Identifying white matter fiber tracts using magnetic resonance imaging (MRI)

Patent metadata

Field	Value
Publication number	US-9568580-B2
Application number	US-49644109-A
Country	US
Kind code	B2
Filing date	Jul 1, 2009
Priority date	Jul 1, 2008
Publication date	Feb 14, 2017
Grant date	Feb 14, 2017

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.

First claim

The invention claimed is:

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, 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 that ν ′ T ν 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

v
′

⁢

T
_

⁢
v

v
′

⁢
Tv

_

,

wherein ν′ is the transpose of v and ν ′ is the transpose of ν .

2. The computer implemented method of claim 1 , the method further comprising:
producing location information of a fiber tract of interest associated with the first data voxel using fiber density data, wherein the fiber density data is associated with the plurality of atlas voxels and includes streamline fiber counts to indicate a likelihood of an atlas voxel to the fiber tract of interest;
resampling the averaged information on the probabilistic locations and the orientations of the first fiber tract from an atlas space to a subject space for the subject, wherein the determined probability that the first data voxel corresponds to the first fiber tract is based in part on resampled location probability and resampled orientation information.

3. The computer implemented method of claim 1 , wherein the nervous system includes a human nervous system, the determining the probability that the first data voxel corresponds to the first fiber tract is based at least in part on the generated diffusion vector and the fiber-specific average diffusion tensor based at least in part on measurements of a plurality of human nervous systems.

4. The computer implemented method of claim 1 , wherein the averaged information on the probabilistic locations and the orientations of the first fiber tract comprises the first atlas diffusion tensor for the first atlas voxel, the fiber tract atlas further comprises an additional tensor for each other atlas voxel, the additional tensor for each other atlas voxel representing orientations of the first fiber tract in the atlas voxel, the method further comprising using the fiber tract atlas to determine an additional probability for each other data voxel, the additional probability for each other data voxel based at least in part on the generated diffusion vector for the data voxel and the additional tensor for an atlas voxel that relates to the data voxel, the additional probability for each other data voxel indicating a probability that the data voxel corresponds to the first fiber tract.

5. The computer implemented method of claim 1 , further comprising determining that the first data voxel relates to the first atlas voxel by performing additional operations comprising:
registering a T 1 -weighted image to an atlas space of the fiber tract atlas; and
resampling the data voxels into the atlas space.

6. The computer implemented method of claim 1 , wherein the averaged information on the probabilistic locations and the orientations of the first fiber tract comprises the first atlas diffusion tensor, and the obtaining the fiber tract atlas comprises obtaining a fiber tract atlas that further includes a plurality of additional tensors, each additional tensor representing the orientations of additional fiber tracts in the first volume element of the nervous system.

7. The computer implemented method of claim 6 , wherein the probability that the first data voxel corresponds to the first fiber tract comprises a first probability, the method further comprising using the fiber tract atlas to determine a plurality of additional probabilities based at least in part on the generated diffusion vector and the plurality of additional tensors, each additional probability representing a relative probability that the first data voxel corresponds to one of the additional fiber tracts.

8. The computer implemented method of claim 1 , further comprising constructing the obtained fiber tract atlas by performing additional operations comprising:
acquiring additional magnetic resonance data from nervous systems of a plurality of additional subjects, the additional magnetic resonance data comprising a plurality of data voxels for each additional subject;
generating an additional diffusion tensor for each data voxel of each additional subject based at least in part on the additional magnetic resonance data; and
generating an average diffusion tensor for each atlas voxel of the fiber tract atlas by averaging the additional diffusion tensors across the additional subjects.

9. The computer implemented method of claim 1 , wherein the generating the diffusion vector comprises generating an additional eigenvector (ν) of a second diffusion tensor, the second diffusion tensor based at least in part on the acquired magnetic resonance data, and wherein the determining the probability comprises combining the rank-two symmetric tensor T , the eigenvector ( ν ), and the additional eigenvector ν according to the expression

v
′

⁢

T
_

⁢
v

Assignees

• Dale Anders M
• Hagler Jr Donald J
• Univ California

Inventors

• Dale Anders M
• Hagler Jr Donald J

Classifications

• G01R33/5602

  by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse · CPC title

• G01R33/56341 Primary

  Diffusion imaging · CPC title

• G01R33/5608

  Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T) · CPC title