We propose a new clustering algorithm. This algorithm performs clustering and manifold learning simultaneously by using a graph-theoretical approach to manifold learning. We apply this algorithm in order to cluster white matter fiber tracts obtained from Diffusion Tensor MRI (DT-MRI) through streamline tractography. Our algorithm is able perform clustering of these fiber tracts incorporating in...

We present a technique for visualizing the differences between two Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) volumes. Our technique registers two DT-MRIs and then produces a 3D model that allows a user to simultaneously view structure in both volumes. The geometric model is based on one developed to visually represent a single volume with a carefully chosen set of streamtubes [6,7]. ...

We present a technique for visualizing the differences between two diffusion tensor MR (DT-MRI) volumes. Our technique registers two DT-MRI’s and then produces a 3D model that allows a user to simultaneously view structure in each volume. The geometric model is based on an earlier one developed to visually represent a single volume with a carefully chosen set of streamtubes [6,7]. In the new mo...

Development of a diffusion tensor (DT) template that is representative of the micro-architecture of the human brain is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the generation of a detailed white matter atlas. Furthermore, a DT template in ICBM space may simplify consolidation of information from DT, anatomical and functio...

Background and Aim: The methods for detecting brain activation with fMRI, MRI provides a way to measure the anatomical connections which enable lightning-fast communication among neurons that specialize in different kinds of brain functions. Diffusion tensor imaging is able to measure the direction of bundles of the axonal fibers which are all aligned. Besides mapping white matter fiber tracts,...

In this chapter, we introduce the problem of registering diffusion tensor magnetic resonance (DT-MR) images. The registration task for these images is made challenging by the orientational information they contain, which is affected by the registration transformation. This information about orientation and other aspects of the diffusion tensor are exploited in the development of similarity meas...

Introduction Diffusion tensor MR imaging (DT-MRI) is sensitive to the preferential diffusion of water molecules along axonal fibers, a property called anisotropic diffusion (1,2) and is helpful to identify different tumor components and to differentiate tumor invasions from normal brain tissues or peritumoral edema (2). In routine clinical neuroimaging, MRI for brain tumors should be performed ...

OBJECTIVE To investigate the role of diffusion tensor magnetic resonance imaging (DT-MRI) in the evaluation of traumatic optic neuropathy (TON). METHODS Six patients with TON underwent DT-MRI prior to decompression surgery. DTV 2 and Volume One 1.44 software were used to measure the fractional anisotropy (FA) and the mean apparent diffusion coefficient (ADC) of the optic nerves. White matter ...

Image registration techniques which require image interpolation are widely used in neuroimaging research. We show that signal variance in interpolated images differs significantly from the signal variance of the original images in native space. We describe a simple approach to compute the signal variance in registered images based on the signal variance and covariance of the original images, th...

Conventional magnetic resonance imaging (MRI) allows investigators and clinicians to observe the anatomy and injuries of the cerebral white matter (CWM) in dogs. However, dynamic images based on the diffusion tensor (DT) technique are required to assess fiber tract integrity of the CWM. Diffusion tensor tractography (DTT) produces a three-dimensional representation in which data are displayed o...