a statistical deformation model. The statistical deformation model is built by principal component analysis (PCA) on a set of training samples of brain deformation fields that warp a selected template image to the individual brain samples. The statistical deformation model is capable of characterizing individual brain deformations by a small number of parameters, which is used to rapidly estimate the brain deformation between the template and a new individual brain image. The estimated deformation is then used to warp the template, thus generating an intermediate template close to the individual brain image. Finally, the shape difference between the intermediate template and the individual brain is estimated by an image registration algorithm, e.g., HAMMER. The overall registration between the template and the individual brain image can be achieved by directly combining the deformation fields that warp the template to the intermediate template, and the intermediate template to the individual brain image. The algorithm has been validated for spatial normalization of both simulated and real magnetic resonance imaging (MRI) brain images. Compared with HAMMER, the experimental results demonstrate that the proposed algorithm can achieve over five times speedup, with similar registration accuracy and statistical power in detecting brain atrophy.     

Spatial normalization of diffusion tensor MRI using multiple channels

Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.     

A macroscopic anatomical study of the appropriate palpation zone of the gluteus medius muscle

[Purpose] Few previous studies have delimitated the palpation zone of the gluteus medius muscle with a focus on its fiber bundles. The purpose of this study was to clarify the morphological characteristics of the gluteus medius muscle using an anatomical approach, and to define its proper palpation zone. [Participants and Methods] In this study, we evaluated thirteen halves of the pelvic region in seven formalin-fixed cadavers. We identified the borders between the iliotibial band and gluteus medius muscle, and between the gluteus medius and gluteus maximus muscles, on the iliac crest. Furthermore, we quantified the border points of the gluteus medius’ fiber bundles and observed its anatomical and morphological characteristics. [Results] We identified two fiber bundles in the gluteus medius muscle, an anterior and a posterior fiber bundle, and detected that a portion of the posterior fibers was located subcutaneously. [Conclusion] We propose that the region where the posterior fibers of the gluteus medius muscle are located subcutaneously is an appropriate zone for the palpation of this muscle.     

Interindividual functional mapping: a nonlinear local approach

Within the scope of three-dimensional brain imaging, we propose an interindividual fusion scheme to register functional activations according to anatomical cortical structures, the sulci. This paper is based on the assumption that an important part of functional intersubject variability is encoded in anatomical variability. The aim of this paper is therefore to propose a generic framework to register functional activations according to the relevant anatomical landmarks. Compared to "classical" interindividual fusion schemes, this approach is local. It relies on a statistical sulci shape model accounting for the interindividual variability of a population of subjects and providing deformation modes relative to a reference shape (a mean sulcus). The deformation field obtained between a given sulcus and the reference sulcus is extended to a neighborhood of the given sulcus by using the thin-plate spline interpolation. It is then applied to functional activations located in the vicinity of this sulcus. This approach is compared with rigid and nonrigid registration methods. In this paper, we present results on MEG somatosensory data acquired on 18 subjects. We show that the nonlinear local fusion scheme significantly reduces the observed functional variability.     

Detection and statistical analysis of human cortical sulci

Many studies dealing with the human brain use the spatial coordinate system of brain anatomy to localize functional regions. Unfortunately, brain anatomy, and especially cortical sulci, is characterized by a high interindividual variability. Specific tools called anatomical atlases must then be considered to make the interpretation of anatomical examinations easier. The work described here first aims at building a numerical atlas of the main cortical sulci. Our system is based on a database containing a collection of anatomical MRI of healthy volunteer brains. Their sulci have been manually drawn and labeled for both hemispheres. Sulci are represented as 3D superficial curves. After a nonlinear registration process, a statistical atlas of the cortical topography of a particular MRI is built from the database. It is an a priori model of cortical sulci, including three major components: an average curve represents the average shape and position of each sulcus; a search area accounts for its spatial variation domain; a set of quantitative parameters describes the variability of sulci geometry and topology. This atlas is completely individualized and adapted to the features of the brain under examination. The atlas is represented by a graph, the nodes of which represent sulci and the edges the relations between sulci. It can also be considered a statistical model that describes the cortical topography as well as its variability.     

A framework for callosal fiber distribution analysis

This paper presents a framework for analyzing the spatial distribution of neural fibers in the brain, with emphasis on interhemispheric fiber bundles crossing through the corpus callosum. The proposed approach combines methodologies for fiber tracking and spatial normalization and is applied on diffusion tensor images and standard magnetic resonance images.     

Registration of longitudinal brain image sequences with implicit template and spatial-temporal heuristics

Accurate measurement of longitudinal changes of brain structures and functions is very important but challenging in many clinical studies. Also, across-subject comparison of longitudinal changes is critical in identifying disease-related changes. In this paper, we propose a novel method to meet these two requirements by simultaneously registering sets of longitudinal image sequences of different subjects to the common space, without assuming any explicit template. Specifically, our goal is to 1) consistently measure the longitudinal changes from a longitudinal image sequence of each subject, and 2) jointly align all image sequences of different subjects to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal image sequence. Then, a probabilistic model is built upon the temporal fibers to characterize both spatial smoothness and temporal continuity. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of the probabilistic models. Promising results have been obtained in quantitative measurement of longitudinal brain changes, i.e., hippocampus volume changes, showing better performance than those obtained by either the pairwise or the groupwise only registration methods.     

Longitudinal change detection: Inference on the diffusion tensor along white matter pathways

Diffusion weighted magnetic resonance imaging (DW-MRI) makes it possible to probe brain connections in vivo. This paper presents a change detection framework that relies on white matter pathways with application to neuromyelitis optica (NMO). The objective is to detect local or global fiber diffusion property modifications between two longitudinal DW-MRI acquisitions of a patient. To this end, we develop two frameworks based on statistical tests on tensor eigenvalues to detect local or global changes along the white matter pathways: a pointwise test that compares tensor populations extracted in bundles cross sections and a fiberwise test that compares paired tensors along all the fiber bundles. Experiments on both synthetic and real data highlight the benefit of considering fiber based statistical tests compared to standard voxelwise strategies.     

A fast diffeomorphic image registration algorithm

This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.     

A neonatal atlas template for spatial normalization of whole-brain magnetic resonance images of newborns: preliminary results

Commonly used brain templates are based on adults' or children's brains. In this study, we create a neonatal brain template. This becomes necessary because of the pronounced differences not only in size but even more importantly in geometrical proportions of the brains of adults and children as compared to the ones of newborns. The template is created based on high resolution T1 magnetic resonance images of 7 individuals with gestational ages between 39 and 42 weeks at the dates of examination. As usual, the created template presents two characteristics in a single image: an average intensity and an average shape. The normalization process to map subjects to the same space is done using SPM2 (Statistical Parametric Mapping) and its deformation toolbox. It consists of two steps: an affine and a nonlinear registration for global and local alignments, respectively. The template was evaluated by (i) study of anatomical local deviations and (ii) amount of local deformations of brain tissues in normalized neonatal images. The extracted results were compared with the ones obtained by normalization using adult and pediatric templates. It was shown that the application of our neonatal brain template for alignment of neonatal images results in a pronounced increase in performance of the normalization procedure as indicated by reduction of deviation of anatomical equivalent structures. The neonatal atlas template is freely downloadable from http://www.u-picardie.fr/labo/GRAMFC.     

Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping

This paper proposes a 3D statistical model aiming at effectively capturing statistics of high-dimensional deformation fields and then uses this prior knowledge to constrain 3D image warping. The conventional statistical shape model methods, such as the active shape model (ASM), have been very successful in modeling shape variability. However, their accuracy and effectiveness typically drop dramatically in high-dimensionality problems involving relatively small training datasets, which is customary in 3D and 4D medical imaging applications. The proposed statistical model of deformation (SMD) uses wavelet-based decompositions coupled with PCA in each wavelet band, in order to more accurately estimate the pdf of high-dimensional deformation fields, when a relatively small number of training samples are available. SMD is further used as statistical prior to regularize the deformation field in an SMD-constrained deformable registration framework. As a result, more robust registration results are obtained relative to using generic smoothness constraints on deformation fields, such as Laplacian-based regularization. In experiments, we first illustrate the performance of SMD in representing the variability of deformation fields and then evaluate the performance of the SMD-constrained registration, via comparing a hierarchical volumetric image registration algorithm, HAMMER, with its SMD-constrained version, referred to as SMD+HAMMER. This SMD-constrained deformable registration framework can potentially incorporate various registration algorithms to improve robustness and stability via statistical shape constraints.