Coronary CT Plaque Analysis With Image Normalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current treatments for cardiovascular diseases, such as stents and bypass surgeries, may not be effective for all patients, particularly those with stable heart disease, and there is a need for more accurate assessment of arterial vessel health to determine the best treatment approach.
Innovation Solution
Utilizing non-invasive medical imaging technologies, including CT scans, and machine learning algorithms to analyze coronary arteries and plaque, with a normalization device to improve image calibration, enabling accurate risk assessment and personalized treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-invasive medical imaging and machine learning algorithms are used to analyze coronary arteries and plaque, then measurement precision of arterial vessel health is improved, but device complexity increases
Solution Approach 1:
A normalization device is introduced as an intermediary component between the medical imaging system and the analysis algorithms. This device includes standardized phantoms or reference objects with known properties that mediate the calibration process, enabling accurate quantitative analysis without requiring complex adjustments to the imaging system itself.
Solution Approach 2:
The system employs parameter changes by adjusting imaging parameters (such as contrast timing, scan protocols, or reconstruction parameters) based on the normalization data. This allows the system to optimize image quality and quantitative accuracy for different clinical scenarios while maintaining a relatively simple hardware configuration.
2Loss of information
If detailed analysis of coronary arteries and plaque is performed using machine learning, then information completeness about disease progression is improved, but loss of time for analysis increases
Solution Approach 1:
The normalization device is prepared and positioned in advance before the actual patient imaging occurs. Reference phantoms with known characteristics are pre-configured, allowing the system to perform rapid quantitative calibration and analysis without time-consuming setup during the clinical workflow. Machine learning models are pre-trained on normalized data to enable fast inference.
Solution Approach 2:
Manual analysis and interpretation of medical images is replaced with automated machine learning algorithms that process the normalized imaging data. This substitution of mechanical/manual processes with computational systems enables comprehensive analysis of plaque characteristics, vessel morphology, and disease progression without proportionally increasing analysis time.
3Manufacturing precision
If normalization device with multiple compartments and samples is used, then manufacturing precision of image calibration is improved, but device complexity increases
Solution Approach 1:
The normalization device is segmented into multiple independent compartments, each containing specific reference samples with known properties (such as different tissue-equivalent materials or contrast concentrations). This segmentation allows each compartment to be manufactured and calibrated independently, ensuring high precision while maintaining modular simplicity that facilitates quality control and manufacturing.
Data Source
AI summary
The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.