Determination of parametrized characteristics of a tissue
Example embodiments describe a computer implemented method for obtaining parameterized characteristics of a tissue comprising obtaining at least two weighted MRI volume scans of the tissue, and transforming the two weighted MRI volume scans into parameters of a parameterized voxel-based model of the tissue by combined performing, by a super-resolution imaging technique, constructing a volume comprising voxels of the parameterized voxel-based model, and, by a quantitative MRI modelling technique, constructing the parameters for the respective voxels of the parameterized voxel-based model.
1. A computer implemented method for obtaining parameterized characteristics of a tissue comprising:
obtaining at least two weighted magnetic resonance imaging, MRI, volume scans of the tissue;
transforming the at least two weighted MRI volume scans into a parameterized voxel-based model of the tissue by combined performing, by a super-resolution imaging technique, constructing a volume comprising voxels of the parameterized voxel-based model, and, by a quantitative MRI modelling technique, constructing parameters for the respective voxels of the parameterized voxel-based model; and
generating an image of the tissue from the parameterized voxel-based model;
wherein the combined performing comprises:
calculating by an outer loop a residual based on an approximated set of the parameters for the respective voxels, wherein the calculating further comprises sequentially determining the approximated set by the quantitative MRI modelling technique and respectively the super-resolution imaging technique thereby obtaining the residual; and
updating by an inner loop the approximated set with the residual by the quantitative MRI modelling technique;
wherein the combined performing is performed until the residual is less than a predefined convergence threshold.
2. The computer implemented method according to claim 1 , wherein the inner loop further comprises imposing optimization constraints on the parameterized voxel-based model.
3. The computer implemented method according to claim 1 , wherein the weighted MRI volume scans comprise diffusion weighted images; and
wherein the quantitative MRI modelling technique comprises a multicompartment spherical deconvolution technique.
4. The computer implemented method according to claim 3 , wherein the diffusion weighted images comprise images according to at least two orientations and at least two strengths of a diffusion sensitizing gradient.
5. The computer implemented method according to claim 1 , wherein the parameters describe functions on spheres.
6. The computer implemented method according to claim 1 , wherein the parameters comprise spherical harmonics.
7. The computer implemented method according to claim 1 , wherein the parameters comprise fibre-orientation distribution functions.
8. The computer implemented method according to claim 1 , wherein the tissue comprises a brain tissue, and
wherein the fibre-orientation distribution functions are indicative for properties of white matter in the brain tissue.
9. The computer implemented method according to claim 8 , wherein the parameters are indicative for properties of the white matter density, a gray matter density, and/or a cerebrospinal fluid density.
10. A data processing apparatus comprising at least a processor and a non-transitory memory configured for carrying out the method according to claim 1 .
11. A magnetic resonance imaging scanner comprising the data processing apparatus according to claim 10 .
12. A non-transitory computer-readable data carrier having stored thereon a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to claim 1 .