Medical Imaging Sensor Navigation with Contrastive Reinforcement Learning
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Solution Overview
Problem
Existing medical imaging technologies, such as ultrasound, require skilled operators for proper navigation and positioning, and there is a shortage of trained sonographers, while existing AI navigation systems lack generalizability and are not suitable for interventional cases.
Innovation Solution
A contrastive reinforcement learning (CRL) framework is used to train an AI system for medical sensor navigation, utilizing simulated trajectories from different patients to enhance generalization and enable navigation to various anatomical landmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ultrasound scanning is performed by skilled operators, then scan quality is maintained, but there is a shortage of skilled sonographers to meet increasing demand
Solution Approach 1:
The system enables self-service navigation where the ultrasound system automatically guides the transducer to target anatomical views using AI-based image recognition and navigation algorithms, eliminating the need for continuous manual operator intervention while maintaining scan quality
Solution Approach 2:
The patent replaces the mechanical skill-based operation with an automated computer vision system that uses machine learning models to interpret ultrasound images and guide transducer positioning, substituting human operator expertise with algorithmic decision-making
2Measurement precision
If AI navigation systems are trained on single-view data, then navigation to that specific view is accurate, but the system lacks generalizability for interventional cases requiring multiple views
Solution Approach 1:
The system implements a universal navigation framework that can handle multiple anatomical views and interventional scenarios simultaneously through a single trained model, enabling the AI to generalize across different clinical cases rather than requiring separate specialized models for each view
Solution Approach 2:
The patent extends navigation from 2D single-view imaging to 3D multi-view spatial understanding by training the AI model on volumetric data and teaching it to navigate through three-dimensional anatomical space, enabling comprehensive interventional guidance
3Measurement precision
If navigation data is collected from actual clinical procedures, then training data quality is high, but the process is time-consuming and may require additional tracking hardware
Solution Approach 1:
The system creates synthetic copies of clinical navigation data through simulation environments that generate virtual ultrasound images and transducer trajectories, providing abundant training data without requiring actual clinical procedure recording or additional hardware
Solution Approach 2:
The patent performs preliminary data preparation by pre-processing and simulating navigation scenarios before actual clinical use, creating a comprehensive training dataset in advance that eliminates the need for time-consuming real-time data collection during procedures
Data Source
AI summary
For movement of medical sensors for medical imaging, an artificial intelligence (AI) is trained using a contrastive reinforcement learning (CRL) framework. Simulation may be used to provide the training data. For training, the sampling for a given input instance in CRL may use trajectories simulated from different patients for better contrast. CRL, with or without the simulation feature and/or the sampling feature, may provide more generalized navigation, such as in interventional or diagnostic settings.