CT Bleeding Detection via Dual-Phase Image Subtraction
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Solution Overview
Problem
Current medical imaging technologies, such as CT scans, require clinicians to manually analyze multiple images to detect internal bleeding, which is time-consuming and challenging, especially in emergency situations, and often results in incorrect interpretations due to the need for extensive training and experience.
Innovation Solution
An image processing system that generates a feature map by analyzing dual-phase contrast-enhanced CT images using a feature-based interest-points detector and descriptor analysis, identifying leakage occurrences through a difference image formed by subtracting earlier and later images, and visualizing these locations for quick clinical diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of multiple CT phase images is performed by clinicians, then detection accuracy may be maintained through expert interpretation, but time consumption increases significantly and diagnostic efficiency decreases
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated computer-based image processing system. The system uses digital image subtraction algorithms to automatically compare contrast-enhanced CT images taken at different time points, eliminating the need for manual visual inspection while maintaining detection accuracy through objective computational methods.
Solution Approach 2:
The image processing system performs self-analysis by automatically detecting and highlighting regions of active extravasation without requiring continuous human intervention. The automated algorithm independently processes the CT images, generates difference images, and produces visual outputs that guide clinicians, making the system self-sufficient in the detection task.
2Reliability
If dual phase CT protocol is used to detect extravasation, then detection capability is improved, but image interpretation difficulty increases due to the need to simultaneously analyze multiple phases
Solution Approach 1:
The patent extracts the critical diagnostic information from the complex dual-phase CT data by performing digital subtraction. The system isolates regions where contrast material has actively extravasated by removing background structures and non-active enhancements, leaving only the pathologically significant changes that indicate active bleeding.
Solution Approach 2:
The system uses visual enhancement techniques including color coding and intensity mapping to highlight regions of active extravasation in the difference images. This transforms the subtle grayscale variations in original CT images into visually distinct colored regions that are easily distinguishable, reducing interpretation difficulty while maintaining high detection reliability.
3Extent of automation
If feature-based interest-points detector and descriptor analysis is used, then automation extent increases, but device complexity increases due to advanced image processing algorithms
Solution Approach 1:
The patent segments the image processing task into distinct modular stages: initial image registration, difference image generation, feature detection using interest-point algorithms, descriptor calculation, and result visualization. Each module handles a specific aspect of the analysis, making the overall complex system manageable through functional decomposition and independent optimization of each component.
Data Source
AI summary
An image processing system (IPS) and related method. The system comprises an input interface (IN) for receiving an earlier input image (Va) and a later input image (Vb) acquired of an object (OB) whilst a fluid is present within the object (OB). A differentiator (Δ) is configured to form a difference image (Vd) from the at least two input images (Va,Vb). An image structure identifier (ID) is operable to identify one or more locations in the difference image. Based on a respective feature descriptor that describes a respective neighborhood around said one or more locations. An output interface (OUT) outputs a feature map (Vm) that includes the one or more locations.