Methods and apparatus for modeling diffusion-weighted MR data acquired at multiple non-zero B-values

1. A computer system for characterizing biological microstructure in a voxel based, at least in part, on a set of diffusion-weighted magnetic resonance (MR) data, the computer system comprising:

a magnetic resonance imaging system;

at least one computer processor; and

at least one storage device configured to store a plurality of instructions that, when executed by the at least one computer processor, perform a method, comprising:

controlling the magnetic resonance imaging system to acquire a set of diffusion-weighted MR data, wherein at least one first dataset of the set of diffusion-weighted MR data is associated with a first non-zero b-value and at least one second dataset of the set of diffusion-weighted MR data is associated with a second non-zero b-value different than the first non-zero b-value;

fitting a parametric model using information from the set of diffusion-weighted MR data, wherein the parametric model is a multi-compartment model that includes a statistical distribution of diffusion tensors for each compartment of the multi-compartment model, and wherein fitting the parametric model comprises determining for the voxel, based on the set of diffusion-weighted MR data, a first set of parameters describing isotropic diffusion in a first compartment of the multi-compartment model and a second set of parameters describing anisotropic diffusion due to the presence of at least one white matter fascicle in a second compartment of the multi-compartment model; and

outputting an indication of the first set of parameters and/or the second set of parameters for the voxel.

2. The computer system of claim 1 , wherein the second set of parameters describe restrictive anisotropic diffusion in the second compartment.

3. The computer system of claim 1 , wherein fitting the parametric model further comprises determining a third set of parameters describing hindered anisotropic diffusion in a third compartment, and wherein the method further comprises outputting the third set of parameters for the voxel.

4. The computer system of claim 1 , wherein fitting the parametric model further comprises determining how many compartments to include in the multi-compartment model for the voxel based, at least in part, on the information from the set of diffusion-weighted MR data.

5. The computer system of claim 1 , wherein the multi-compartment model includes at least four compartments, wherein the at least four compartments include a restricted isotropic diffusion compartment, a free isotropic diffusion compartment, a restrictive anisotropic diffusion compartment, and a hindered anisotropic diffusion compartment.

6. The computer system of claim 1 , wherein the parametric model is DIAMOND.

7. The computer system of claim 1 , wherein fitting the parametric model further comprises iteratively increasing the complexity of the parametric model until it is determined that further increases in complexity do not provide a statistically significant increase in the parametric model's ability to predict diffusion data in the voxel.

8. A method of characterizing biological microstructure in a voxel based, at least in part, on a set of diffusion-weighted magnetic resonance (MR) data, the method comprising:

acquiring, using a magnetic resonance imaging system, a set of diffusion-weighted MR data, wherein at least one first dataset of the set of diffusion-weighted MR data is associated with a first non-zero b-value and at least one second dataset of the set of diffusion-weighted MR data is associated with a second non-zero b-value different than the first non-zero b-value;

fitting a parametric model using information from the set of diffusion-weighted MR data, wherein the parametric model is a multi-compartment model that includes a statistical distribution of diffusion tensors for each compartment of the multi-compartment model, and wherein fitting the parametric model comprises determining for the voxel, based on the set of diffusion-weighted MR data, a first set of parameters describing isotropic diffusion in a first compartment of the multi-compartment model and a second set of parameters describing anisotropic diffusion due to the presence of at least one white matter fascicle in a second compartment of the multi-compartment model; and

outputting an indication of the first set of parameters and/or the second set of parameters for the voxel.

9. The method of claim 8 , wherein fitting the parametric model further comprises determining how many compartments to include in the multi-compartment model for the voxel based, at least in part, on the information from the set of diffusion-weighted MR data.

10. The method of claim 8 , wherein the multi-compartment model includes at least four compartments, wherein the at least four compartments include a restricted isotropic diffusion compartment, a free isotropic diffusion compartment, a restrictive anisotropic diffusion compartment, and a hindered anisotropic diffusion compartment.

11. A non-transitory computer readable storage medium encoded with a plurality of instructions that, when executed by at least one computer processor, perform a method comprising:

acquiring, using a magnetic resonance imaging system, a set of diffusion-weighted MR data, wherein at least one first dataset of the set of diffusion-weighted MR data is associated with a first non-zero b-value and at least one second dataset of the set of diffusion-weighted MR data is associated with a second non-zero b-value different than the first non-zero b-value;

fitting a parametric model using information from the set of diffusion-weighted MR data, wherein the parametric model is a multi-compartment model that includes a statistical distribution of diffusion tensors for each compartment of the multi-compartment model, and wherein fitting the parametric model comprises determining for a voxel, based on the set of diffusion-weighted MR data, a first set of parameters describing isotropic diffusion in a first compartment of the multi-compartment model and a second set of parameters describing anisotropic diffusion due to the presence of at least one white matter fascicle in a second compartment of the multi-compartment model; and

outputting an indication of the first set of parameters and/or the second set of parameters for the voxel.

12. The non-transitory computer-readable storage medium of claim 11 , wherein the second set of parameters describe restrictive anisotropic diffusion in the second compartment.

13. The non-transitory computer-readable storage medium of claim 11 , wherein fitting the parametric model further comprises determining a third set of parameters describing hindered anisotropic diffusion in a third compartment, and wherein the method further comprises outputting the third set of parameters for the voxel.

14. The non-transitory computer-readable storage medium of claim 11 , wherein fitting the parametric model further comprises determining how many compartments to include in the multi-compartment model for the voxel based, at least in part, on the information from the set of diffusion-weighted MR data.

15. The non-transitory computer-readable storage medium of claim 11 , wherein the multi-compartment model includes at least four compartments, wherein the at least four compartments include a restricted isotropic diffusion compartment, a free isotropic diffusion compartment, a restrictive anisotropic diffusion compartment, and a hindered anisotropic diffusion compartment.

16. The non-transitory computer-readable storage medium of claim 11 , wherein fitting the parametric model further comprises iteratively increasing the complexity of the parametric model until it is determined that further increases in complexity do not provide a statistically significant increase in the parametric model's ability to predict diffusion data in the voxel.