# US 2023/0056838 A1 · Apparatus to analyse diffusion magnetic resonance imaging data

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- Number: US 2023/0056838 A1
- Document ID: US20230056838A1
- Status: Published application
- Kind: A1
- Application number: 17/796,687
- Filed: 2021-01-03
- Published: 2023-02-23
- Earliest claimed: 2020-02-03
- Assignees: Koninklijke Philips N.V.
- Applicants: Koninklijke Philips N.V.
- Inventors: Evan Schwab
- Web page: https://patentgrep.com/patent/US20230056838A1

## CPC

- G06T 7/0012: {Biomedical image inspection}
- A61B 5/0042: {for the brain}
- A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
- A61B 5/4088: {Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia}
- G01R 33/5608: {Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T)}
- G01R 33/56341: {Diffusion imaging}
- G06T 7/11: Region-based segmentation
- G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80)
- G06T 2207/10092: Diffusion tensor magnetic resonance imaging [DTI]
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30016: Brain
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)

## Abstract

The present invention relates to an apparatus (10) to analyse diffusion magnetic resonance imaging data. The apparatus comprises an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide the processing unit with at least one diffusion magnetic resonance imaging “dMRI” image of a patient's brain. The processing unit is configured to determine an estimate of an orientation of neurons at each voxel in the dMRI image, the determination comprising utilization of the at least one dMRI image. The processing unit is configured to determine a plurality of fiber tracts in the at least one dMRI image, the determination comprising utilization of the estimated orientation of neurons at each voxel in the at least one dMRI image. The processing unit is configured to select a plurality of voxels along at least one fiber tract of the plurality of fiber tracts. The processing unit is configured to determine a neurological disease classification, the determination comprising utilization of at least one diffusivity feature associated with each of the selected plurality of voxels. The output unit is configured to output the neurological disease classification.

## Claims

### Claim 1

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.

### Claim at position 2

-7. (canceled)

### Claim 8 (depends on 1)

The apparatus of claim 1, wherein the processing unit is configured to determine at least one connectivity weight for each connected pair of segmented regions based on the number of fiber tracts that connect the two connected segmented regions.

### Claim 9 (depends on 1)

The apparatus of claim 1, wherein the processing unit is configured to determine at least one connectivity weight for each pair of connected segmented regions based on at least one diffusivity feature associated with each of a plurality of voxels along fiber tracts that connect the two connected segmented regions.

### Claim 10

(canceled)

### Claim 11 (depends on 1)

The apparatus of claim 1, wherein the at least one trained machine learning algorithm is at least one 3D convolutional neural network (CNN).

### Claim 12 (depends on 1)

The apparatus of claim 1, wherein the processing unit is configured to select voxels to be input to the input deep neural network for active or non-zero voxels of the plurality of pixels along the at least one fiber tract.

### Claim 13

(canceled)

### Claim 14 (depends on 1)

An imaging system, comprising:
a magnetic resonance image unit;
an apparatus (10) according to claim 1;
wherein, the magnetic resonance imaging unit is configured to acquire a set of diffusion-weighted diffusion magnetic resonance imaging (dMRI) image of a patient's brain, and provide the dMRI image to the apparatus,
and wherein, the apparatus is configured to output a neurological disease classification for the patient on the basis of the least one dMRI image.

### Claim 15

A method of analyzing diffusion magnetic resonance imaging data, the method comprising:
a) providing a processing unit with a set of diffusion-weighted diffusion magnetic resonance imaging (dMRI) image of a patient's brain wherein three or more regions of the brain volume in the dMRI image set are segmented;
b) determining by the processing unit an estimate of an orientation of neurons at each voxel of a brain volume in the dMRI image set, the determining 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;
ba) determining by the processing unit at least one diffusivity feature for each voxel of the brain volume based on the set of diffusion-weighted images c) determining by the processing unit a plurality of fiber tracts in the brain volume in the dMRI image set, the determining comprising utilizing the estimated orientation of neurons at each voxel of the brain volume;
d) identifying by the processing unit 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:
selecting by the processing unit a plurality of voxels along fiber tracks connecting the pair or regions and;
providing 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;
e) determining by the processing unit 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
f) outputting by an output unit the neurological disease classification.

### Claim 16

(canceled)