5,207 research outputs found
Orientation matching for diffusion tensor image registration.
This thesis develops a registration algorithm specifically for diffusion-tensor (DT) images. The proposed approach matches the tensor orientations to find the registration transformation. Early results show that local optimisation does not find the global minimum in registration of DT-MR brain images. Therefore, a global optimisation registration technique is also implemented. This thesis proposes several new similarity measures for DT registration and provides a comparison of them along with several others previously proposed in the literature. The thesis also proposes several new performance evaluation measures to assess registration quality and develops a performance evaluation framework that uses directional coherence and landmark separation. Experiments with direct optimisation demonstrate increased local minima in tensor registration objective functions over scalar registration. Using registration with global optimisation, this thesis compares the performance of scalar-derived similarity measures with those derived from the full tensor. Results suggest that similarity measures derived from the full tensor matrix do not find a more accurate registration than those based on the derived scalar indices. Affine and higher-order polynomial registration is not reliable enough to make a firm conclusion about whether diffusion tensor orientation matching improves the accuracy of registration over registration algorithms that ignore orientation. The main problem preventing a firm conclusion is that the local minima problem persists despite the use of global optimisation, causing poor registration of the regions of interest
Spatial transformations of diffusion tensor magnetic resonance images
The authors address the problem of applying spatial transformations (or “image warps”) to diffusion tensor magnetic resonance images. The orientational information that these images contain must be handled appropriately when they are transformed spatially during image registration. The authors present solutions for global transformations of three-dimensional images up to 12-parameter affine complexity and indicate how their methods can be extended for higher order transformations. Several approaches are presented and tested using synthetic data. One method, the preservation of principal direction algorithm, which takes into account shearing, stretching and rigid rotation, is shown to be the most effective. Additional registration experiments are performed on human brain data obtained from a single subject, whose head was imaged in three different orientations within the scanner. All of the authors' methods improve the consistency between registered and target images over naive warping algorithms
Diffeomorphic Metric Mapping of High Angular Resolution Diffusion Imaging based on Riemannian Structure of Orientation Distribution Functions
In this paper, we propose a novel large deformation diffeomorphic
registration algorithm to align high angular resolution diffusion images
(HARDI) characterized by orientation distribution functions (ODFs). Our
proposed algorithm seeks an optimal diffeomorphism of large deformation between
two ODF fields in a spatial volume domain and at the same time, locally
reorients an ODF in a manner such that it remains consistent with the
surrounding anatomical structure. To this end, we first review the Riemannian
manifold of ODFs. We then define the reorientation of an ODF when an affine
transformation is applied and subsequently, define the diffeomorphic group
action to be applied on the ODF based on this reorientation. We incorporate the
Riemannian metric of ODFs for quantifying the similarity of two HARDI images
into a variational problem defined under the large deformation diffeomorphic
metric mapping (LDDMM) framework. We finally derive the gradient of the cost
function in both Riemannian spaces of diffeomorphisms and the ODFs, and present
its numerical implementation. Both synthetic and real brain HARDI data are used
to illustrate the performance of our registration algorithm
SPHERE: SPherical Harmonic Elastic REgistration of HARDI data
In contrast to the more common Diffusion Tensor Imaging (DTI), High Angular Resolution Diffusion Imaging (HARDI) allows superior delineation of angular microstructures of brain white matter, and makes possible multiple-fiber modeling of each voxel for better characterization of brain connectivity. However, the complex orientation information afforded by HARDI makes registration of HARDI images more complicated than scalar images. In particular, the question of how much orientation information is needed for satisfactory alignment has not been sufficiently addressed. Low order orientation representation is generally more robust than high order representation, although the latter provides more information for correct alignment of fiber pathways. However, high order representation, when naïvely utilized, might not necessarily be conducive to improving registration accuracy since similar structures with significant orientation differences prior to proper alignment might be mistakenly taken as non-matching structures. We present in this paper a HARDI registration algorithm, called SPherical Harmonic Elastic REgistration (SPHERE), which in a principled means hierarchically extracts orientation information from HARDI data for structural alignment. The image volumes are first registered using robust, relatively direction invariant features derived from the Orientation Distribution Function (ODF), and the alignment is then further refined using spherical harmonic (SH) representation with gradually increasing orders. This progression from non-directional, single-directional to multi-directional representation provides a systematic means of extracting directional information given by diffusion-weighted imaging. Coupled with a template-subject-consistent soft-correspondence-matching scheme, this approach allows robust and accurate alignment of HARDI data. Experimental results show marked increase in accuracy over a state-of-the-art DTI registration algorithm
Bayesian Estimation of White Matter Atlas from High Angular Resolution Diffusion Imaging
We present a Bayesian probabilistic model to estimate the brain white matter
atlas from high angular resolution diffusion imaging (HARDI) data. This model
incorporates a shape prior of the white matter anatomy and the likelihood of
individual observed HARDI datasets. We first assume that the atlas is generated
from a known hyperatlas through a flow of diffeomorphisms and its shape prior
can be constructed based on the framework of large deformation diffeomorphic
metric mapping (LDDMM). LDDMM characterizes a nonlinear diffeomorphic shape
space in a linear space of initial momentum uniquely determining diffeomorphic
geodesic flows from the hyperatlas. Therefore, the shape prior of the HARDI
atlas can be modeled using a centered Gaussian random field (GRF) model of the
initial momentum. In order to construct the likelihood of observed HARDI
datasets, it is necessary to study the diffeomorphic transformation of
individual observations relative to the atlas and the probabilistic
distribution of orientation distribution functions (ODFs). To this end, we
construct the likelihood related to the transformation using the same
construction as discussed for the shape prior of the atlas. The probabilistic
distribution of ODFs is then constructed based on the ODF Riemannian manifold.
We assume that the observed ODFs are generated by an exponential map of random
tangent vectors at the deformed atlas ODF. Hence, the likelihood of the ODFs
can be modeled using a GRF of their tangent vectors in the ODF Riemannian
manifold. We solve for the maximum a posteriori using the
Expectation-Maximization algorithm and derive the corresponding update
equations. Finally, we illustrate the HARDI atlas constructed based on a
Chinese aging cohort of 94 adults and compare it with that generated by
averaging the coefficients of spherical harmonics of the ODF across subjects
Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
We present the concept of fiber-flux density for locally quantifying white
matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g.,
fractional anisotropy) with fiber-flux measurements, we define new local
descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each
descriptor throughout fiber bundles allows along-tract coupling of a specific
diffusion measure with geometrical properties, such as fiber orientation and
coherence. A key step in the proposed framework is the construction of an FFDD
dissimilarity measure for sub-voxel alignment of fiber bundles, based on the
fast marching method (FMM). The obtained aligned WM tract-profiles enable
meaningful inter-subject comparisons and group-wise statistical analysis. We
demonstrate our method using two different datasets of contact sports players.
Along-tract pairwise comparison as well as group-wise analysis, with respect to
non-player healthy controls, reveal significant and spatially-consistent FFDD
anomalies. Comparing our method with along-tract FA analysis shows improved
sensitivity to subtle structural anomalies in football players over standard FA
measurements
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