BACKGROUND

The brain is composed of grey and white matter. These tissue types are formed by the arrangement of approximately 100 billion highly organized, interconnected and communicating neurons. The neuron's cell bodies form the grey matter where signals are received and processed. Each cell body has a long axon that extends out and bundles with other axons to form the white matter tracts that transmit signals throughout the brain and out to the body. Analysis of the grey matter and/or white matter tracts may be used to evaluate the extent of brain injury caused, for example, by stroke, traumatic brain injury, or other neurological diseases.

SUMMARY

Some embodiments relate to a computerized system for evaluating an impact of injury to brain networks or regions. The system comprises at least one computer processor, and at least one computer-readable medium encoded with a plurality of instructions that, when executed by the at least one computer processor, perform a method of evaluating an impact of injury to one or more brain networks or regions. The method comprises receiving magnetic resonance imaging (MRI) data of a brain of an individual, wherein the MRI data includes a first volumetric dataset recorded using first imaging parameters and a second volumetric dataset recorded using second imaging parameters, combining, on a voxel-by-voxel basis, first MRI data based on the first volumetric dataset and second MRI data based on the second volumetric dataset to produce a volumetric injury map, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map that specifies eloquent brain tissue within the one or more brain networks or regions to determine an impact of injury within the one or more brain networks, and displaying a visualization of the determined impact of injury within the one or more brain networks or regions.

In one aspect, the method further comprises processing the first volumetric dataset to extract a first volumetric brain map for a first feature, and combining the first MRI data and the second MRI data is performed using the first volumetric brain map for the first feature.

In another aspect, the method further comprises processing the first MRI data to extract a second volumetric brain map for a second feature, and combining the first MRI data and the second MRI data is further performed using the second volumetric brain map for the second feature.

In another aspect, the first volumetric dataset comprises diffusion tensor imaging (DTI) data, the first feature is a first one selected from the group consisting of fractional anisotropy, radial diffusivity, mean diffusivity and axial diffusivity, and the second feature is a second one selected from the group consisting of fractional anisotropy, radial diffusivity, mean diffusivity and axial diffusivity.

In another aspect, the method further comprises transforming each of the first MRI data and the second MRI data into a common volumetric coordinate space, and combining the first MRI data and the second MRI data is performed using the transformed first MRI data and the transformed second MRI data.

In another aspect, the method further comprises applying a mask to the first MRI data and the second MRI, wherein the mask limits each of the first and second MRI data to voxels corresponding to a particular tissue type or anatomical region, and combining the first MRI data and the second MRI data is performed using the first MRI data and the second MRI data after the mask is applied.

In another aspect, the method further comprises normalizing at least some of the first MRI data to produce normalized first MRI data, and normalizing at least some of the second MRI data to produce normalized second MRI data, and combining the first MRI data and the second MRI data is performed using the normalized first MRI data and the normalized second MRI data.

In another aspect, normalizing at least some of the first MRI data and normalizing at least some of the second MRI data comprises using one or more of internal normalization, regional internal normalization and swapped regional internal normalization.

In another aspect, the method further comprises thresholding the normalized first MRI data to produce thresholded normalized first MRI data, and thresholding the normalized second MRI data to produce thresholded normalized second MRI data, and combining the first MRI data and the second MRI data is performed using the thresholded normalized first MRI data and the thresholded normalized second MRI data.

In another aspect, thresholding the normalized first MRI data comprises including in the thresholded normalized first MRI data only values that fall within two standard deviations from a mean value of the normalized first MRI data.

In another aspect, the method further comprises selecting a threshold value based, at least in part, on a type of MRI scan used to collect the first volumetric dataset and/or a neurological disease of the individual, and thresholding the normalized first MRI data comprises applying the selected threshold value to the normalized first MRI data to produce the thresholded normalized first MRI data.

In another aspect, combining on a voxel-by-voxel basis, the first MRI data and the second MRI data to produce a volumetric injury map comprises generating, for each voxel, a weighted combination of the first MRI data and the second MRI data.

In another aspect, the method further comprises selecting a first weighting factor based on a type of MRI scan used to collect the first volumetric dataset, selecting a second weighting factor based on a type of MRI scan used to collect the second volumetric dataset, and generating a weighted combination of the first MRI data and the second MRI data comprises combining the first MRI data weighted by the first weighting factor and the second MRI data weighted by the second weighting factor.

In another aspect, the first weighting factor and/or the second weighting factor are further selected based on a neurological disease of the individual.

In another aspect, generating a weighted combination of the first MRI data and the second MRI data for each voxel is based, at least in part, on a value of one or more other voxels in the first MRI data and/or the second MRI data.

In another aspect, the one or more other voxels include at least one voxel that neighbors the voxel for which the weighted combination is being generated.

In another aspect, generating a weighted combination of the first MRI data and the second MRI data for each voxel is based, at least in part, on a tissue type associated with the voxel.

In another aspect, the method further comprises applying a first weighting factor when the tissue type associated with the voxel is white matter, applying a second weighting factor when the tissue type associated with the voxel is grey matter.

In another aspect, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map comprises combining, on a voxel-by-voxel basis, the volumetric injury map and the volumetric eloquence map to produce an injury mask.

In another aspect, combining, on a voxel-by-voxel basis, the first MRI data and the second MRI data to produce a volumetric injury map and combining, on a voxel-by-voxel basis, the volumetric injury map and the volumetric eloquence map are merged into a single step.

In another aspect, performing a structural-functional analysis of one or more brain networks or regions further comprises comparing the injury mask to a library of masks, and determining the impact of the injury based on the comparison of the injury mask to the library of masks.

In another aspect, each mask in the library of masks indicates grey matter nodes and white matter tracts for a structural-functional unit in the brain, and determining the impact of the injury based on the comparison of the injury mask to the library of masks is further based on a spatial overlap between voxels in the injury mask and a structural-functional unit for a particular mask in the library of masks.

In another aspect, performing a structural-functional analysis of one or more brain networks or regions to determine an impact of injury within the one or more brain networks or regions comprises determining a location of voxels within the one or more brain networks or regions impacted by the injury.

In another aspect, performing a structural-functional analysis of one or more brain networks or regions to determine an impact of injury within the one or more brain networks or regions comprises determining a volume of the one or more brain networks or regions impacted by the injury.

In another aspect, the method further comprises estimating recoverability from the injury based, at least in part, on the structural-functional analysis and/or a tissue type.

In another aspect, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map comprises combining, on a voxel-by-voxel basis, the volumetric injury map and the volumetric eloquence map to produce an injury mask, and estimating recoverability from the injury comprises determining a proportion of voxels in the injury mask associated with white matter, and estimating the recoverability from the injury based on the determined proportion of voxels in the injury mask associated with white matter.

Some embodiments relate to a computer-implemented method for evaluating an impact of injury to brain networks or regions. The method comprises receiving magnetic resonance imaging (MRI) data of a brain of an individual, wherein the MRI data includes a first volumetric dataset recorded using first imaging parameters and a second volumetric dataset recorded using second imaging parameters, combining, on a voxel-by-voxel basis, first MRI data based on the first volumetric dataset and second MRI data based on the second volumetric dataset to produce a volumetric injury map, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map that specifies eloquent brain tissue within the one or more brain networks or regions to determine an impact of injury within the one or more brain networks or regions, and displaying a visualization of the determined impact of injury within the one or more brain networks or regions.

Some embodiments relate to a non-transitory computer-readable medium encoded with a plurality of instructions that, when executed, by at least one computer processor performs a method of evaluating an impact of injury to brain networks or regions. The method comprises receiving magnetic resonance imaging (MRI) data of a brain of an individual, wherein the MRI data includes a first volumetric dataset recorded using first imaging parameters and a second volumetric dataset recorded using second imaging parameters, combining, on a voxel-by-voxel basis, first MRI data based on the first volumetric dataset and second MRI data based on the second volumetric dataset to produce a volumetric injury map, performing a structural-functional analysis of one or more brain networks or regions by refining the volumetric injury map using a volumetric eloquence map that specifies eloquent brain tissue within the one or more brain networks or regions to determine an impact of injury within the one or more brain networks or regions, and displaying a visualization of the determined impact of injury within the one or more brain networks or regions.

Some embodiments are directed to techniques for combining multiple types of MRI data to evaluate, diagnose, stage and/or track the progress/recovery of a neurological disease or disorder, non-limiting examples of which include stroke, moyamoya disease, transient ischemic attack, concussion (mild traumatic brain injury (mTBI)), cumulative subconcussive impacts, chronic traumatic encephalography (CTE), Alzheimer's disease, dementia, and other neurological conditions.

Some embodiments are directed to combining two or more types of independently collected neuroimaging data (either in the same scan or different scan) to produce a composite image of a subject's injury. This composite image can be evaluated directly by a physician or can be compared to a reference library of neurological masks to determine the location, magnitude, recoverability and/or likely functional impact of an injury.

It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein.

