mCTA Tissue Fate Prediction for Rapid Stroke Perfusion Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic protocols for ischemic stroke, particularly in diagnosing medium vessel occlusion, are time-consuming, require significant expertise, and lack precision due to the limitations of multi-phase CT-angiography (mCTA) in providing quantitative data on core, penumbra, and perfusion, leading to variability in treatment decisions.
Innovation Solution
A machine learning-based system that interprets mCTA images to predict core, penumbra, and perfusion status by analyzing historical data from CTP and mCTA studies, enabling rapid generation of predictive maps using features like density, time, and location analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multi-phase CT-angiography (mCTA) is used to diagnose ischemic stroke, then diagnostic time is reduced and radiation exposure is minimized, but the ability to provide quantitative data on core, penumbra, and perfusion is insufficient
Solution Approach 1:
A machine learning-based software system acts as an intermediary between mCTA images and clinical decision-making. The software analyzes mCTA images and generates predictive maps of core, penumbra, and perfusion status, bridging the gap between the limited quantitative capability of mCTA and the need for precise perfusion data. This intermediary processing enables quantitative assessment without requiring full CTP protocols.
Solution Approach 2:
The system creates predictive copies of CTP-derived perfusion maps from mCTA images. By training machine learning models on paired mCTA and CTP data, the system learns to generate synthetic perfusion maps that replicate the quantitative information normally obtained only from more time-consuming CTP studies, effectively copying the desired output from a simpler input.
2Measurement precision
If CT perfusion (CTP) study is conducted to obtain quantitative perfusion data, then measurement precision is improved, but diagnostic time and radiation exposure increase
Solution Approach 1:
The system uses mCTA images as a simpler, faster alternative to full CTP studies. Just as disposable items replace expensive durable ones, the machine learning model processes readily available mCTA images to provide perfusion information without requiring the more resource-intensive CTP protocol, achieving similar diagnostic value with less time and radiation.
3Reliability
If traditional diagnostic protocols are used for medium vessel occlusion, then comprehensive assessment is achieved, but expertise requirement and diagnostic complexity increase
Solution Approach 1:
The machine learning system performs self-service by automatically analyzing mCTA images and generating predictive perfusion maps without requiring manual interpretation expertise. The software autonomously identifies core, penumbra, and perfusion status, eliminating the need for highly specialized human expertise while maintaining diagnostic reliability.
4Measurement precision
If more information is gathered before making treatment decisions, then decision accuracy is improved, but time to treatment increases
Solution Approach 1:
The machine learning system performs preliminary analysis of mCTA images to generate predictive perfusion maps before treatment decisions are made. By pre-processing and pre-interpreting the imaging data, the system provides actionable information immediately, eliminating delays associated with manual analysis and enabling faster treatment decisions with maintained accuracy.
Data Source
AI summary
The invention relates to systems and methods for predicting ischemic brain tissue fate from multi-phase CT-angiography. More specifically, systems and methods are provided that enable meaningful prediction of core, penumbra and perfusion from mCTA images using software that has been trained via machine learning to interpret mCTA images.