Abstract: A computer implemented method for diffusion tensor visualization includes receiving diffusion weighted image slice data, segmenting a diffusion tensor field from the diffusion weighted image slice data to determine a three-dimensional triangular mesh, and determining a fractional anisotropy field and a principle diffusion direction field of the diffusion tensor field. The method includes determining a streamline through each surface point on a segmented surface of the diffusion tensor field according to the principle diffusion direction field, determining an oriented texture intensity for the surface points from corresponding streamlines, determining a surface color for the surface points by combining the oriented texture intensity, a color determined from the principle diffusion direction field, and a value of fractional anisotropy field at the surface points, and visualizing the surface points by rendering the surface points having the corresponding surface colors.

Abstract: A graphics processing unit implemented method for fiber tract mapping from diffusion tensor magnetic resonance imaging data includes providing a diffusion tensor magnetic resonance brain image volume, initializing a set of fiber positions in a 3D set of points, fiber displacements, and a posterior distribution for an updated fiber displacement in terms of the initial displacements and diffusion tensors, randomly sampling a set of updated fiber displacements from said posterior distribution, computing a new set of fiber positions from said initial fiber positions and said updated fiber displacements, wherein a fiber path comprises a set of fiber points connected by successive fiber displacements, accumulating connectivity values in each point of said 3D set of points by additive alpha-blending a scaled value if a fiber path has passed through a point and adding zero if not, and rendering said connectivity values.

Abstract: Segmenting a digitized image includes providing a diffusion tensor field image, partitioning the image into 2 regions, each point being assigned to one of the regions, associating a level-set function with each region, the level-set function having a negative value in one region and a positive value in the other region, calculating a mean value of the diffusion tensor field over each of 2 regions, initializing an energy defined as a functional of level-set functions and diffusion tensor field, changing the region membership of each point in the image if the energy functional value decreases as a result of region membership change and updating said mean value of said diffusion tensor field over each of 2 regions, and obtaining a segmentation of the image when the magnitude of the change of the energy function value resulting from changing the region membership of a point is less then a predetermined threshold.

Abstract: A method for visualizing diffusion-tensor magnetic resonance images, including the steps of providing a diffusion-tensor magnetic resonance image (DT-MRI) volume, positioning and orienting a virtual camera for rendering the volume, the camera having an image plane, the camera's orientation being defined by its look, right and up vectors, associating a billboard with each voxel, each the billboard being parallel with the image plane of the camera, where the look, right and up vectors are used to orient each the billboard, applying a texture to each billboard, each the texture being transformed by the inverse of the diffusion tensor associated with the voxel to which the billboard is associated, and rendering the volume by rendering each visible billboard in the image volume, where a 2-dimensional (2D) visualization of the DT-MRI is produced.

Abstract: A method for visualizing diffusion-tensor magnetic resonance images, including the steps of providing a diffusion-tensor magnetic resonance image (DT-MRI) volume, positioning and orienting a virtual camera for rendering the volume, the camera having an image plane, the camera's orientation being defined by its look, right and up vectors, associating a billboard with each voxel, each the billboard being parallel with the image plane of the camera, where the look, right and up vectors are used to orient each the billboard, applying a texture to each billboard, each the texture being transformed by the inverse of the diffusion tensor associated with the voxel to which the billboard is associated, and rendering the volume by rendering each visible billboard in the image volume, where a 2-dimensional (2D) visualization of the DT-MRI is produced.

Abstract: A graphics processing unit implemented method for fiber tract mapping from diffusion tensor magnetic resonance imaging data includes providing a diffusion tensor magnetic resonance brain image volume, initializing a set of fiber positions in a 3D set of points, fiber displacements, and a posterior distribution for an updated fiber displacement in terms of the initial displacements and diffusion tensors, randomly sampling a set of updated fiber displacements from said posterior distribution, computing a new set of fiber positions from said initial fiber positions and said updated fiber displacements, wherein a fiber path comprises a set of fiber points connected by successive fiber displacements, accumulating connectivity values in each point of said 3D set of points by additive alpha-blending a scaled value if a fiber path has passed through a point and adding zero if not, and rendering said connectivity values.

Abstract: A computer implemented method for diffusion tensor visualization includes receiving diffusion weighted image slice data, segmenting a diffusion tensor field from the diffusion weighted image slice data to determine a three-dimensional triangular mesh, and determining a fractional anisotropy field and a principle diffusion direction field of the diffusion tensor field. The method includes determining a streamline through each surface point on a segmented surface of the diffusion tensor field according to the principle diffusion direction field, determining an oriented texture intensity for the surface points from corresponding streamlines, determining a surface color for the surface points by combining the oriented texture intensity, a color determined from the principle diffusion direction field, and a value of fractional anisotropy field at the surface points, and visualizing the surface points by rendering the surface points having the corresponding surface colors.

Abstract: Segmenting a digitized image includes providing a diffusion tensor field image, partitioning the image into 2 regions, each point being assigned to one of the regions, associating a level-set function with each region, the level-set function having a negative value in one region and a positive value in the other region, calculating a mean value of the diffusion tensor field over each of 2 regions, initializing an energy defined as a functional of level-set functions and diffusion tensor field, changing the region membership of each point in the image if the energy functional value decreases as a result of region membership change and updating said mean value of said diffusion tensor field over each of 2 regions, and obtaining a segmentation of the image when the magnitude of the change of the energy function value resulting from changing the region membership of a point is less then a predetermined threshold.