C-Arm X-Ray Mode Selection Using Angulation-Aware Machine Learning
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Solution Overview
Problem
Existing X-ray imaging systems often operate in suboptimal modes due to users selecting generic parameter lists that compromise between different use cases, leading to reduced performance and image quality.
Innovation Solution
A machine learning model is trained to classify operation modes based on the angulation state of the C-arm and X-ray images, automatically selecting the most suitable settings for the X-ray imaging system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a generic organ program is selected to be applicable for various use cases, then the ease of operation is improved, but the manufacturing precision deteriorates
Solution Approach 1:
The system automatically determines the appropriate operation mode by analyzing the X-ray images and angulation data without requiring manual user selection. The machine learning model classifies the imaging task and selects optimal parameters autonomously, making the system self-configuring and eliminating the need for user expertise in selecting the correct organ program.
Solution Approach 2:
The system dynamically changes imaging parameters based on the classified imaging task. The machine learning model outputs specific parameter recommendations including exposure time, tube voltage, tube current, and other X-ray imaging parameters that are automatically adjusted to match the detected use case, transitioning from static generic settings to dynamic task-specific settings.
2Manufacturing precision
If manual configuration of operation modes is required to achieve optimal settings, then the manufacturing precision is improved, but the ease of operation deteriorates
Solution Approach 1:
The system performs self-configuration by automatically analyzing the input X-ray images and angulation data to determine the appropriate imaging task. The machine learning model enables the system to autonomously select optimal operation modes without requiring manual user intervention, combining automated intelligence with precise parameter selection.
Solution Approach 2:
The system uses feedback from the analyzed X-ray images and angulation state to automatically adjust and select operation modes. The machine learning model continuously processes the input data and provides feedback-driven parameter recommendations, creating a closed-loop system that adapts to the actual imaging requirements.
3Adaptability or versatility
If generic parameters are used to accommodate multiple use cases, then the adaptability is improved, but the manufacturing precision deteriorates
Solution Approach 1:
The system transitions from static generic parameters to dynamic task-specific parameters. The machine learning model enables real-time adaptation of imaging parameters based on the classified imaging task, allowing the system to dynamically adjust exposure time, tube voltage, tube current, and other parameters to optimize image quality for each specific use case.
Solution Approach 2:
The system automatically changes imaging parameters based on the detected imaging task. The machine learning model outputs specific parameter recommendations that are tailored to each use case, enabling the system to adapt its parameters dynamically rather than relying on fixed generic settings, thereby maintaining both versatility and precision.
4Productivity
If automatic mode determination using machine learning is implemented, then the productivity is improved, but the device complexity increases
Solution Approach 1:
The system replaces manual mechanical configuration with automated machine learning-based determination. Instead of requiring users to manually select and configure operation modes, the patent uses a trained machine learning model that automatically classifies the imaging task and recommends optimal parameters, substituting human operation with intelligent automation.
Solution Approach 2:
The machine learning model enables the system to self-determine the appropriate operation mode without external user input. The system autonomously processes the input X-ray images and angulation data, performs classification, and generates parameter recommendations, making the complex automation transparent and effortless for the user.
Data Source
AI summary
For automatically determining an operation mode for an X-ray imaging system that includes an X-ray source and an X-ray detector mounted on a C-arm, angulation data defining an angulation state of the C-arm is received, and at least one X-ray image depicting an object according to the angulation state is received. The operation mode for the X-ray imaging system is selected as one of two or more predefined operation modes by applying a trained machine learning model for classification to input data. The input data includes the at least one X-ray image and the angulation data.