Wavelet-Based Motion Analysis for Optimal Angiography Frame Selection

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Solution Overview

Problem

Current coronary angiography analysis is time-consuming and prone to errors due to the manual selection of optimal end-systole (ES) and end-diastole (ED) frames, which require minimal motion and maximal visibility of arteries, often relying on ECG synchronization or centered window calculations that are not robust.

Innovation Solution

A method using wavelet-based motion analysis to identify local minima frames with minimal motion, selecting optimal ES and ED frames by calculating wavelet coefficients and differences between sequential frames, thereby automating the selection process without relying on ECG curves or centered windows.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual selection of optimal frames is performed, then diagnostic accuracy can be maintained, but time consumption increases and error susceptibility increases

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically detecting optimal ES and ED frames through wavelet-based motion analysis without requiring manual physician intervention. The algorithm independently analyzes arterial motion patterns, identifies minimal motion frames, and selects optimal candidates for quantitative coronary analysis, thereby reducing time consumption while maintaining diagnostic accuracy through automated intelligent detection

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical selection process by physicians is replaced with an automated computational system using wavelet transform and motion analysis algorithms. This substitution eliminates human error susceptibility and time-consuming manual review while preserving diagnostic accuracy through sophisticated automated detection of arterial motion patterns and minimal motion frame identification

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Extent of automation

If ECG synchronization or centered window calculations are used, then frame selection can be automated, but robustness decreases due to sensitivity to timing and positioning

Engineering Contradiction:

Improveautomation levelVSAvoidrobustness

Core Design Contradiction:

Extent of automationVSReliability

Solution Approach 1:

The approach changes the selection parameter from ECG timing synchronization or centered window positioning to wavelet-based motion analysis. By analyzing actual arterial motion patterns and identifying frames with minimal motion through wavelet coefficients, the system achieves automation that is robust to timing variations and positioning differences, as it directly measures motion rather than relying on synchronized timing or fixed window calculations

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Wavelet-based motion analysis serves as an intermediary that bridges the gap between automated selection and robust performance. Instead of directly using ECG timing or fixed window positions, the system introduces motion analysis as an intermediate step that objectively identifies minimal motion frames based on actual arterial movement patterns, making the automation robust to variations in timing and positioning

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If automated detection methods are implemented, then time efficiency improves, but measurement precision may deteriorate due to algorithmic limitations

Engineering Contradiction:

Improvetime efficiencyVSAvoidselection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The detection process is segmented into multiple precise steps: wavelet transform decomposition, motion magnitude calculation, minimal motion frame identification, and optimal candidate selection. This segmentation allows each step to be optimized for precision while maintaining overall time efficiency, as the systematic breakdown enables accurate measurement of arterial motion patterns without requiring exhaustive manual review of all frames

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The automated wavelet-based detection system replaces manual frame review with sophisticated signal processing algorithms that precisely quantify arterial motion. The substitution maintains measurement precision by using mathematical transformations to objectively identify minimal motion frames, eliminating human error while preserving diagnostic accuracy through rigorous computational analysis of motion patterns

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS8605976B2System and method of detection of optimal angiography frames for quantitative coronary analysis using wavelet-based motion analysis

Publication Date: 2013.12.10 GENERAL ELECTRIC CO

AI summary

A novel and useful system and method of detecting the best candidate frames for diagnosis out of a series of coronary angiograms frames. The mechanism analyzes the motion of the visible arteries in the sequence of angiogram frames to identify the local minimas of the differences between sequential frames. Wavelet transform coefficients are generated for each frame and used to quantify the differences between every two sequential frames to detect minimal differences (which correspond to minimal motion). The optimal end-systole and end-diastole frames are selected from these local minima frames.