X-ray Exposure Control via Machine Learning and Spatial Sensors
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Solution Overview
Problem
Existing radiographic imaging systems face challenges in manually adjusting X-ray exposure settings to achieve optimal image quality, leading to over- or underexposed images, which can result in diagnostic challenges and the need for retakes.
Innovation Solution
A system utilizing a machine learning model to predict and control X-ray exposure settings based on spatial sensor data and current exposure settings, ensuring optimal exposure for individual patients and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual exposure adjustment is used in free X-ray examinations, then user flexibility is maintained, but image quality consistency deteriorates due to human error and environmental variations
Solution Approach 1:
The patent introduces an intermediary system consisting of sensors, machine learning models, and automated exposure control algorithms that mediate between the user's intent and the actual X-ray exposure parameters. This intermediary layer analyzes spatial sensor data, patient anatomy, and environmental conditions to automatically determine optimal exposure settings, thereby maintaining user flexibility while ensuring consistent image quality.
Solution Approach 2:
The patent replaces the manual mechanical adjustment of exposure parameters with an automated electronic control system. Machine learning models and algorithms automatically calculate and adjust exposure settings based on sensor data and environmental conditions, eliminating the need for manual intervention while maintaining operational flexibility through programmable parameters and adaptive control.
2Manufacturing precision
If fixed automatic exposure control is used, then exposure consistency is improved, but adaptability to individual patients and environmental conditions deteriorates
Solution Approach 1:
The patent implements a dynamic exposure control system that continuously adapts to changing conditions. Sensors capture real-time spatial data, and machine learning models dynamically adjust exposure parameters based on patient anatomy, environmental lighting, and equipment positioning. This dynamic approach maintains exposure consistency while adapting to individual patients and varying environmental conditions.
Solution Approach 2:
The patent utilizes parameter changes in the machine learning models and control algorithms to adapt exposure settings. By modifying input parameters (spatial sensor data, patient characteristics, environmental conditions) and adjusting output parameters (exposure time, intensity, positioning), the system maintains consistency while achieving versatility across different patients and situations.
3Adaptability or versatility
If manual exposure adjustment is used, then adaptability to environmental conditions is maintained, but exposure accuracy deteriorates leading to over- or underexposed images
Solution Approach 1:
The patent implements a feedback mechanism where sensors continuously monitor environmental conditions, patient positioning, and exposure parameters. Machine learning models analyze this feedback data in real-time and automatically adjust exposure settings to maintain accuracy. This closed-loop control system preserves adaptability to environmental conditions while ensuring precise exposure delivery.
Solution Approach 2:
The patent replaces manual exposure adjustment with an automated electronic control system that uses algorithms to calculate precise exposure parameters. This substitution eliminates human error in judgment and calculation, maintaining adaptability through programmable logic while achieving superior exposure accuracy through computational precision.
Data Source
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AI summary
The invention concerns a system for controlling an exposure of a patient generated from an X-ray device with a machine learning model, the system comprising one or more processors configured to: receive spatial sensor data from an image device, receive one or more exposure parameter from the X-ray device, provide the spatial sensor data and the exposure setting to the machine learning model, and obtain a prediction of the optimal exposure setting for the given X-ray imaging circumstances from the machine learning model based on an analysis of the received spatial sensor data. The invention further concern an X-ray system comprising the above system for controlling the exposure and a method for controlling the exposure of a patient generated by an X-ray device.