We present a methodology to achieve 3D high-resolution diffusion tensor image reconstruction of the brain in moving subjects. The source data is diffusion-sensitized single-shot echo-planar images. After continuous scanning to acquire a repeated series of parallel slices with 15 diffusion directions, image registration is used to realign the images to correct for subject motion. Once aligned, t...

Introduction Diffusion-weighted spectroscopy and diffusion-weighted spectroscopic imaging (DWSI) are expected to provide useful information about tissue microstructures and functions [1]. The diffusion tensor of metabolites is expected to provide more specific microstructures of such neurons; however, only a few studies using diffusion tensor spectroscopy (DTS) [2,3] and no studies using diffus...

Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image interpretation. The methods proposed ...

We present a variational framework for the registration of tensorvalued images. It is based on an energy functional with four terms: a data term based on a diffusion tensor constancy constraint, a compatibility term encoding the physical model linking domain deformations and tensor reorientation, and smoothness terms for deformation and tensor reorientation. Although the tensor deformation mode...

In this paper, we present an affine image coregistration technique for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data sets based on mutual information. The technique is based on a multichannel approach where the diffusion weighted images are aligned according to the corresponding acquisition gradient directions. Also, in addition to the coregistration of the DT-MRI data sets, an appr...

We propose a novel method for deformable tensor-to-tensor registration of Diffusion Tensor Imaging (DTI) data. Our registration method considers estimated diffusion tensors as normally distributed random variables whose covariance matrices describe uncertainties in the mean estimated tensor due to factors such as noise in diffusion weighted images (DWIs), tissue diffusion properties, and experi...

Hemisphere asymmetry was explored in normal healthy subjects and in patients with schizophrenia using a novel voxel-based tensor analysis applied to fractional anisotropy (FA) of the diffusion tensor. Our voxel-based approach, which requires precise spatial normalization to remove the misalignment of fiber tracts, includes generating a symmetrical group average template of the diffusion tensor ...

There is a need for an imaging sequence that can provide high-resolution diffusion tensor images at 3T near air-tissue interfaces. By employing a radial fast spin-echo (FSE) collection in conjunction with magnitude filtered back-projection reconstruction, high-resolution diffusion-weighted images can be produced without susceptibility artifacts. However, violation of the Carr-Purcell-Meiboom-Gi...

Diffusion tensor imaging provides information about deep white matter anatomy that structural magnetic resonance images typically fail to resolve. Non-linear registration of diffusion tensor images, for which a few methods already exist, allows us to capture the deformations of these structures that would otherwise go unobserved. Here, we build on an existing method for diffeomorphic registrati...

materials and methods the segmentation process is evaluated using four different clustering methods with different number of clusters where some dti scalar indices for 10 human brains are processed. results the aim was to produce results with less segmentation error and a lower computational cost while attempting to minimizing boundary overlapping and minimizing the effect of artifacts due to m...