Identifying White Matter Fiber Tracts Using Magnetic Resonance Imaging (MRI)

• US 20100004527A1
• Filed: 07/01/2009
• Published: 01/07/2010
• Est. Priority Date: 07/01/2008
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Dale, Anders M., Hagler, Donald J. JR.
• Granted Patent: US 9,568,580 B2
• Time in Patent Office: Days
• US Class Current: 600/410
• CPC Class Codes: G01R 33/5602 by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse
• CPC Class Codes: G01R 33/5608 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 Class Codes: G01R 33/56341 Diffusion imaging

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.

Claims

1. A method for magnetic resonance imaging 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

information on orientations of a first fiber tract in the first volume element of the nervous system;

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;

generating a diffusion vector for the first data voxel based at least in part on the acquired magnetic resonance data; and

using the fiber tract atlas 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.

2. The method of claim 1, wherein the fiber tract atlas further comprises a location probability that the first fiber tract resides in first volume element, the method further comprising resampling the location probability and the information on orientations of the first fiber tract from an atlas space to a subject space for the subject, wherein the found probability is based in part on the resampled location probability and the resampled orientation information.

3. The method of claim 1, wherein the nervous system includes a human nervous system, the information on the orientations of the first fiber tract includes an average diffusion tensor based at least in part on measurements of a plurality of human nervous systems, and using the fiber tract atlas to find the probability that the first data voxel represents the first fiber tract is based at least in part on the generated diffusion vector and the average diffusion tensor.

4. The method of claim 1, wherein the information on the orientations of the first fiber tract comprises a first 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 find 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 represents the first fiber tract.

5. The 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 T₁-weighted image to an atlas space of the fiber tract atlas; and

resampling the data voxels into the atlas space.

6. The method of claim 1, wherein the information on the orientations of the first fiber tract comprises a first tensor, and obtaining a 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 method of claim 6, wherein the found probability comprises a first probability, the method further comprising using the fiber tract atlas to find 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 represents one of the additional fiber tracts.

8. The method of claim 1, further comprising constructing the 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 method of claim 1, wherein the information on the orientations of the first fiber tract includes a rank-two symmetric tensor ( T) having an eigenvector ( ν

), generating a 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 using the fiber tract atlas to find the probability comprises combining the rank-two symmetric tensor T, the eigenvector ( ν

), and the additional eigenvector ν

according to the equation

$\frac{v^{\mathrm{\prime <br><br>}}<br><br>\stackrel{\_}{T}<br><br>v}{\stackrel{\_}{v^{\mathrm{\prime <br><br>}}}<br><br>\stackrel{\_}{T}<br><br>\stackrel{\_}{v}}.$

10. The method of claim 1, further comprising:

using the fiber tract atlas and the acquired magnetic resonance data to generate a fiber probability map for at least a first portion of the nervous system of the subject; and

using the probability map to determine at least one of a fractional anisotropy for the first fiber tract or a diffusivity for the first fiber tract.

11. The method of claim 1, wherein acquiring magnetic resonance data comprises:

applying a plurality of radio frequency pulses to the subject;

applying a plurality of gradient fields to the subject; and

detecting at least one echo signal from the subject.

12. A magnetic resonance imaging (MRI) system comprising:

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 receives the magnetic resonance data from the magnetic resonance data acquisition system and comprises;

a mechanism that obtains 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

information on orientations of a first fiber tract in the first volume element of the nervous system;

a mechanism that generates a diffusion vector for the first data voxel based at least in part on the acquired magnetic resonance data; and

a mechanism that processes the fiber tract atlas 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.

13. The MRI system of claim 12, the magnetic resonance data acquisition system comprising:

a magnet that generates a substantially static principal magnetic field;

a gradient sub-system that applies a plurality of gradient fields to the subject; and

a radio frequency sub-system that applies a plurality of radio frequency pulses to the subject and detects at least one echo signal from the subject.

14. The MRI system of claim 13, wherein the radio frequency sub-system includes a head coil that acquires magnetic resonance data from a human nervous system.

15. The MRI system of claim 12, further comprising:

a mechanism that processes the fiber tract atlas and the acquired magnetic resonance data to generate a fiber probability map for at least a first portion of the nervous system of the subject; and

a mechanism that uses the probability map to identify a region of interest of the nervous system of the subject.

16. The MRI system of claim 12, further comprising a data communication network communicatively coupling the magnetic resonance data acquisition system and a computing device that includes the data processor.

17. An article comprising a machine-readable medium storing instructions adapted to cause data processing apparatus to perform operations 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

information on orientations of a first fiber tract in the first volume element of the nervous system;

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;

generating a diffusion vector for the first data voxel based at least in part on the acquired magnetic resonance data; and

using the fiber tract atlas 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.

18. The article of claim 17, wherein the fiber tract atlas further comprises a location probability that the first fiber tract resides in first volume element, the method further comprising resampling the location probability and the information on orientations of the first fiber tract from an atlas space to a subject space for the subject, wherein the found probability is based in part on the resampled location probability and the resampled orientation information

19. The article of claim 17, the operations further comprising:

using the fiber tract atlas and the acquired magnetic resonance data to generate a fiber probability map for at least a first portion of the nervous system of the subject; and

using the probability map to determine at least one of a fractional anisotropy for the first fiber tract or a diffusivity for the first fiber tract.

20. The article of claim 17, the information on the orientations of the first fiber tract comprising a first tensor, and the fiber tract atlas further comprising a plurality of additional tensors representing orientations of additional fiber tracts in the first volume element of the nervous system.

21. The article of claim 20, the found probability comprising a first probability, the operations further comprising using the fiber tract atlas to find 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 represents one of the additional fiber tracts.

22. The article of claim 17, the information on the orientations of the first fiber tract including a rank-two tensor, generating a diffusion vector comprising generating an eigenvector of a diffusion tensor based at least in part on the acquired magnetic resonance data, and using the fiber tract atlas to find the probability comprising combining the rank-two tensor and the eigenvector.