# US 11,026,626 B2 · Tissue-orientation-based simulation of deep brain stimulation

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- Number: US 11,026,626 B2
- Document ID: US11026626B2
- Status: Granted
- Kind: B2
- Application number: 15/540,110
- Filed: 2016-01-18
- Published: 2018-01-25
- Granted: 2021-06-08
- Earliest claimed: 2015-01-19
- Assignees: Brainlab AG
- Applicants: Brainlab AG
- Inventors: Bálint Varkuti
- Web page: https://patentgrep.com/patent/US11026626B2

## CPC

- A61B 5/4893: {Nerves}
- A61B 5/0035: {adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography}
- A61B 5/0042: {for the brain}
- A61B 5/743: {Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots}
- A61B 6/032: {Transmission computed tomography [CT]}
- G01R 33/4808: {Multimodal MR, e.g. MR combined with positron emission tomography [PET], MR combined with ultrasound or MR combined with computed tomography [CT]}
- G01R 33/56341: {Diffusion imaging}
- G06T 7/0014: {using an image reference approach}
- G06T 7/11: Region-based segmentation
- G06T 7/337: {involving reference images or patches}
- G06T 7/74: {involving reference images or patches}
- G06T 7/75: {involving models}
- G06T 7/90: Determination of colour characteristics
- A61N 1/0534: {Electrodes for deep brain stimulation}
- G06T 2207/10024: Color image
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/10092: Diffusion tensor magnetic resonance imaging [DTI]
- G06T 2207/20128: Atlas-based segmentation
- G06T 2207/30016: Brain
- G16H 50/50: for simulation or modelling of medical disorders
- 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)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A method is provided for determining an orientation of nerve fibres relative to a non-physiological electric field. Patient medical image data is acquired, which describes a patient medical image of an anatomical body part of a patient's body. The anatomical body part includes nerve tissue comprising white matter nerve fibres. Diffusion image data is acquired, which describes a diffusion-enhanced image of the anatomical body part. Atlas data is acquired, which describes a spatial distribution of grey value-based tissue classes in a model body part representing a model of the anatomical body part. Based on the patient image data, the diffusion image data, and the atlas data, fibre orientation data is determined. The fibre orientation data describes an orientation of the white matter nerve fibres. Electric field orientation data is acquired, which describes an orientation of the non-physiological electric field. The non-physiological electric field is an electric field simulated around a simulated electrode virtually placed in a predetermined spatial relationship relative to the anatomical body part. Based on the fibre orientation data and the electric field orientation data, relative orientation data is determined. The relative orientation data describes a relative orientation between the orientation of the white matter nerve fibres and the orientation of the electric field.

## Claims

### Claim 1

A medical visualization system for determining an orientation of nerve fibers relative to a non-physiological electric field, comprising:
a display device; and
a computer having a processor, the computer being configured to:
acquire patient medical image data describing a patient medical image of an anatomical body part of a body of an associated patient, the anatomical body part including nerve tissue comprising white matter nerve fibers;
acquire diffusion image data describing a diffusion-enhanced image of the anatomical body part;
acquire atlas data describing a spatial distribution of grey value-based tissue classes in a model body part representing a model of the anatomical body part;
determine, based on the patient image data and the diffusion image data and the atlas data, fiber orientation data describing an orientation of the white matter nerve fibers, wherein the atlas data serves as a comparison for determining the area in which the nerve fibers are positioned in the diffusion image data, and the diffusion image data is used to reconstruct a diffusion tensor which describes the orientation of the nerve fibers and determining the fiber orientation data includes determining a transformation between a position of the anatomical body part in the diffusion-enhanced image and a position of the anatomical body part in the patient medical image, wherein the patient medical image is defined by grey values and wherein the position of the white matter in the patient medical image is determined by comparing the grey values of the patient medical image to the tissue classes described by the atlas data;
acquire electric field orientation data describing an orientation of the non-physiological electric field, the non-physiological electric field being an electric field simulated around a simulated electrode virtually placed in a predetermined spatial relationship relative to the anatomical body part;
determine, based on the fiber orientation data and the electric field orientation data, relative orientation data describing a relative orientation between the orientation of the white matter nerve fibers and the orientation of the electric field, the relative orientation being described as an orientation angle between the orientation of the white matter nerve fibers and the orientation of the electric field;
determine indication signal data describing an indication signal to be output to a user using the information content of the relative orientation data,
wherein the indication signal data comprises relative orientation visualization data describing a visualization of the relative orientation between the white matter nerve fibers and the electric field, wherein the visualization indicates the orientation angle described by the relative orientation data,
wherein the visualization comprises a colored display of the patient medical image or the diffusion-enhanced image, and wherein angular threshold data is acquired describing a threshold angle,
wherein image units of the patient medical image or the diffusion-enhanced image, respectively, are constituted to be displayed in different colors in dependence on the relationship between the relative orientation between the white matter nerve fibers and the electric field and the threshold angle; and
output, to a user by the display device, the indication signal.

### Claim 2

A computer-implemented medical method for determining an orientation of nerve fibers relative to a non-physiological electric field, the method comprising executing, by a processor of a computer, steps of:
acquiring patient medical image data describing a patient medical image of an anatomical body part of a body of an associated patient, the anatomical body part including nerve tissue comprising white matter nerve fibers;
acquiring diffusion image data describing a diffusion-enhanced image of the anatomical body part;
acquiring atlas data describing a spatial distribution of grey value-based tissue classes in a model body part representing a model of the anatomical body part;
determining, based on the patient image data and the diffusion image data and the atlas data, fiber orientation data describing an orientation of the white matter nerve fibers, wherein the atlas data serves as a comparison for determining the area in which the nerve fibers are positioned in the diffusion image data, and the diffusion image data is used to reconstruct a diffusion tensor which describes the orientation of the nerve fibers and determining the fiber orientation data includes determining a transformation between a position of the anatomical body part in the diffusion-enhanced image and a position of the anatomical body part in the patient medical image, wherein the patient medical image is defined by grey values and wherein the position of the white matter in the patient medical image is determined by comparing the grey values of the patient medical image to the tissue classes described by the atlas data;
acquiring electric field orientation data describing an orientation of the non-physiological electric field, the non-physiological electric field being an electric field simulated around a simulated electrode virtually placed in a predetermined spatial relationship relative to the anatomical body part;
determining, based on the fiber orientation data and the electric field orientation data, relative orientation data describing a relative orientation between the orientation of the white matter nerve fibers and the orientation of the electric field, the relative orientation being described as an orientation angle between the orientation of the white matter nerve fibers and the orientation of the electric field;
determining indication signal data describing an indication signal to be output to a user using the information content of the relative orientation data,
wherein the indication signal data comprises relative orientation visualization data describing a visualization of the relative orientation between the white matter nerve fibers and the electric field, wherein the visualization indicates the orientation angle described by the relative orientation data,
wherein the visualization comprises a colored display of the patient medical image or the diffusion-enhanced image, and wherein angular threshold data is acquired describing a threshold angle,
wherein image units of the patient medical image or the diffusion-enhanced image, respectively, are constituted to be displayed in different colors in dependence on the relationship between the relative orientation between the white matter nerve fibers and the electric field and the threshold angle; and
outputting, to a user using an associated indication device for indicating digital information, the indication signal.

### Claim 3 (depends on 2)

The method according to claim 2, wherein determining the fiber orientation data includes determining, by the processor, a transformation between a position of the anatomical body part in the diffusion-enhanced image and a position of the anatomical body part in the patient medical image, wherein the patient medical image is defined by grey values and wherein the position of the white matter in the patient medical image is determined by comparing the grey values of the patient medical image to the tissue classes described by the atlas data.

### Claim 4 (depends on 2)

The method according to claim 2, wherein the patient medical image data is generated based on a computed x-ray tomography or a magnetic resonance tomography of the anatomical body part and the diffusion image data is generated based on a magnetic resonance tomography of the anatomical body part.

### Claim 5 (depends on 2)

The method according to claim 2, wherein determining the fiber orientation data based on the diffusion image data further comprises determining the diffusion tensor describing the diffusion of a fluid in the anatomical body part, and determining a main axis of the diffusion tensor.

### Claim 6 (depends on 5)

The method according to claim 5, wherein the main axis of the diffusion tensor is defined as an orientation of the longest eigenvector of the diffusion tensor.

### Claim 7 (depends on 2)

The method according to claim 2, wherein the electric field orientation data describes an electric field tensor.

### Claim 8 (depends on 2)

The method according to claim 2, wherein the electric field orientation data describes an electric field tensor, the method further comprising:
determining, by the processor and based on the electric field orientation data, a main axis of the electric field tensor,
wherein the relative orientation data describes an angle, at the position of a white matter nerve fiber, between the main axis of the diffusion tensor and the main axis of the electric field tensor.

### Claim 9 (depends on 8)

The method according to claim 8, wherein the main axis of the electric field tensor is defined as the orientation of the longest eigenvector of the electric field tensor.

### Claim 10 (depends on 2)

The method according to claim 2, wherein determining the fiber orientation data includes acquiring, at the processor, anisotropy threshold data describing an anisotropy threshold defining an anisotropy of a diffusion of a fluid described by the diffusion-enhanced image, and wherein only parts of the diffusion-enhanced image associated with an anisotropy greater than or equal to the anisotropy threshold serve as a basis for determining the fiber orientation data.

### Claim 11 (depends on 2)

The method according to claim 2, further comprising:
varying the electric field; and
determining respective fiber orientation data for the varied electric field.

### Claim 12

A non-transitory computer-readable program storage medium storing a program which, when executed by a processor of a computer, causes the computer to execute a computer-implemented medical method for determining an orientation of nerve fibers relative to a non-physiological electric field, the method comprising:
acquiring patient medical image data describing a patient medical image of an anatomical body part of a body of an associated patient, the anatomical body part including nerve tissue comprising white matter nerve fibers;
acquiring diffusion image data describing a diffusion-enhanced image of the anatomical body part;
acquiring atlas data describing a spatial distribution of grey value-based tissue classes in a model body part representing a model of the anatomical body part;
determining, based on the patient image data and the diffusion image data and the atlas data, fiber orientation data describing an orientation of the white matter nerve fibers, wherein the atlas data serves as a comparison for determining the area in which the nerve fibers are positioned in the diffusion image data, and the diffusion image data is used to reconstruct a diffusion tensor which describes the orientation of the nerve fibers and determining the fiber orientation data includes determining a transformation between a position of the anatomical body part in the diffusion-enhanced image and a position of the anatomical body part in the patient medical image, wherein the patient medical image is defined by grey values and wherein the position of the white matter in the patient medical image is determined by comparing the grey values of the patient medical image to the tissue classes described by the atlas data;
acquiring electric field orientation data describing an orientation of the non-physiological electric field, the non-physiological electric field being an electric field simulated around a simulated electrode virtually placed in a predetermined spatial relationship relative to the anatomical body part;
determining, based on the fiber orientation data and the electric field orientation data, relative orientation data describing a relative orientation between the orientation of the white matter nerve fibers and the orientation of the electric field, the relative orientation being described as an orientation angle between the orientation of the white matter nerve fibers and the orientation of the electric field;
determining indication signal data describing an indication signal to be output to a user using the information content of the relative orientation data,
wherein the indication signal data comprises relative orientation visualization data describing a visualization of the relative orientation between the white matter nerve fibers and the electric field, wherein the visualization indicates the orientation angle described by the relative orientation data,
wherein the visualization comprises a colored display of the patient medical image or the diffusion-enhanced image, and wherein angular threshold data is acquired describing a threshold angle,
wherein image units of the patient medical image or the diffusion-enhanced image, respectively, are constituted to be displayed in different colors in dependence on the relationship between the relative orientation between the white matter nerve fibers and the electric field and the threshold angle; and
outputting, to a user using an associated indication device for indicating digital information, the indication signal.

### Claim 13 (depends on 12)

A computer comprising the non-transitory computer-readable program storage medium according to claim 12.