Inter-subject coherence in DT-MRI

• US 7,570,049 B2
• Filed: 04/06/2005
• Issued: 08/04/2009
• Est. Priority Date: 04/14/2004
• Status: Active Grant

• Current Assignee: Mclean Hospital (Mass General Brigham, Inc.)
• Original Assignee: Mclean Hospital (Mass General Brigham, Inc.)
• Inventors: Lange, Nicholas T.
• Primary Examiner(s): Shrivastav; Brij B.
• Assistant Examiner(s): Vargas; Dixomara
• Application Number:
  US11/578,182
• Publication Number:
  US 20070273373A1
• Time in Patent Office: 1,581 Days
• Field of Search: 324300-322, 600410-435
• US Class Current: 324/307
• CPC Class Codes: G01R 33/56341 Diffusion imaging

Abstract

A method for estimating a value of a diffusion tensor includes obtaining, from a plurality of test subjects, DT-MRI data from which an initial estimate of the tensor can be derived. Values indicative of intra-subject variation and inter-subject variation in the data are then determined. These values are used to determine a subject-specific additive offset for adjusting the DT-MRI data.

Claims

1. A computer-implemented method for estimating the evolution of a diffusion tensor using DT-MRI data provided by an MRI system, the method comprising:

obtaining, from each of a plurality of test subjects, DT-MRI data representing a time series of diffusion tensors associated with each test subject;

using a computer to reduce the DT-MRI data to determine individual data descriptive of the evolution of tensor values for each of the test subjects;

causing a computer to reduce the individual data to determine data descriptive of the evolution of a diffusion tensor associated with the plurality of test subjects; and

outputting the data descriptive of the evolution of a diffusion tensor associated with the plurality of test subjects.

2. The computer-implemented method of claim 1, wherein reducing the individual data comprises:

determining a plurality of individual mean functions, each of which corresponds to a test subject; and

determining a plurality of individual covariance matrices, each of which corresponds to a test subject.

3. The computer-implemented method of claim 1, wherein reducing at least one of the collective data and the individual data comprises determining a growth model.

4. The computer-implemented method of claim 3, determining a growth model comprises selecting a growth model from the group consisting of a parametric growth-model, a semi-parametric growth-model, and a non-parametric growth-model.

5. The computer-implemented method of claim 1, wherein reducing at least one of the collective data and the individual data comprises determining a longitudinal model.

6. A computer-readable medium having encoded thereon software for estimating the evolution of a diffusion tensor using DT-MRI data provided by an MRI system, the software comprising computer-executable instructions for:

obtaining, from each of a plurality of test subjects, DT-MRI data representing a time series of diffusion tensors associated with each test subject;

reducing the DT-MRI data to determine individual data descriptive of the evolution of tensor values for each of the test subjects;

reducing the individual data to determine data descriptive of the evolution of a diffusion tensor associated with the plurality of test subjects.

7. The computer-readable medium of claim 6, wherein the instructions for reducing the individual data comprise instructions for:

determining a plurality of individual mean functions, each of which corresponds to a test subject; and

determining a plurality of individual covariance matrices, each of which corresponds to a test subject.

8. The computer-readable medium of claim 6, wherein the instructions for reducing at least one of the collective data and the individual data comprise instructions for determining a growth model.

9. The computer-readable medium of claim 6, wherein the instructions for determining a growth model comprise instructions for selecting a growth model from the group consisting of a parametric growth-model, a semi-parametric growth-model, and a non-parametric growth-model.

10. The computer-readable medium of claim 6, wherein the instructions for reducing at least one of the collective data and the individual data comprise instructions for determining a longitudinal model.