# US 2026/0065480 A1 · Methods for determining aortic disease severity using statistical shape modeling and universal coordinate system mesh analysis

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- Number: US 2026/0065480 A1
- Document ID: US20260065480A1
- Status: Published application
- Kind: A1
- Application number: 19/314,478
- Filed: 2025-08-29
- Published: 2026-03-05
- Earliest claimed: 2024-08-30
- Assignees: The Regents of the University of Michigan
- Applicants: Regents of the University of Michigan
- Inventors: Nicholas S. Burris; Carlos Alberto Campello Jorge; Prabhvir Singh Marway; Timothy Baker; David Nordsletten
- Web page: https://patentgrep.com/patent/US20260065480A1

## CPC

- G06T 7/0012: {Biomedical image inspection}
- G06T 7/0014: {using an image reference approach}
- G06T 7/11: Region-based segmentation
- G06T 17/20: Finite element generation, e.g. wire-frame surface description, {tesselation}
- G06V 10/82: using neural networks
- G16H 30/40: for processing medical images, e.g. editing
- G06T 7/62: of area, perimeter, diameter or volume
- G06T 2207/30101: Blood vessel; Artery; Vein; Vascular
- G06T 2207/30172: Centreline of tubular or elongated structure
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Systems and methods for classifying an aortic condition include capturing aorta image data, applying a mesh generation process, and determining an intrinsic coordinate system for the resulting mesh structure, including a plurality of vertices. The mesh structure is mapped to a reference coordinate system for generating, from the vertices, mapped vertices, which are embedded into the mesh structure to create an embedded mesh structure. The embedded mesh structure is compared to reference mesh structures including at least one normal aortic mesh structure and at least one pathology aortic mesh structure, and a similarity score is generated, and the aortic condition is classified.

## Claims

### Claim 1

A method for classifying an aortic condition of a patient from aorta image data of a patient, the method comprising:
applying, by one or more processors, the aorta image data to a mesh generator to generate a mesh structure of an aorta of the patient;
determining, by the one or more processors, an intrinsic coordinate system for the mesh structure and determining a plurality of vertices corresponding to the intrinsic coordinate system;
mapping, by the one or more processors, the mesh structure to a reference coordinate system and generating, from the plurality of vertices corresponding to the intrinsic coordinate system, a plurality of mapped vertices corresponding to the reference coordinate system;
embedding, by the one or more processors, the plurality of mapped vertices into the mesh structure to create an embedded mesh structure;
comparing, by the one or more processors, the embedded mesh structure to a plurality of reference mesh structures comprising at least one normal aortic mesh structure and at least one pathology aortic mesh structure, where each of the plurality of reference mesh structures correspond to the reference coordinate system, and generating a similarity score indicating a similarity of the embedded mesh structure to at least one of the plurality of reference mesh structures; and
based on the similarity score, classifying, by the one or more processors, the aortic condition of the patient.

### Claim 2 (depends on 1)

The method of claim 1, wherein determining the mesh structure further includes:
segmenting, by the one or more processors, the aorta image data of a patient using a segmentation model to determine a segmented aorta image data; and
applying, by the one or more processors, the segmented aorta image data into the mesh generator to determine the mesh structure.

### Claim 3 (depends on 1)

The method of claim 1, wherein generating the mesh structure further comprises:
applying, by the one or more processors, a surface smoothing algorithm to the mesh structure; and
applying, by the one or more processors, a down-sampling algorithm to the mesh structure.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
applying, by the one or more processors, a centerline algorithm to the mesh structure to determine an initial mesh centerline;
implementing, by the one or more processors, post-processing algorithms to the initial mesh centerline to determine a mesh centerline; and
projecting, by the one or more processors, data of the mesh centerline into the mesh structure.

### Claim 5 (depends on 4)

The method of claim 4, wherein the applying the centerline algorithm to the mesh structure to determine the initial mesh centerline further comprises:
determining, by the one or more processors, a Voronoi diagram of the mesh structure;
calculating, by the one or more processors, a maximum inscribed sphere for each polyhedron of the Voronoi diagram;
connecting, by the one or more processors, radii of the maximum inscribed spheres; and
identifying, by the one or more processors, the initial mesh centerline by using a shortest path algorithm to identify a shortest path through the radii of the maximum inscribed spheres from one extremal point to another extremal point.

### Claim 6 (depends on 1)

The method of claim 1, wherein the intrinsic coordinate system comprises of a distance to a closest centerline point (r), an angle normal to the closest centerline point (a), and a longitudinal position along the closest centerline point (h).

### Claim 7 (depends on 1)

The method of claim 1, wherein the reference coordinate system is a Cartesian coordinate system.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
determining, by the one or more processors, a first ratio score using the aorta image data of the patient.

### Claim 9 (depends on 8)

The method of claim 8, wherein determining the first ratio score further comprises:
determining, by the one or more processors, a diameter of a mid-ascending aorta of the aorta image data of a patient and a diameter of sinuses of the aorta image data of a patient; and
calculating, by the one or more processors, the first ratio score based on the diameter of a mid-ascending aorta and the diameter of sinuses.

### Claim 10 (depends on 9)

The method of claim 9, further comprising:
classifying, by the one or more processors, the aortic condition of the patient of the patient based on the similarity score and/or the first ratio score.

### Claim 11 (depends on 9)

The method of claim 9, further comprising:
determining, by the one or more processors, a second ratio score using the aorta image data, wherein the second ratio score is determined based on the diameter of a mid-ascending aorta and a body surface area of the patient; and
classifying, by the one or more processors, the aortic condition of the patient based on the similarity score, the first ratio score, and/or the second ratio score.

### Claim 12 (depends on 1)

The method of claim 1, further comprising:
scaling, by the one or more processors, the similarity score to be in a predefined range of numbers; and
classifying, by the one or more processors, the aortic condition of the patient based on the scaled similarity score.

### Claim 13

A computer system for classifying an aortic condition of a patient from aorta image data of a patient comprising:
one or more processors; and
a non-transitory program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, causes the computer system to:
apply the aorta image data to a mesh generator to generate a mesh structure of an aorta of the patient;
determine an intrinsic coordinate system for the mesh structure and determining a plurality of vertices corresponding to the intrinsic coordinate system;
map the mesh structure to a reference coordinate system and generate, from the plurality of vertices corresponding to the intrinsic coordinate system, a plurality of mapped vertices corresponding to the reference coordinate system;
embed the plurality of mapped vertices into the mesh structure to create an embedded mesh structure;
compare the embedded mesh structure to a plurality of reference mesh structures comprising at least one normal aortic mesh structure and at least one pathology aortic mesh structure, where each of the plurality of reference mesh structures correspond to the reference coordinate system, and generate a similarity score indicating a similarity of the embedded mesh structure to at least one of the plurality of reference mesh structures; and
based on the similarity score, classify the aortic condition of the patient.

### Claim 14 (depends on 13)

The computer system of claim 13, wherein the instructions, when executed by the one or more processors, further cause the computer system to:
segment the aorta image data of a patient using a segmentation model to determine a segmented aorta image data; and
apply the segmented aorta image data into the mesh generator to determine the mesh structure.

### Claim 15 (depends on 13)

The computer system of claim 13, wherein the instructions, when executed by the one or more processors, further cause the computer system to:
apply a surface smoothing algorithm to the mesh structure; and
apply a down-sampling algorithm to the mesh structure.

### Claim 16 (depends on 13)

The computer system of claim 13, wherein the instructions, when executed by the one or more processors, further cause the computer system to:
applying a centerline algorithm to the mesh structure to determine an initial mesh centerline;
implement post-processing algorithms to the initial mesh centerline to determine a mesh centerline; and
projecting data of the mesh centerline into the mesh structure.

### Claim 17 (depends on 13)

The computer system of claim 13, wherein the instructions, when executed by the one or more processors, further cause the computer system to:
determine a first ratio score using the aorta image data of the patient.

### Claim 18 (depends on 17)

The computer system of claim 17, wherein the instructions, when executed by the one or more processors, further cause the computer system to:
determine a diameter of a mid-ascending aorta of the aorta image data of a patient and a diameter of sinuses of the aorta image data of a patient; and
calculate the first ratio score based on the diameter of a mid-ascending aorta and the diameter of sinuses.

### Claim 19 (depends on 18)

The computer system of claim 18, wherein the instructions, when executed by the one or more processors, further cause the computer system to:
classify the aortic condition of the patient of the patient based on the similarity score and/or the first ratio score.

### Claim 20 (depends on 18)

The computer system of claim 18, wherein the instructions, when executed by the one or more processors, further cause the computer system to:
determine a second ratio score using the aorta image data, wherein the second ratio score is determined based on the diameter of a mid-ascending aorta and a body surface area of the patient; and
classify the aortic condition of the patient based on the similarity score, the first ratio score, and/or the second ratio score.