Processing of brain image data to assign voxels to parcellations

1. A method comprising:

obtaining brain tractography data of a particular brain;

determining a registration function for the brain tractography data of the particular brain in a three dimensional coordinate system space;

determining a registered atlas from the registration function and a standard Atlas containing a standard parcellation scheme;

for at least one voxel in a particular parcellation in the registered atlas:

determining end-to-end voxel level tractography vectors showing end-to-end connectivity of the voxel with voxels in other parcellations;

determining pass-through parcelleted voxel level tractography vectors;

classifying the voxel to determine labels and a voxel grid for end-to-end parcellated voxel level tractography vectors, based on probability of the voxel being part of the particular parcellation; and

repeating the determining of the end-to-end voxel level tractography vectors and the classifying of the voxels for a plurality of parcellations of the registered atlas to form a personalised brain atlas containing an adjusted parcellation scheme reflecting the particular brain.

2. The method according to claim 1 , further comprising, prior to the repeating step, the step of interpolating the voxel grid for end-to-end parcellated voxel level tractography vectors to fill gaps between voxels.

3. The method according to claim 2 , further comprising, for each voxel in a particular parcellation in the registered atlas:

determining end-to-end voxel level tractography vectors and pass-through parcellated voxel level tractography vectors respectively showing end-to-end and pass-through connectivity of the voxel with voxels in other parcellations;

classifying the voxel to determine labels and a voxel grid for end-to-end parcellated voxel level tractography vectors and pass-through parcellated voxel level tractography vectors, based on the probability of the voxel being part of the particular parcellation; and

interpolating the voxel grid for end-to-end parcellated voxel level tractography vectors and pass-through parcellated voxel level tractography vectors to fill gaps between voxels.

4. The method according to claim 1 , wherein the step of determining the registration function for the particular brain in the three dimensional coordinate system space comprises the steps of:

performing face stripping, skull stripping and masking of a NIfTI version of T 1 images of brain image data to obtain a masked, skull and face stripped T 1 image; and

determining a relationship between the masked, skull and face stripped T 1 image and a set of standard brain data image sets to generate the registration function.

5. The method according to claim 1 , wherein the step of determining the registered atlas comprises applying the registration function to the standard Atlas to generate the registered atlas.

6. The method according to claim 1 , wherein the method further includes performing diffusion tractography on brain image data performed in relation to a face stripped masked NIfTI version of DTI images of a DICOM image set.

7. The method according to claim 1 , wherein the determining of the voxel level tractography vectors comprises the steps of:

registering the registered Atlas and a brain tractography image set; and

generating end-to-end parcellated voxel level tractography vectors.

8. The method according to claim 1 , wherein classifying the voxel comprises processing the end-to-end parcellated voxel level tractography vectors and the pass-through parcellated voxel level tractography vectors with an end-to-end classifier and a pass-by classifier to form the voxel grid.

9. The method according to claim 1 , wherein the plurality of parcellations comprises all parcellations in the registered atlas.

10. The method according to claim 1 , wherein the three dimensional coordinate system space is Montreal Neurological Institute space described by a set (HCP-SDB) of standard brain data image sets.

11. The method according to claim 1 , wherein the standard Atlas is a HCP-MMP1 Atlas.

12. The method of claim 1 , wherein the brain tractography data of the particular brain are whole brain tractography images of the particular brain.

13. A system comprising:

one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

obtaining brain tractography data for a particular brain;

determining a registration function for the brain tractogrpahy data of the particular brain in a specified space;

determining a registered atlas from the registration function and a standard Atlas containing a standard parcellation scheme;

for at least one voxel in a particular parcellation in the registered atlas:

determining end-to-end voxel level tractography vectors showing end-to-end connectivity of the voxel with voxels in other parcellations;

determining pass-through parcellated voxel level tractography vectors; and

classifying the voxel to determine labels and a voxel grid for end-to-end parcellated voxel level tractography vectors, based on probability of the voxel being part of the particular parcellation; and

 repeating the determining of the end-to-end voxel level tractography vectors and the classifying of the voxels for a plurality of parcellations of the registered atlas to form a personalised brain atlas containing an adjusted parcellation scheme reflecting the particular brain.

14. The system according to claim 13 , wherein the operations further comprise, prior to the repeating step, the step of interpolating the voxel grid for end-to-end parcellated voxel level tractography vectors to fill gaps between voxels.

15. The system according to claim 13 , wherein the step of determining the registered atlas comprises applying the registration function to the standard Atlas to generate the registered atlas.

16. The system according to claim 13 , wherein operations further comprise the step of performing diffusion tractography on brain image data performed in relation to a face stripped masked NIfTI version of DTI images of a DICOM image set.

17. The system according to claim 13 , wherein the plurality of parcellations comprises all parcellations in the registered atlas.

18. The system according to claim 13 , wherein the determining of the voxel level tractography vectors comprises the steps of:

registering the registered Atlas and a brain tractography image set; and

generating end-to-end parcellated voxel level tractography vectors.

19. A computer readable storage medium having one or more computer programs recorded therein, the one or more programs being executable by a computer apparatus to make the computer apparatus perform a method of processing brain image data of a particular brain to be parcellated, the method comprising the steps of:

obtaining brain tractography data of a particular brain;

determining a registration function for the brain tractography data of the particular brain in a three dimensional coordinate system space;

determining a registered atlas from the registration function and a standard Atlas containing a standard parcellation scheme;

for at least one voxel in a particular parcellation in the registered atlas:

determining end-to-end voxel level tractography vectors showing end-to-end connectivity of the voxel with voxels in other parcellations;

determining pass-through parcellated voxel-level tractography vectors;

classifying the voxel to determine labels and a voxel grid for end-to-end parcellated voxel level tractography vectors, based on probability of the voxel being part of the particular parcellation; and

repeating the determining of the end-to-end voxel level tractography vectors and the classifying of the voxels for a plurality of parcellations of the registered atlas to form a personalised brain atlas containing an adjusted parcellation scheme reflecting the particular brain.