Coronary Angiogram Embedding Analysis for Motion-Consistent Imaging
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Solution Overview
Problem
Conventional coronary angiography image analysis techniques are hindered by varying contrast levels and patient motion, leading to reduced performance and inconsistent results due to cardiac and breathing motion, as well as patient and table movement during the procedure.
Innovation Solution
Utilizing AI-based methods and systems that encode medical images into embeddings using machine learning networks, such as CNNs and transformers, to perform medical imaging analysis tasks with improved temporal consistency, enabling accurate determination of corresponding images and maintaining temporal relationships across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional coronary angiography image analysis techniques are used, then the analysis process is simple, but the performance is reduced due to varying contrast levels and motion artifacts
Solution Approach 1:
The patent introduces an intermediary system comprising a contrast level normalization module and a motion artifact reduction module that mediate between the input coronary angiography images and the analysis algorithm. The normalization module adjusts contrast levels across temporal frames using histogram matching or adaptive thresholding, while the motion artifact reduction module applies registration techniques to align vessels across frames, thereby improving analysis reliability without requiring fundamental changes to the analysis approach
Solution Approach 2:
The patent applies preliminary processing actions to the coronary angiography images before analysis. Specifically, contrast normalization is performed beforehand to equalize vessel visibility across frames, and motion compensation is applied in advance through image registration techniques. This preliminary action ensures that the subsequent analysis operates on pre-corrected data, improving reliability while keeping the overall system manageable
2Stability of the object's composition
If conventional image analysis techniques are used, then the processing speed is fast, but the consistency of results is reduced due to temporal variations in contrast and motion
Solution Approach 1:
The patent segments the image processing task into distinct functional modules: a contrast normalization module that processes each frame independently to adjust contrast levels, and a motion compensation module that performs frame-to-frame registration. This segmentation allows each module to be optimized separately and enables parallel processing of multiple frames, improving temporal consistency while managing processing time through modular efficiency
Solution Approach 2:
The patent applies partial action by selectively processing only the aspects of images that contribute to temporal consistency. Rather than fully reprocessing all image characteristics, the system focuses specifically on contrast normalization and motion alignment, applying sufficient correction to achieve consistency without excessive processing that would unnecessarily extend analysis time
3Illumination intensity
If contrast agent propagation is used to visualize vessels, then vessel visualization is achieved, but contrast levels vary across temporal frames reducing analysis performance
Solution Approach 1:
The patent changes the contrast parameter across temporal frames through normalization processing. The contrast normalization module analyzes the distribution of pixel intensities in each frame and applies transformation functions to equalize contrast levels. This may involve adjusting histogram distributions, applying adaptive thresholding, or using reference frame matching to ensure consistent vessel visibility across all temporal frames, thereby improving analysis reliability
Solution Approach 2:
The patent creates equipotentiality in contrast levels across all temporal frames by normalizing the contrast distribution to a reference standard. Each frame's contrast is adjusted to match a target contrast profile derived from reference frames or statistical models, ensuring that all frames operate at equivalent contrast levels. This eliminates the variability caused by contrast agent propagation dynamics while preserving the temporal information needed for analysis
4Area of stationary object
If patient motion is accommodated during imaging, then complete vessel coverage is achieved, but overlapping branches and motion artifacts reduce analysis performance
Solution Approach 1:
The patent applies preliminary motion compensation through image registration before analysis. The system estimates motion vectors from reference frames or tracking markers and applies geometric transformations to align vessels across frames. This preliminary alignment prevents overlapping branches caused by cardiac and respiratory motion, as well as patient and table movement, thereby improving analysis reliability while maintaining complete vessel coverage in the field of view
Solution Approach 2:
The patent introduces a motion compensation module as an intermediary between image acquisition and analysis. This module performs frame-to-frame registration using feature-based or intensity-based alignment algorithms, correcting for motion artifacts before the images are subjected to analysis. The intermediary processing separates the motion correction function from the analysis function, allowing each to be optimized independently while improving overall reliability
Data Source
AI summary
Systems and methods for performing a medical imaging analysis task are provided. One or more input medical images of a patient are received. The one or more input medical images are encoded into embeddings using a machine learning based encoder network. A medical imaging analysis task is performed based on the embeddings. Results of the medical imaging analysis task are output.