Dual Energy CT Bleeding Detection via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current CT scanning technologies face challenges in accurately detecting and quantifying traumatic bleeding, especially when bleeding sources are not apparent on the surface, as they struggle to differentiate bleeding from surrounding structures with similar intensity values, leading to complex data analysis and delayed diagnosis.
Innovation Solution
A dual energy CT scan system that uses machine-learned networks to generate a bleeding probability map, allowing for the automatic detection and quantification of bleeding areas by differentiating materials based on their unique attenuation profiles at different energy levels, and providing a visualization and risk assessment to operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dual energy CT scan is used to differentiate bleeding from surrounding structures, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex dual energy CT data processing into distinct functional modules: image acquisition at different energy levels, material decomposition algorithm, bleeding detection module, and quantification module. This segmentation allows the complex system to be managed through modular components, each handling a specific aspect of the detection process.
Solution Approach 2:
The patent introduces material decomposition as an intermediary process that transforms raw dual energy CT data into material-specific maps. This intermediary step separates the complex mixed-signal CT data into distinct material components (blood, bone, soft tissue, contrast agent), making the subsequent bleeding detection more precise while managing system complexity through mathematical transformation rather than hardware complexity.
2Productivity
If machine-learned networks are used for automatic bleeding detection, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with extensive medical imaging data before deployment. The neural networks are pre-trained to recognize bleeding patterns, allowing them to automatically detect and quantify bleeding in new patient scans without requiring real-time expert intervention. This preliminary training phase enables rapid automated diagnosis while managing complexity through transfer learning from pre-trained models.
Solution Approach 2:
The system implements self-service through automated bleeding detection and quantification using machine learning algorithms. The neural networks automatically analyze the dual energy CT images, generate bleeding probability maps, calculate bleeding volumes, and provide diagnostic recommendations without requiring manual image analysis by radiologists. This automation significantly improves productivity while the modular AI architecture manages computational complexity.
3Reliability
If whole body scanning is performed to detect bleeding anywhere in the patient, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent implements continuous useful action through rapid dual energy CT scanning that captures the entire patient body in a single continuous scan. The system acquires images at both high and low energy levels simultaneously or in rapid succession, maintaining continuous data acquisition throughout the whole body. This continuous scanning approach ensures comprehensive bleeding detection across all potential sites while minimizing total scan time through efficient data collection.
Solution Approach 2:
The system uses periodic action by rapidly alternating between high energy and low energy X-ray acquisitions during the scan. The dual energy CT scanner performs periodic switching between different energy levels, capturing complementary information in rapid succession. This periodic acquisition pattern enables comprehensive material decomposition and bleeding detection throughout the entire body within a short time frame, maintaining both reliability and speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate and efficient identification and grading of bleeding areas, reducing diagnostic complexity and improving timely intervention in traumatic bleeding cases.
Implementation Method 1
differentiating materials based on their unique attenuation profiles at different energy levels
Data Source
AI summary
Systems and methods are provided for automatic detection and quantification for traumatic bleeding. Image data is acquired using a full body dual energy CT scanner. A machine-learned network detects one or more bleeding areas on a bleeding map from the dual energy CT scan image data. A visualization is generated from the bleeding map. The predicted bleeding areas are quantified, and a risk value is generated. The visualization and risk value are presented to an operator.