Automated M-Mode Cardiac Analysis via Tissue Layer Alignment
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Solution Overview
Problem
Current echocardiography techniques rely heavily on manual skills for analyzing M-Mode images, which are prone to human error and require extensive training due to low-quality ultrasound images with fuzzy edges and noise, lacking automated analysis methods for efficient measurement and comparison of cardiac functions.
Innovation Solution
An automated method for analyzing M-Mode images using dynamic time warping to align tissue layers and separate motion curves, enabling the extraction of cardiac characteristics like ejection fraction and ventricle wall thickness, and comparing images for similarity, allowing for efficient indexing and search within electronic medical records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual positioning of sensor and calipers is used for M-Mode analysis, then measurement capability is achieved, but analysis accuracy is reduced due to human error and image quality limitations
Solution Approach 1:
The patent replaces manual mechanical positioning of sensor and calipers with automated computer vision algorithms. The system automatically detects tissue layers, identifies landmarks, and positions measurement tools using image processing techniques, eliminating human error in positioning while maintaining measurement capability.
Solution Approach 2:
The patent creates a digital representation of the M-Mode image with annotated tissue layers and landmarks. This copied and enhanced version allows for precise automated measurements without being constrained by the limitations of the original low-quality ultrasound image.
2Adaptability or versatility
If extensive manual training is provided for echocardiography interpretation, then diagnostic capability is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-analysis of M-Mode images by automatically detecting tissue layers, identifying cardiac landmarks, and extracting measurements without requiring expert human intervention. The automated algorithms serve the diagnostic function that previously required extensive human training.
Solution Approach 2:
The patent introduces an intermediate automated analysis layer between the raw M-Mode image and the final diagnostic interpretation. This intermediary system pre-processes images, identifies features, and prepares measurements, reducing the burden on trained professionals while maintaining diagnostic accuracy.
3Productivity
If automated analysis methods are implemented, then productivity is improved, but measurement precision may be reduced without accurate tissue layer alignment
Solution Approach 1:
The patent segments the M-Mode image into distinct tissue layers by detecting edges and intensity transitions. This segmentation allows the automated system to accurately identify boundaries between different cardiac structures, ensuring precise measurements while maintaining high processing efficiency.
Solution Approach 2:
The system dynamically adjusts its analysis parameters and alignment transformations based on the specific characteristics of each M-Mode image. By adapting to varying image qualities and anatomical presentations, the automated method maintains measurement precision across different cases while preserving productivity benefits.
Data Source
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AI summary
Automated analysis of M-Mode images is provided based on the separation of M-Mode images into tissue layers and motion curves (702, 704 ) by simultaneously aligning all layers and extracting the motion curves (702, 704) from the alignment. Also provided is the ability to search for similar M-Modes using a representation comprised of tissue layers and motion curves (702, 704) and a similarity measure thereof.