Apparatus to analyse diffusion magnetic resonance imaging data

An apparatus to analyze diffusion magnetic resonance imaging data, the apparatus comprising:

• an input unit;
• a processing unit;
• and
• an output unit;
• wherein, the input unit is configured to provide the processing unit with a set of diffusion-weighted diffusion magnetic resonance imaging (dMRI) image of a patient's brain;
• wherein, the processing unit is configured to determine an estimate of an orientation of neurons at each voxel of a brain volume in the dMRI image set, the determination comprising determining a spherical probability distribution function at each voxel of the brain volume relating to probabilities of diffusion in a given direction at each voxel based on the set of diffusion weighted images, and
• determining the estimated orientation of neurons at each voxel by utilizing a peak in the spherical probability distribution function at each voxel;
• wherein three or more regions of the brain volume in the dMRI image set are segmented;
• wherein the processing unit is configured to determine at least one diffusivity feature for each voxel of the brain volume based on the set of diffusion-weighted images;
• wherein, the processing unit is configured to determine a plurality of fiber tracts in the brain volume in the dMRI image set, the determination comprising utilization of the estimated orientation of neurons at each voxel of the brain volume;
• wherein the processing unit is configured to identify a plurality of pairs of the segmented regions which are connected by fiber tracts to one another, and for each pair of connected segmented regions:
• select a plurality of voxels along fiber tracts connecting the pair of regions,
• provide the at least one diffusivity feature of each of the selected voxels as inputs to a multi-input deep neural network which comprises a respective sparse 3D convolutional neural network for each pair of segmented regions, wherein each respective sparse neural network outputs a respective feature vector for the pair of regions based on the input features,
• wherein, the processing unit is configured to determine a neurological disease classification, wherein the multi-input deep neural network concatenates the feature vectors generated for each of the pairs of brain regions in a further fully connected layer, and wherein an output of the multi-input deep neural network is the classification of the neurological disease;
• and
• wherein, the output unit is configured to output the neurological disease classification,
• wherein the multi-input deep neural network is trained on the basis of a plurality of dMRI image sets of reference patients' brains and information relating to neurological disease information for each of the reference patients.