Identifying white matter fiber tracts using magnetic resonance imaging (MRI)
First Claim
1. A computer implemented method for determining how well magnetic resonance imaging data obtained from a subject corresponds to a given white matter fiber tract, the method comprising:
- obtaining a fiber tract atlas for a nervous system, the fiber tract atlas comprising;
a plurality of atlas voxels that each represent a different volume element of the nervous system, the plurality of atlas voxels including a first atlas voxel that represents a first volume element of the nervous system, andaveraged information on probabilistic locations and orientations of a first fiber tract in the first volume element of the nervous system, wherein the averaged information on the probabilistic locations and the orientations of the first fiber tract includes a rank-two symmetric atlas diffusion tensor (T) having a first atlas diffusion tensor eigenvector (ν
), such that ν
′
Tν
is equal to a largest eigenvalue of T;
acquiring magnetic resonance data from the nervous system of a subject, the magnetic resonance data comprising a plurality of data voxels including a first data voxel that relates to the first atlas voxel;
calculating a diffusion tensor for the first data voxel;
generating a diffusion vector (ν
) for the first data voxel, the diffusion vector being a first eigenvector corresponding to a largest eigenvalue of the calculated diffusion tensor for the first data voxel; and
determining a probability P that the first data voxel of the magnetic resonance data of the subject corresponds to the first fiber tract, based at least in part on the generated diffusion vector (ν
) and the averaged information on the probabilistic locations and orientations of the first fiber tract in the first volume element, and without manual inspection of the generated diffusion vector, the orientation information, or magnetic resonance images associated with the magnetic resonance data, wherein the determined probability P is determined by combining the atlas diffusion tensor (T), the first atlas diffusion tensor eigenvector (ν
), and the diffusion vector (ν
) of the first data voxel according to the expression
1 Assignment
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Accused Products
Abstract
Systems, methods, and software are used for identifying fibers based at least in part on magnetic resonance imaging. A fiber tract atlas for a nervous system includes atlas voxels that each represent a different volume element of the nervous system; a first atlas voxel represents a first volume element of the nervous system. The fiber tract atlas also includes information on orientations of a first fiber tract in the first volume element of the nervous system. Magnetic resonance data is acquired from the nervous system of a subject. The magnetic resonance data includes data voxels; a first data voxel relates to the first atlas voxel. A diffusion vector is generated for the first data voxel based at least in part on the acquired magnetic resonance data. The fiber tract atlas is used to find a probability that the first data voxel represents the first fiber tract based at least in part on the generated diffusion vector and the information on the orientations of the first fiber tract in the first volume element.
37 Citations
21 Claims
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1. A computer implemented method for determining how well magnetic resonance imaging data obtained from a subject corresponds to a given white matter fiber tract, the method comprising:
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obtaining a fiber tract atlas for a nervous system, the fiber tract atlas comprising; a plurality of atlas voxels that each represent a different volume element of the nervous system, the plurality of atlas voxels including a first atlas voxel that represents a first volume element of the nervous system, and averaged information on probabilistic locations and orientations of a first fiber tract in the first volume element of the nervous system, wherein the averaged information on the probabilistic locations and the orientations of the first fiber tract includes a rank-two symmetric atlas diffusion tensor ( T ) having a first atlas diffusion tensor eigenvector (ν ), such thatν ′T ν is equal to a largest eigenvalue ofT ;acquiring magnetic resonance data from the nervous system of a subject, the magnetic resonance data comprising a plurality of data voxels including a first data voxel that relates to the first atlas voxel; calculating a diffusion tensor for the first data voxel; generating a diffusion vector (ν
) for the first data voxel, the diffusion vector being a first eigenvector corresponding to a largest eigenvalue of the calculated diffusion tensor for the first data voxel; anddetermining a probability P that the first data voxel of the magnetic resonance data of the subject corresponds to the first fiber tract, based at least in part on the generated diffusion vector (ν
) and the averaged information on the probabilistic locations and orientations of the first fiber tract in the first volume element, and without manual inspection of the generated diffusion vector, the orientation information, or magnetic resonance images associated with the magnetic resonance data, wherein the determined probability P is determined by combining the atlas diffusion tensor (T ), the first atlas diffusion tensor eigenvector (ν ), and the diffusion vector (ν
) of the first data voxel according to the expression - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A magnetic resonance imaging (MRI) system comprising:
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a magnetic resonance data acquisition system adapted to acquire magnetic resonance data from a nervous system of a subject, the magnetic resonance data comprising a plurality of data voxels including a first data voxel; and a data processor that is configured to; receive the magnetic resonance data from the magnetic resonance data acquisition system; obtain a fiber tract atlas for the nervous system from the magnetic resonance data, the fiber tract atlas comprising; a plurality of atlas voxels that each represent a different volume element of the nervous system, the plurality of atlas voxels including a first atlas voxel that represents a first volume element of the nervous system, the first data voxel relating to the first atlas voxel; and averaged information on probabilistic locations and orientations of a first fiber tract in the first volume element of the nervous system, wherein the averaged information on the probabilistic locations and the orientations of the first fiber tract includes a rank-two symmetric atlas diffusion tensor ( T ) having a first atlas diffusion tensor eigenvector (ν ), such thatν ′T ν is equal to the largest eigenvalue ofT ;calculate a diffusion tensor for the first data voxel; generate a diffusion vector (ν
) for the first data voxel, the diffusion vector being a first eigenvector corresponding to a largest eigenvalue of the calculated diffusion tensor for the first data voxel; andprocess the fiber tract atlas and magnetic resonance data to determine a probability P that the first data voxel corresponds to the first fiber tract, based at least in part on the generated diffusion vector (ν
) and the averaged information on the probabilistic locations and the orientations of the first fiber tract in the first volume element and without manual inspection of the generated diffusion vector, the orientation information, or magnetic resonance images associated with the magnetic resonance data, wherein the determined probability includes P is determined by combining the atlas diffusion tensor (T ), the first atlas diffusion tensor eigenvector (ν ), and the diffusion vector (ν
) of the first data voxel according to the expression - View Dependent Claims (13, 14, 15, 16)
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17. A computer program product comprising a non-transitory computer-readable medium having code stored thereon, the code, when executed by a data processing apparatus, causing the data processing apparatus to implement a method comprising:
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obtaining a fiber tract atlas for a nervous system, the fiber tract atlas comprising; a plurality of atlas voxels that each represent a different volume element of the nervous system, the plurality of atlas voxels including a first atlas voxel that represents a first volume element of the nervous system, and averaged information on probabilistic locations and orientations of a first fiber tract in the first volume element of the nervous system, wherein the averaged information on the probabilistic locations and the orientations of the first fiber tract includes a rank-two symmetric atlas diffusion tensor ( T ) having a first atlas diffusion tensor eigenvector (ν ), such thatν ′T ν is equal to the largest eigenvalue ofT ;acquiring magnetic resonance data from the nervous system of a subject, the magnetic resonance data comprising a plurality of data voxels including a first data voxel, the first data voxel relating to the first atlas voxel; calculating a diffusion tensor for the first data voxel; generating a diffusion vector (ν
) for the first data voxel, the diffusion vector being a first eigenvector corresponding to a largest eigenvalue of the calculated diffusion tensor for the first data voxel; anddetermining a probability P that the first data voxel of the magnetic resonance data of the subject corresponds to the first fiber tract, based at least in part on the generated diffusion vector (ν
) and the averaged information on the probabilistic locations and the orientations of the first fiber tract in the first volume element, and without manual inspection of the generated diffusion vector, the orientation information, or magnetic resonance images associated with the magnetic resonance data, wherein the determined probability includes P is determined by combining the atlas diffusion tensor (T ), the first atlas diffusion tensor eigenvector (ν ), and the diffusion vector (ν
) of the first data voxel according to the expression - View Dependent Claims (18, 19, 20, 21)
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Specification