Automated ED ES Image Identification in Cardiac Angiography
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Solution Overview
Problem
Manual selection of end-diastolic (ED) and end-systolic (ES) images in left ventricular angiography is burdensome, time-consuming, and prone to human error due to low contrast and noisy backgrounds in X-ray images, making automated identification challenging.
Innovation Solution
A system integrates heart cycle information from ECG data, multiple images over a heart cycle, and contrast agent flow timing data to automatically identify and retrieve ED and ES images using a Hidden Markov Model, histogram-based and scene-based observation likelihood models, and ECG signal analysis for robust image selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated ED and ES image identification is implemented, then user burden is reduced and analysis accuracy increases, but the system becomes difficult to implement due to low contrast and noisy background in X-ray images
Solution Approach 1:
The patent segments the complex image analysis task into multiple processing stages: preprocessing (noise reduction, contrast enhancement), feature extraction (boundary detection, shape analysis), and automated identification (ED/ES frame selection). This segmentation makes the automated system feasible by breaking down the difficult overall task into manageable sub-tasks that can be handled by specific algorithms.
Solution Approach 2:
The patent introduces intermediate processing steps including noise filtering algorithms, contrast enhancement techniques, and feature extraction modules that act as intermediaries between the raw noisy images and the final automated identification. These intermediaries prepare the data and extract meaningful features, making automated analysis possible despite the poor quality of original images.
2Measurement precision
If manual selection of ED and ES images is performed, then accurate image selection can be achieved, but the process becomes burdensome, time consuming and vulnerable to human error
Solution Approach 1:
The patent implements self-service automation where the system performs ED and ES image identification automatically without requiring manual user intervention. The automated algorithms analyze the image sequences, identify key frames based on ventricular volume changes, and retrieve the appropriate images, thereby eliminating time-consuming manual selection while maintaining accuracy through sophisticated image processing.
Solution Approach 2:
The patent changes the approach from manual visual assessment to automated parameter-based analysis. It uses quantitative parameters such as ventricular volume calculations, contrast agent distribution patterns, and temporal dynamics to objectively identify ED and ES frames, replacing subjective manual judgment with precise computational measurements.
3Productivity
If automated image identification is attempted, then time consumption is reduced, but the system fails to achieve accurate identification due to low contrast and noisy background
Solution Approach 1:
The patent applies preliminary actions in the form of preprocessing operations including noise reduction filtering, contrast enhancement, and image normalization before the main automated identification process. These preliminary steps improve the quality of input data, enabling subsequent automated algorithms to achieve accurate identification despite the inherently poor quality of raw X-ray images.
Solution Approach 2:
The patent employs composite processing techniques that combine multiple image processing methods and algorithms working together. It integrates various noise reduction techniques, contrast enhancement methods, and feature extraction algorithms into a unified automated system, where the combined effect of multiple processing components achieves accurate identification that individual methods could not accomplish alone.
Data Source
AI summary
A system identifies a particular image associated with a particular cardiac characteristic from within a sequence of cardiac images including image noise artifacts and obtained over a heart beat cycle. The system comprises at least one repository including, first data comprising heart cycle information derived from ECG data, second data comprising data representing multiple images acquired over at least one heart cycle and third data comprising data associated with timing of contrast agent flow. An image data processor identifies a particular image exhibiting a particular cardiac characteristic from within a sequence of cardiac images by processing the first, second and third data to identify an image having a substantially maximum likelihood of exhibiting the particular cardiac characteristic. A storage processor retrieves data representing the particular image from storage.