Automatic X-Ray Image Restoration Parameter Deployment
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Solution Overview
Problem
Current X-ray imaging systems face challenges in maintaining optimal image quality due to varying calibration across different installations and the aging of X-ray tubes, which affects noise characteristics and requires manual intervention for calibration and image restoration.
Innovation Solution
An AI-based image restoration method using a neural network that collects site-specific data to automatically update image restoration parameters, allowing for individualized noise characterization and recalibration of X-ray systems without human intervention, utilizing a remote server to generate and deploy localized restoration information to local imaging systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual calibration procedures are performed by service technicians at each installation, then local system parameters can be adjusted, but the process requires significant human intervention and time
Solution Approach 1:
The system performs automatic self-calibration by capturing images of a calibration object, extracting feature data, and computing transformation parameters without requiring service technician intervention. The calibration process is autonomously executed by the imaging system itself, eliminating manual operation requirements and reducing time loss.
Solution Approach 2:
The system pre-processes calibration data by capturing multiple images of a calibration object and extracting feature points before actual imaging operations. This preliminary calibration setup establishes transformation parameters in advance, enabling rapid subsequent calibrations without repeated manual interventions.
2Reliability
If service technicians perform local calibration procedures, then system parameters can be determined, but the process requires technician involvement and cannot account for system aging
Solution Approach 1:
The imaging system autonomously performs calibration by capturing images, extracting feature data, and computing transformation parameters without service technician involvement. This self-service mechanism ensures consistent calibration accuracy while achieving complete automation, allowing the system to adapt to aging components through repeated autonomous recalibration.
Solution Approach 2:
The system continuously monitors its own performance by capturing calibration images and comparing feature extraction results against expected values. This feedback loop enables automatic detection of drift due to system aging and triggers recalibration when necessary, maintaining reliability through automated adaptation.
3Adaptability or versatility
If different service technicians perform calibration, then local adjustments can be made, but variation in calibration quality occurs across installations
Solution Approach 1:
The system uses a universal calibration object with known geometric features that can be imaged and processed by any installation of the imaging system. The same feature extraction algorithm and transformation computation method are applied across all systems, ensuring consistent calibration precision while maintaining adaptability to local conditions through automated parameter adjustment.
Solution Approach 2:
The calibration process automatically adjusts transformation parameters based on the specific installation environment and system characteristics. By computing parameters locally from captured images rather than applying fixed technician-dependent settings, the system maintains measurement precision consistency across different installations while adapting to local variations.
Data Source
AI summary
A method includes obtaining, at a local imaging system, projection data for an object representing an intensity of radiation detected along a plurality of rays through the object using a first set of imaging parameters; transmitting an image quality dataset related to the obtained projection data to a remote server; generating, via the remote server, localized restoration information based on the received image quality dataset; transferring the localized restoration information from the remote server to the local imaging system; and updating the local imaging system using the localized restoration information.