Introduction: Diffusion tensor imaging (DTI) is widely used to characterize white matter in health and disease. Previous approaches to the estimation of diffusion tensors have either been statistically suboptimal or have used Gaussian approximations of the underlying noise structure, which is Rician in reality. The most prevalent tensor estimation method, the log-linear minimum mean squared err...

PURPOSE Diffusion MRI requires acquisition of multiple diffusion-weighted images, resulting in long scan times. Here, we investigate combining compressed sensing and a fast imaging sequence to dramatically reduce acquisition times in cardiac diffusion MRI. METHODS Fully sampled and prospectively undersampled diffusion tensor imaging data were acquired in five rat hearts at acceleration factor...

Rician noise introduces a bias into MRI measurements that can have a significant impact on the shapes and orientations of tensors in diffusion tensor magnetic resonance images. This is less of a problem in structural MRI, because this bias is signal dependent and it does not seriously impair tissue identification or clinical diagnoses. However, diffusion imaging is used extensively for quantita...

Introduction The observation of non-monoexponential decay of diffusion-weighted MR signals with b-value has been widely reported [1]. Using the theory of anomalous diffusion, several groups have derived a stretched-exponential form for this signal decay (e.g. [2][3]) which parameterises the signal in terms of a distributed diffusivity α (measuring the overall rate of diffusion) and an anomalous...

The optimization of acquisition parameters for precise measurement of diffusion in anisotropic systems is described. First, an algorithm is presented that minimizes the bias inherent in making measurements with a fixed set of gradient vector directions by spreading out measurements in 3-dimensional gradient vector space. Next, it is shown how the set of b-matrices and echo time can be optimized...

Tensor-driven anisotropic diffusion and regularisation have been successfully applied to a wide range of image processing and computer vision tasks such as denoising, inpainting, and optical flow. Empirically it has been shown that anisotropic models with a diffusion tensor perform better than their isotropic counterparts with a scalar-valued diffusivity function. However, the reason for this s...

Diffusion Tensor Imaging (DTI) image registration is an essential step for diffusion tensor image analysis. Most of the fiber bundle based registration algorithms use deterministic fiber tracking technique to get the white matter fiber bundles, which will be affected by the noise and volume. In order to overcome the above problem, we proposed a Diffusion Tensor Imaging image registration method...

Diffusion tensor magnetic resonance imaging (DT-MRI) is emerging as an important tool in medical image analysis of the brain. However, relatively little work has been done on producing statistics of diffusion tensors. A main difficulty is that the space of diffusion tensors, i.e., the space of symmetric, positivedefinite matrices, does not form a vector space. Therefore, standard linear statist...

The degree of diffusion tensor anisotropy is often associated with the organization of structural tissues such as white matter. Numerous measures of diffusion anisotropy have been proposed, which could lead to confusion in interpreting and comparing results from different studies. In this study, a new method for representing the diffusion tensor shape, called the three-phase (3P) plot, is descr...

OBJECTIVES Investigating in a case-control study whether the performance scores of a group of patients with Parkinson disease (PD) without dementia on tests of declarative memory could be predicted by hippocampal volume reduction (as assessed by automatic segmentation of cerebral magnetic resonance [MR] images) or by the rate of microstructural alterations (as evaluated by diffusion tensor anal...