ML-Based AEC Sensor Selection for Safer X-Ray Exposure
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Solution Overview
Problem
Existing X-ray imaging systems often exceed safe radiation dosage limits, posing health risks to patients and medical personnel, despite the presence of safety measures like Automatic Exposure Control (AEC) devices, due to user error in selecting and activating AEC sensors.
Innovation Solution
A system utilizing a trained machine learning model to select and activate appropriate AEC sensors based on input data, including scout camera imagery and medical protocols, to ensure safe and efficient X-ray exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection of AEC sensors is used, then device complexity is reduced, but radiation safety reliability deteriorates due to user error
Solution Approach 1:
The system automatically selects and activates appropriate AEC sensors based on scout camera imagery and medical protocols without requiring manual user input. The machine learning model processes the input data and autonomously determines the optimal sensor configuration, eliminating human error in the selection process while maintaining safety reliability.
Solution Approach 2:
The patent replaces the manual mechanical selection process with an automated machine learning-based system. The selector module uses trained ML models to process scout imagery and protocol data, substituting human decision-making with an automated computational system that determines AEC sensor activation based on image analysis and predefined safety criteria.
2Reliability
If automated AEC sensor selection is implemented, then radiation safety reliability improves, but device complexity increases due to ML model integration
Solution Approach 1:
The machine learning model serves multiple functions within the system: it analyzes scout camera imagery, determines optimal AEC sensor selection, and ensures radiation safety compliance. This multi-functionality justifies the added complexity by consolidating multiple tasks into a single integrated component that improves overall system reliability.
3Object-affected harmful factors
If manual AEC activation is used, then ease of operation is maintained, but harmful radiation exposure increases due to improper sensor selection
Solution Approach 1:
The system uses scout camera imagery as feedback to automatically determine the appropriate AEC sensor configuration. The ML model processes this visual feedback along with medical protocol data to make informed decisions about sensor activation, ensuring that radiation exposure is minimized while maintaining ease of operation through automated adjustment based on real-time image analysis.
Data Source
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Figure 3A~3C
AI summary
System (FS) and related method of facilitating operation of a medical X-ray imaging apparatus having plural exposure control devices (SN, ECD1-5). The system comprises an input interface (IN) through which is receivable input data (m) pertaining to an acquisition operation to be performed in relation to a region of interest (ROI). A selector module (SM) is capable to provide a selection (s), based on the input data, of one or more of the plural exposure control devices (SN, ECD) for activation during the acquisition operation. The selector module (SM) is based on a trained machine learning model (M).