Constrained spherical deconvolution (CSD) is a new technique that, based on high-angular resolution diffusion imaging (HARDI) MR data, estimates the orientation of multiple intravoxel fiber populations within regions of complex white matter architecture, thereby overcoming the limitations of the widely used diffusion tensor imaging (DTI) technique. One of its main applications is fiber tractogr...

Introduction A number of acquisition and reconstruction techniques have recently been proposed to extract the orientations of the white matter fibres within each imaging voxel from diffusion-weighted imaging (DWI) data. Of these, the diffusion tensor model is currently the most commonly used, but is limited in that it cannot resolve crossing fibres [1]. Constrained spherical deconvolution (CSD)...

In diffusion Magnetic Resonance Imaging (dMRI), Spherical Deconvolution (SD) is a commonly used approach for estimating the fiber Orientation Distribution Function (fODF). As a Probability Density Function (PDF) that characterizes the distribution of fiber orientations, the fODF is expected to be non-negative and to integrate to unity on the continuous unit sphere S2. However, many existing app...

Spatial encoding in MR techniques is achieved by sampling the signal as a function of time in the presence of a magnetic field gradient. The gradients are assumed to generate a linear magnetic field gradient, and typical image reconstruction relies upon this approximation. However, high-speed gradients in the current generation of MRI scanners often sacrifice linearity for improvements in speed...

BACKGROUND AND PURPOSE Imaging the optic radiation (OR) is of considerable interest in studying diseases affecting the visual pathway and for pre-surgical planning of temporal lobe resections. The purpose of this study was to investigate the clinical feasibility of using probabilistic diffusion tractography based on constrained spherical deconvolution (CSD) to image the optic radiation. It was ...

We propose two strategies to improve the quality of tractography results computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both methods are based on the same PDE framework, defined in the coupled space of positions and orientations, associated with a stochastic process describing the enhancement of elongated structures while preserving crossing structures. In the first ...

If the Navier-Stokes equations are averaged with a local, spacial convolution type filter, φ = gδ∗φ, the resulting system is not closed due to the filtered nonlinear term uu. An approximate deconvolution operator D is a bounded linear operator which satisfies u = D(u) + O(δ), where δ is the filter width and α ≥ 2. Using a deconvolution operator as an approximate filter inverse, yields the closu...

Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biological samples. Unfortunately, dealing with deconvolution tools is not straightforward. Among others, end users have to select the appropriate a...

We present experimental data comparing the accuracy obtained for 2D and 3D scanning surfaces using CLEAN-SC deconvolution of beamformed acoustic maps. A spherical array is used to obtain recordings from a dense point cloud of sound source locations. Beamforming and CLEAN-SC acoustic maps are generated using traditional 2D scanning surfaces and 3D scanning surfaces corresponding to the surface g...

Having to determine an adequate number of fiber directions is a fundamental limitation of multi-compartment models in diffusion MRI. This paper proposes a novel strategy to approach this problem, based on simulating data that closely follows the characteristics of the measured data. This provides the ground truth required to determine the number of directions that optimizes a formal measure of ...