Deep Learning MRI Collateral Assessment for Acute Stroke Triage
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Solution Overview
Problem
Existing methods for assessing collateral circulation in acute ischemic stroke patients using brain MRI are labor-intensive and require expert interpretation, limiting timely and accurate assessment in emergency situations.
Innovation Solution
A method and analysis device using dynamic perfusion MRI images and a deep learning model to automatically assess collateral circulation by processing arterial, capillary, and venous phase images, enabling automated prediction of collateral circulation probability values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert interpretation is used for collateral circulation assessment, then assessment accuracy is improved, but assessment time and labor requirements increase
Solution Approach 1:
A deep learning model is introduced as an intermediary between the MRI images and the final assessment result. The model automatically extracts features from arterial, capillary, and venous phase images to predict collateral circulation probability, replacing the need for expert interpretation while maintaining assessment accuracy
Solution Approach 2:
The system enables self-service assessment by allowing the collateral circulation evaluation to be performed automatically through the deep learning model without requiring expert intervention. The model processes the dynamic perfusion MRI images and generates assessment results independently
2Measurement precision
If expert interpretation is used for collateral circulation assessment, then assessment accuracy is improved, but operational complexity increases
Solution Approach 1:
The deep learning model performs the assessment automatically without requiring expert operation. The system accepts dynamic perfusion MRI images as input and generates collateral circulation assessment results autonomously, making the process easy to operate while maintaining high accuracy
Solution Approach 2:
The manual expert interpretation process is replaced with an automated deep learning-based computational system. The model processes images and generates assessments algorithmically, eliminating the need for manual expert analysis and simplifying the operational workflow
3Productivity
If automated deep learning assessment is implemented, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The deep learning model is pre-trained on a large dataset of perfusion MRI images with expert-assessed labels. This preliminary training enables the model to learn accurate feature representations and prediction patterns, ensuring high measurement precision when deployed for automated assessment
Solution Approach 2:
The model processes multiple phases (arterial, capillary, and venous) of the dynamic perfusion MRI sequence, using more information than a single-phase analysis would provide. This excessive processing of multiple image phases enhances the accuracy of the automated assessment while maintaining high productivity
Data Source
AI summary
A method for collateral circulation assessment, using dynamic MRI images, includes the steps of: receiving dynamic perfusion MRI images of a subject by an analysis device; extracting arterial phase images, capillary phase images, and venous phase images from the dynamic perfusion MRI images by the analysis device; inputting the arterial phase images, capillary phase images, and venous phase images into pre-trained, deep learning models, respectively, to calculate a plurality of collateral circulation prediction probability values by the analysis device; and conducting a final collateral circulation assessment for the subject, using the plurality of collateral circulation prediction probability values by the analysis device.