# US 12,223,666 B2 · Methods for optimizing the planning and placement of probes in the brain via multimodal 3D analyses of cerebral anatomy

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- Number: US 12,223,666 B2
- Document ID: US12223666B2
- Status: Granted
- Kind: B2
- Application number: 18/448,289
- Filed: 2023-08-11
- Published: 2024-07-04
- Granted: 2025-02-11
- Earliest claimed: 2020-02-20
- Assignees: Board of Regents, The University of Texas System
- Applicants: Board of Regents of the University of Texas System
- Inventors: Nitin Tandon; Mehmet Kadipasaoglu; Kevin Pham; Cristian Donos; Kiefer Forseth; Patrick Sarahan Rollo
- Web page: https://patentgrep.com/patent/US12223666B2

## CPC

- G06T 7/37: using transform domain methods
- 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 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
- A61B 5/37: Intracranial electroencephalography [IC-EEG], e.g. electrocorticography [ECoG]
- A61B 5/4806: {Sleep evaluation (A61B5/4821 takes precedence; devices for inducing sleep A61M21/02)}
- A61B 5/4836: {Diagnosis combined with treatment in closed-loop systems or methods (A61B5/0036 takes precedence)}
- A61B 5/6868: {Brain}
- A61B 5/6891: {Furniture}
- A61B 5/7425: {Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image}
- A61B 6/501: {for diagnosis of the head, e.g. neuroimaging or craniography}
- A61B 34/10: Computer-aided planning, simulation or modelling of surgical operations
- G01R 33/5607: {by reducing the NMR signal of a particular spin species, e.g. of a chemical species for fat suppression, or of a moving spin species for black-blood imaging}
- G06T 7/11: Region-based segmentation
- G06T 7/174: involving the use of two or more images
- G06T 7/33: using feature-based methods
- G06T 7/38: Registration of image sequences
- A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)}
- A61B 5/4815: {Sleep quality}
- A61B 5/4818: {Sleep apnoea}
- A61B 5/489: {Blood vessels}
- A61B 5/6814: {Head}
- A61B 6/5229: {combining image data of a patient, e.g. combining a functional image with an anatomical image}
- A61B 2034/102: {Modelling of surgical devices, implants or prosthesis}
- A61B 2034/105: {Modelling of the patient, e.g. for ligaments or bones}
- A61B 2034/107: {Visualisation of planned trajectories or target regions}
- A61B 2576/026: for the brain
- G01R 33/5601: {involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent}
- 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)}
- G06T 2207/10072: Tomographic images
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/10104: Positron emission tomography [PET]
- 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

A method includes obtaining a first imaging scan and a second imaging scan of a single subject brain. The first imaging scan is converted to a first dataset, and the second imaging scan is converted to a second dataset. A sequence-adaptive multimodal segmentation algorithm is applied to the first dataset and the second dataset. The sequence-adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset. The first labeled dataset and the second labeled dataset are automatically co-registered to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset. The transformation matrix is applied to align the first dataset and the second dataset.

## Claims

### Claim 1

A method, comprising:
obtaining a first imaging scan and a second imaging scan of a single subject brain;
converting the first imaging scan to a first dataset, and the second imaging scan to a second dataset;
applying an adaptive segmentation algorithm to the first dataset and the second dataset, wherein the adaptive segmentation algorithm performs automatic intensity-based tissue classification to generate a first labeled dataset and a second labeled dataset;
automatically co-registering the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset; and
applying the transformation matrix to align the first dataset and the second dataset.

### Claim 2 (depends on 1)

The method of claim 1, wherein:
the single subject brain comprises a cerebrospinal fluid region;
the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset; and
the method further comprises:
selecting voxels of the first dataset identified as belonging to the cerebrospinal fluid region;
applying a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel;
integrating the vesselness weight values into the first dataset; and
after aligning the first dataset and the second dataset, converting the first dataset to a surface anatomical mesh model.

### Claim 3 (depends on 1)

The method of claim 1, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset.

### Claim 4 (depends on 1)

The method of claim 1 wherein the method further comprises:
defining predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points;
defining a trajectory for the probe based on a mean target coordinates and mean entry point coordinates;
adjusting the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest;
checking proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints; and
superimposing the trajectory on the second data set to form a planning dataset.

### Claim 5 (depends on 1)

The method of claim 1, wherein:
the first imaging scan is an anatomical scan, the second imaging scan is a post-implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation imaging dataset; and
the method further comprises:
obtaining a third imaging scan used to guide electrode implantation during surgery;
converting the third imaging scan to a third dataset;
aligning a third dataset with the first dataset;
obtaining a trajectory implant data file created during the electrode implantation;
generating a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry;
aligning the planned trajectory dataset to the imaging dataset; and
automatically identifying and labelling electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file.

### Claim 6

A non-transitory computer-readable medium encoded with instructions that are executable by one or more processors to:
obtain a first imaging scan and a second imaging scan of a single subject brain;
convert the first imaging scan to a first dataset, and the second imaging scan to a second dataset;
apply an adaptive segmentation algorithm to the first dataset and the second dataset, wherein the adaptive segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset;
automatically co-register the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset; and
apply the transformation matrix to align the first dataset and the second dataset.

### Claim 7 (depends on 6)

The non-transitory computer-readable medium of claim 6, wherein:
the single subject brain comprises a cerebrospinal fluid region;
the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset; and
the instructions are executable by the one or more processors to:
select, based on the labeled dataset, voxels of the first dataset identified as belonging to the cerebrospinal fluid region;
apply a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel;
integrate the vesselness weight values into the first dataset; and
after aligning the first dataset and the second dataset, convert the first dataset to a surface anatomical mesh model.

### Claim 8 (depends on 6)

The non-transitory computer-readable medium of claim 6, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset.

### Claim 9 (depends on 6)

The non-transitory computer-readable medium of claim 6, wherein:
the first imaging scan is a contrast weighted scan and the second imaging scan is an anatomical scan; and
the instructions are executable by the one or more processors to:
define predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points;
define a trajectory for the probe based on a mean target coordinates and mean entry point coordinates;
adjust the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest;
check proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints; and
superimpose the trajectory on the second data set to form a planning dataset.

### Claim 10 (depends on 6)

The non-transitory computer-readable medium of claim 6, wherein:
the first imaging scan is an anatomical scan, the second imaging scan is a post-implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation dataset; and
the instructions are executable by the one or more processors to:
obtain a third imaging scan used to guide electrode implantation during surgery;
convert the third imaging scan to a third dataset;
align a third dataset with the first dataset;
obtain a trajectory implant data file created during the electrode implantation;
generate a planned trajectory dataset, based on trajectory implant data file, that includes dummy objects disposed at locations of electrode geometry;
align the planned trajectory dataset to the post-implantation dataset; and
automatically identify and label electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file.

### Claim 11

A system, comprising:
one or more processors; and
a memory coupled to the one or more processors, wherein the memory stores instructions that configure the one or more processors to:
obtain a first imaging scan and a second imaging scan of a subject brain;
convert the first imaging scan to a first dataset, and the second imaging scan to a second dataset;
apply an adaptive segmentation algorithm to the first dataset and the second dataset, wherein the adaptive segmentation algorithm performs automatic intensity-based tissue classification to generate a first labeled dataset and a second labeled dataset;
automatically co-register the first labeled dataset and the second labeled dataset to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset; and
apply the transformation matrix to align the first dataset and the second dataset.

### Claim 12 (depends on 11)

The system of claim 11, wherein:
the single subject brain comprises a cerebrospinal fluid region;
the first imaging scan is a contrast weighted scan and the first dataset is a contrast weighted dataset; and
the instructions configure the one or more processors to:
select, based on the labeled dataset, voxels of the first dataset identified as belonging to the cerebrospinal fluid region;
apply a filtering algorithm to identify voxels of the first dataset representing blood vessels and assign a vesselness weight value to each voxel;
integrate the vesselness weight values into the first dataset; and
after aligning the first dataset and the second dataset, convert the first dataset to a surface anatomical mesh model.

### Claim 13 (depends on 11)

The system of claim 11, wherein the adaptive segmentation algorithm assigns a numeric label value to each voxel of the first dataset or the second dataset.

### Claim 14 (depends on 11)

The system of claim 11, wherein the instructions configure the one or more processors to:
extract voxels from the first dataset having a label corresponding to a subcortical region of interest;
form a third dataset containing the voxels extracted from the first dataset;
convert the third dataset into a first subcortical surface mesh model;
compute curvature and sulcal features of the first subcortical surface mesh model;
align the first subcortical surface mesh model to a subcortical atlas of the region of interest using the curvature and sulcal features; and
overlay the first subcortical surface mesh model aligned to an atlas of the subcortical region of interest on a second subcortical surface mesh model, the second subcortical surface mesh model having a standardized number of nodes that enables a one-to-one correspondence between node identity and atlas location; and
assign coordinates of nodes of the first subcortical surface mesh model to the second subcortical structure surface mesh model such that the second subcortical surface mesh model assumes a topology of the first subcortical structure surface mesh model.

### Claim 15 (depends on 11)

The system of claim 11, wherein:
the first imaging scan is a contrast weighted scan and the second imaging scan is an anatomical scan; and
the instructions configure the one or more processors to:
define predicted target point coordinates and entry point coordinates for a probe based on target point coordinates and entry point coordinates of previously implanted probes or by user defined target and entry points;
define a trajectory for the probe based on a mean target coordinates and mean entry point coordinates;
adjust the trajectory to intersect with a nearest voxel assigned a label of an anatomical region of interest;
check proximity of the trajectory to critical structures based on user defined constraints and/or user defined modification of the trajectory to satisfy the user defined constraints; and
superimpose the trajectory on the second data set to form a planning dataset.

### Claim 16 (depends on 11)

The system of claim 11, wherein:
the first imaging scan is an anatomical scan, the second imaging scan is a post-implantation imaging scan, the first dataset is an anatomical dataset, and the second dataset is a post-implantation imaging dataset; and
the instructions configure the one or more processors to:
obtain a third imaging scan used to guide electrode implantation during surgery;
convert the third imaging scan to a third dataset;
align a third dataset with the first dataset;
obtain a trajectory implant data file created during the electrode implantation;
generate a planned trajectory dataset, wherein:
the planned trajectory dataset is based on trajectory implant data file; and
the planned trajectory dataset includes dummy objects disposed at locations of electrode geometry;
align the planned trajectory dataset to the post-implantation imaging dataset; and
identify electrodes in an electrode dataset based on the dummy objects of the trajectory implant data file.