In this paper we present a novel Bayesian approach for fractional segmentation of white matter tracts and simultaneous estimation of a multi-tensor diffusion model. Our model consists of several white matter tracts, each with a corresponding weight and tensor compartment in each voxel. By incorporating a prior that assumes the tensor fields inside each tract are spatially correlated, we are abl...

Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an indirect measure of neuronal activity by detecting blood flow changes, has experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable f...

We consider dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated times and at each time a functional observation (curve) is recorded. We propose a novel parsimonious modeling framework for repeatedly observed functional observations that allows to extract low dimensional features. The proposed methodology accounts for the lon...

Diffusion tensor imaging (DTI) is a powerful tool in the study of the course of nerve fiber bundles in the human brain. Using DTI, the local fiber orientation in each image voxel can be described by a diffusion tensor which is constructed from local measurements of diffusion coefficients along several directions. The measured diffusion coefficients and thereby the diffusion tensors are subject ...

Abstract. We present new scalar measures for diffusion-weighted MRI visualization which are based on operations of tensor calculus and have a connection to topological visualization. These operators are generalizations of the familiar divergence and curl operations in vector calculus. We also present a method for computing the Helmholtz decomposition of tensor fields which can make the new scal...

Diffusion tensor imaging (DTI) data differ fundamentally from most brain imaging data in that values at each voxel are not scalars but 3 x 3 positive definite matrices also called diffusion tensors. Frequently, investigators simplify the data analysis by reducing the tensor to a scalar, such as fractional anisotropy (FA). New statistical methods are needed for analyzing vector and tensor valued...

As clinicians and scientists, we believe scientific evidence and prudent clinical practice form the proper basis for determining the utility of diagnostic measures, which should subsequently inform forensic use. The misleading and often entirely unsubstantiated opinions and positions of Wortzel et al., in opposition to DTI as a useful measure in mTBI, are at odds with the clear consensus of the...

A somewhat recent extension to traditional MRI imaging techniques, Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) measures the self-diffusion of water in biological tissue. Neural fiber tracts contain bundles of parallel axons whose membranes restrict diffusion, so the self-diffusion of water occurs preferentially along the tracts. Thus in DT-MRI imagery of the brain, each diffusion tenso...

PURPOSE To prospectively evaluate several diffusion-tensor magnetic resonance (MR) imaging indexes (mean diffusivity [MD], fractional anisotropy [FA], and eigenvalues) of corticospinal tract impairment in patients with progressive muscular atrophy (PMA) and patients with amyotrophic lateral sclerosis (ALS). MATERIALS AND METHODS This study had institutional review board approval, and written ...

This paper describes image processing techniques for Diffusion Tensor Magnetic Resonance. In Diffusion Tensor MRI, a tensor describing local water diffusion is acquired for each voxel. The geometric nature of the diffusion tensors can quantitatively characterize the local structure in tissues such as bone, muscles, and white matter of the brain. The close relationship between local image struct...