Registration of Diffusion Tensor Images in Log-Euclidean and Euclidean Space

Diffusion Tensor Imaging is a type of Magnetic Resonance Imaging that allows for the examination of brain connectivity and axonal integrity. Diffusion Tensor Images are created by capturing Diffusion-Weighted MRI images with specific RF pulses, inputing the images and the RF pulse gradient vectors into a set of equations, and solving the equations with linear algebra. To compare one DTI image with another, the images can be aligned using Image Registration. Image Registration works by defining a metric that describes the similarity between two images and iteratively transforming one of the images until the similarity measure is minimized. Existing methods of DTI comparison fit tensors to DW-MRI images, compute matrix logarithms to transform the tensors into a vector-space, register the vector-space structures, and then matrix exponentiate the results to transform them back to tensors. Logging and exponentiating the tensors introduces biases and noise so this registration framework is not ideal. Additionally, the information encoded in a diffusion tensor is a subset of the information present in the original DW-MRI images. This thesis proposes a new registration framework which avoids these shortcomings by registering the underlying DW-MRI images and then fitting the diffusion tensors to the registered DW-MRI images and transformed gradient vectors. The existing DTI registration framework and the new DW-MRI registration framework are applied to a small image set and their results are compared along a number of qualitative and quantitative attributes

3D tensor normalization for improved accuracy in DTI tensor registration methods

pre-printThis paper presents a method for normalization of diffusion tensor images (DTI) to a xed DTI template, a pre-processing step to improve the performance of full tensor based registration methods. The proposed method maps the individual tensors of the subject image in to the template space based on matching the cumulative distribution function and the fractional anisotrophy values. The method aims to determine a more accurate deformation field from any full tensor registration method by applying the registration algorithm on the normalized DTI rather than the original DTI. The deformation field applied to the original tensor images are compared to the deformed image without normalization for 11 different cases of mapping seven subjects (neonate through 2 years) to two different atlases. The method shows an improvement in DTI registration based on comparing the normalized fractional anisotropy values of major fiber tracts in the brain

Multimodality and Nonrigid Image Registration with Application to Diffusion Tensor Imaging

The great challenge in image registration is to devise computationally efficient algorithms for aligning images so that their details overlap accurately. The first problem addressed in this thesis is multimodality medical image registration, which we formulate as an optimization problem in the information-theoretic setting. We introduce a viable and practical image registration method by maximizing a generalized entropic dissimilarity measure using a modified simultaneous perturbation stochastic approximation algorithm. The feasibility of the proposed image registration approach is demonstrated through extensive experiments. The rest of the thesis is devoted to nonrigid medical image registration. We propose an informationtheoretic framework by optimizing a non-extensive entropic similarity measure using the quasi-Newton method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between the fixed and moving 3D image pairs. To achieve a compromise between the nonrigid registration accuracy and the associated computational cost, we implement a three-level hierarchical multi-resolution approach in such a way that the image resolution is increased in a coarse to fine fashion. The feasibility and registration accuracy of the proposed method are demonstrated through experimental results on a 3D magnetic resonance data volume and also on clinically acquired 4D computed tomography image data sets. In the same vein, we extend our nonrigid registration approach to align diffusion tensor images for multiple components by enabling explicit optimization of tensor reorientation. Incorporating tensor reorientation in the registration algorithm is pivotal in wrapping diffusion tensor images. Experimental results on diffusion-tensor image registration indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information, not only in terms of registration accuracy in the presence of geometric distortions but also in terms of robustness in the presence of Rician noise

Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration

Diffusion tensor based cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion. Registration is challenging in DT-CMR due to the low signal-to-noise and various contrasts induced by the diffusion encoding in the myocardial and surrounding organs. Traditional deformable registration destroys the texture information while rigid registration inefficiently discards frames with local deformation. In this study, we explored the possibility of deep learning-based deformable registration on DT- CMR. Based on the noise suppression using low-rank features and diffusion encoding suppression using variational auto encoder-decoder, a B-spline based registration network extracted the displacement fields and maintained the texture features of DT-CMR. In this way, our method improved the efficiency of frame utilization, manual cropping, and computational speed.Comment: 4 pages, 4 figures, conferenc

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

Diffusion tensor driven image registration: a deep learning approach

Tracking microsctructural changes in the developing brain relies on accurate inter-subject image registration. However, most methods rely on either structural or diffusion data to learn the spatial correspondences between two or more images, without taking into account the complementary information provided by using both. Here we propose a deep learning registration framework which combines the structural information provided by T2-weighted (T2w) images with the rich microstructural information offered by diffusion tensor imaging (DTI) scans. We perform a leave-one-out cross-validation study where we compare the performance of our multi-modality registration model with a baseline model trained on structural data only, in terms of Dice scores and differences in fractional anisotropy (FA) maps. Our results show that in terms of average Dice scores our model performs better in subcortical regions when compared to using structural data only. Moreover, average sum-of-squared differences between warped and fixed FA maps show that our proposed model performs better at aligning the diffusion data

A comparison of methods for the registration of tractographic fibre images

Diffusion tensor imaging (DTI) and tractography have opened up new avenues in neuroscience. As most applications require precise spatial localization of the fibre images, image registration is an important area of research. Registration is usually performed prior to tractography. However more reliable images could be produced if a viable registration can be performed post tractography. This study shows two available techniques for direct registration of fibre images and explores novel adaptations of these. The methods register volume images derived from the fibres, and reapply the transformation from these registrations to the fibre images. The first method is a local affine registration and the second is a global affine registration. The local affine method produced superior results

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.ope

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