Granted Patent B2

US 8,169,216 · App. 12/387,697 · Granted May 1, 2012

Assignee: Medical College of Georgia Research Institute, Inc.

Abstract

Claims (59)

1. A method for synthesizing crossing ADC distributions via reassembly of multiple k-spaces in an MRI system using a DTI sequence to generate a mean image modeling human or animal tissue, comprising:

scanning with the MRI system a test object having a plurality of anisotropic structures to acquire a first set of DTI data using a plurality of gradient directions;

rotating the gradient directions by an angle α;

repeating the step of scanning the test object with the MRI system to acquire a second set of DTI data;

creating a composite data set from the first and second sets of DTI data; and

applying an inverse Fourier transform to the composite data set to generate the mean image.

2. The method of claim 1 , wherein the step of creating a composite data set includes combining half of the data from each data set.

3. The method of claim 2 , wherein the half of the data of each set comprises data with conjugate complex components removed.

4. The method of claim 2 , wherein the plurality of gradient direction is greater than or equal to 12 gradient directions.

5. The method of claim 2 , wherein the angle α is between 0° to between 90°.

6. The method of claim 1 , further comprising the following steps occurring before creating the composite data set:

performing an inverse Fourier transform to generate the spin density of each set of data;

applying an appropriate phase shift to each set of data; and

transforming each set of data back to k-space.

7. The method of claim 6 , further comprising applying smoothing to a phase map before applying the appropriate phase shift to each set of data.

8. The method of claim 6 , wherein the step of creating a composite data set includes combining half of the data from each data set.

9. The method of claim 1 , further comprising the step of comparing the transformed composite data to a prediction and/or a two-tensor model fit.

10. A non-transitory computer-readable medium storing instructions that, when executed on a programmed processor, carry out a method for synthesizing crossing ADC distributions via reassembly of multiple k-spaces in an MRI system, comprising:

instructions for scanning a test object having a plurality of anisotropic structures to acquire a first set of DTI data using a plurality of gradient directions;

instructions for rotating the gradient directions by an angle α;

instructions for repeating the step of scanning the test object to acquire a second set of DTI data;

instructions for creating a composite data set from the first and second sets of data by combining half of the data from each data set; and

instructions for applying an inverse Fourier transform to the composite data set to generate a mean image.

11. The computer-readable medium of claim 10 , wherein the plurality of gradient direction is greater than or equal to 12 gradient directions.

12. The computer-readable medium of claim 10 , wherein the angle α is between about 0° to between 90°.

13. The computer-readable medium of claim 10 , further comprising instructions for:

performing an inverse Fourier transform to generate the spin density of each set of data;

applying an appropriate phase shift to each set of data; and

transforming each set of data back to k-space.

14. The computer-readable medium of claim 13 , further comprising instructions for applying smoothing to a phase map before applying the appropriate phase shift to each set of data.

15. The computer-readable medium of claim 10 , further comprising instructions for comparing the transformed composite data to a prediction and/or a two-tensor model fit.

16. A system for synthesizing crossing ADC distributions via reassembly of multiple k-spaces in an MRI system using a DTI sequence to generate a mean image modeling human or animal tissue, comprising:

means for scanning a test object having a plurality of anisotropic structures to acquire a first set of DTI data using a plurality of gradient directions;

means for rotating the gradient directions by an angle α;

means for repeating the step of scanning the test object to acquire a second set of DTI data;

means for creating a composite data set from the first and second sets of DTI data by combining half the data from each data set; and

means for applying an inverse Fourier transform to the composite data set to generate the mean image.

17. The system of claim 16 , further comprising means for:

performing an inverse Fourier transform to generate the spin density of each set of data;

applying an appropriate phase shift to each set of data; and

transforming each set of data back to k-space.

18. The system of claim 17 , further comprising instructions for applying smoothing to a phase map before applying the appropriate phase shift to each set of data.

19. The system of claim 16 , further comprising instructions for comparing the transformed composite data to a prediction and/or a two-tensor model fit.

20. A method for synthesizing crossing ADC distributions via reassembly of multiple k-spaces in an MRI system using a DTI sequence to generate a mean image modeling human or animal tissue, comprising:

acquiring first and second pluralities of diffusion-weighted images, wherein the first plurality of diffusion-weighted images has a different diffusion weighting than the second plurality of diffusion-weighted images;

combining half of the diffusion-weighted images of the first plurality with half of the diffusion-weighted images of the second plurality to create a composite data set; and

applying an inverse Fourier transform to the composite data set to generate the mean image.

21. A method for DTI analysis of tissue of a human or animal subject, comprising:

scanning a test object having a plurality of anisotropic structures to acquire a first set of apparent diffusion coefficient (ADC) data using a plurality of gradient directions;

rotating the gradient directions by an angle α;

repeating the step of scanning the test object to acquire a second set of ADC data;

creating a composite data set from the first and second sets of data;

applying an inverse Fourier transform to the composite data set to generate a mean image modeling the human or animal tissue;

scanning tissue of a human or animal subject to acquire a third set of ADC data using the plurality of gradient directions;

rotating the gradient directions by the angle α;

repeating the step of scanning the tissue to acquire a fourth set of ADC data;

creating a second composite data set from the third and fourth sets of data;

applying an inverse Fourier transform to the second composite data set to generate a mean image of the human or animal tissue; and

comparing the mean image modeling the tissue with the mean image of the actual tissue.

Assignments (4)

Continuity (10)