Medical Image Contrast Overlay via Reference Loop Matching
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Solution Overview
Problem
In medical imaging, especially in vivo imaging of patients and small animals, the comparison of post-contrast and pre-contrast images is challenging due to motion caused by respiration and cardiac activity, leading to difficulties in identifying and quantifying tissue contrast enhancement accurately.
Innovation Solution
A method is developed to create an image difference overlay by identifying sets of reference and data images, comparing them to generate a contrast overlay, which involves associating images that closely resemble each other and processing them to highlight intensity differences, thereby reducing the impact of motion artifacts and improving accuracy without the need for complex gating techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contrast agents are used to increase signal intensity difference between tissues, then measurement precision is improved, but device complexity increases due to need for multiple image sets and processing
Solution Approach 1:
The patent segments the image processing into distinct modules: reference image acquisition module, contrast agent administration timing module, image comparison module, and overlay generation module. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining high measurement precision for contrast enhancement identification.
Solution Approach 2:
The system performs preliminary actions by acquiring multiple reference images before contrast agent administration and storing them in a reference image set. This preliminary preparation allows the system to automatically match and compare images without requiring complex real-time processing during contrast agent circulation, thereby reducing device complexity while improving measurement precision.
2Reliability
If multiple reference images are compared to data images, then reliability is improved through better motion compensation, but ease of operation deteriorates due to increased processing time
Solution Approach 1:
The system performs self-service by automatically comparing reference images with contrast-enhanced images and generating the overlay display without requiring manual intervention. The computer automatically identifies matching images, performs subtraction, and creates the final overlay, eliminating the need for operator involvement in the complex comparison process while maintaining high reliability.
Solution Approach 2:
The patent changes the parameter of image comparison from single-image to multi-image analysis by introducing a reference image set containing multiple frames. This parameter change allows the system to automatically compensate for motion artifacts through image matching algorithms, improving reliability while reducing operator effort as the system handles the complexity automatically.
3Measurement precision
If image subtraction is performed to highlight contrast enhancement, then measurement precision is improved, but loss of information increases due to motion artifacts and positioning variations
Solution Approach 1:
The patent extracts and removes motion artifacts and positioning variations from the image comparison process by using a reference image set that captures the baseline anatomy before contrast administration. The system extracts only the relevant contrast enhancement information by comparing current images against the reference set, thereby improving measurement precision while minimizing information loss from motion-related noise.
Solution Approach 2:
The reference image set acts as an intermediary that mediates between the raw contrast-enhanced images and the final overlay output. By using the reference images as an intermediate comparison standard, the system can isolate true contrast enhancement from motion artifacts, improving measurement precision while reducing information loss from positioning variations.
Data Source
AI summary
A method of creating an image difference overlay comprises identifying a loop of reference images of a subject and identifying a loop of data images of the subject. The loop of image data can be identified after an event, such as the administration of contrast agent to the subject. A reference loop image frame is compared to one or more data loop image frames and the reference loop frame is associated with a data loop image frame which closely resembles the data loop image frame. Each of the associated frames can then be processed and used to create an image difference overlay frame.