Autonomous 3D Vessel Reconstruction in CT Imaging

Solution Overview

Problem

Existing methods for reconstructing blood vessels in CT images are time-consuming and error-prone, particularly in generating reference models for comparative computations of blood flow, as they require manual input and lack autonomous reconstruction capabilities.

Innovation Solution

A computer-implemented method using a pre-trained reconstruction convolutional neural network to autonomously reconstruct 3D models of blood vessels by processing CT images, defining regions of interest, and moving them along a scanning path to combine reconstructed fragments into a complete model, with adjustments based on differences detected between input and reconstructed model fragments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual reconstruction methods are used to generate reference models of blood vessels, then the models can be created for comparative computations, but the process is time-consuming and error-prone

Engineering Contradiction:

Improveaccuracy of reference modelVSAvoidtime to generate reference model

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical reconstruction processes with an automated computer-implemented method. The system automatically identifies vessel segments, generates reference models without stenoses, and performs comparative computations, eliminating manual intervention and significantly reducing time while maintaining or improving accuracy through consistent algorithmic application.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically generating reference models from input CT images without requiring manual operator intervention. The computer-implemented method autonomously processes vessel segments, removes stenoses, and creates reference models, allowing the system to serve itself rather than relying on external manual operations.

Inventive Principle:

Principle #25Self-service

2Extent of automation

If manual reconstruction of blood vessels is performed, then reference models can be generated, but the process requires significant manual input and is error-prone

Engineering Contradiction:

Improveautomation of vessel reconstructionVSAvoidaccuracy of reconstructed model

Core Design Contradiction:

Extent of automationVSReliability

Solution Approach 1:

The patent replaces manual mechanical reconstruction processes with an automated computer-implemented method. The system automatically identifies vessel segments, generates reference models without stenoses, and performs comparative computations, eliminating manual intervention and significantly reducing time while maintaining or improving accuracy through consistent algorithmic application.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the computer automatically compares reconstructed models with original models, identifies discrepancies such as stenoses, and iteratively refines the reference model generation process. This feedback loop ensures high reliability by continuously validating and correcting the automated reconstruction.

Inventive Principle:

Principle #23Feedback

3Productivity

If conventional methods are used to process CT images for vessel reconstruction, then three-dimensional models can be developed, but the process is time-consuming

Engineering Contradiction:

Improvespeed of vessel model generationVSAvoidtime for CT image processing

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical reconstruction processes with an automated computer-implemented method. The system automatically identifies vessel segments, generates reference models without stenoses, and performs comparative computations, eliminating manual intervention and significantly reducing time while maintaining or improving accuracy through consistent algorithmic application.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary actions by pre-processing CT images to identify vessel segments and prepare data structures before the actual reconstruction process. This preliminary organization of data enables the main reconstruction algorithm to operate more efficiently, reducing overall processing time while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12067675B2Autonomous reconstruction of vessels on computed tomography images

Publication Date: 2024.08.20 KARDIOLYTICS INC

AI summary

A computer-implemented method for autonomous reconstruction of vessels on computed tomography images, includes: providing a reconstruction convolutional neural network (CNN); receiving an input 3D model of a vessel to be reconstructed; defining a region of interest (ROI) and a movement step, wherein the ROI is a 3D volume that covers an area to be processed; defining a starting position and positioning the ROI at the starting position; reconstructing a shape of the input 3D model within the ROI by inputting the fragment of the input 3D model within the ROI to the reconstruction convolutional neural network (CNN) and receiving the reconstructed 3D model fragment; moving the ROI by the movement step along a scanning path; repeating the reconstruction and moving steps to reconstruct a desired portion of the input 3D model at consecutive ROI positions; and combining the reconstructed 3D model fragments.