Convolutional Kernel Calibration for Imaging Field Accuracy
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Solution Overview
Problem
Imaging devices such as X-ray scanners and CT devices face inaccuracies due to mechanical deviations, crosstalk between detection units, and scattering phenomena, leading to suboptimal imaging data.
Innovation Solution
A calibration method and system that utilize a convolutional neural network-based calibration model to determine a target convolution kernel, which is used to calculate calibration information for mechanical deviations, crosstalk, and scattering, thereby improving the accuracy of imaging data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used to correct mechanical deviations, crosstalk, and scattering, then calibration accuracy can be improved, but the calibration process becomes time-consuming and computationally intensive
Solution Approach 1:
The patent pre-calculates and stores calibration kernels for various deviation scenarios in a lookup table before actual imaging operations. During calibration, the system only needs to query the pre-computed kernels based on measured deviation parameters, transforming a complex real-time calculation into a simple retrieval operation that significantly reduces calibration time while maintaining accuracy
Solution Approach 2:
The patent creates simplified mathematical models (kernels) that replicate the complex physical calibration processes. These kernel functions serve as computationally efficient copies of the full calibration algorithms, allowing the system to achieve accurate calibration results through lightweight mathematical operations rather than intensive simulations
2Measurement precision
If complex calibration algorithms are applied to correct multiple error sources, then imaging accuracy is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent separates the calibration process into distinct modular components: mechanical deviation correction, crosstalk correction, and scattering correction. Each error source is handled by a dedicated kernel function, allowing the system to address multiple errors through a series of simple, independent operations rather than one complex intertwined algorithm
Solution Approach 2:
The patent introduces calibration kernels as intermediary mathematical functions that mediate between the raw imaging data and the final corrected images. These kernels act as intermediate processing layers that simplify the overall system architecture by providing a unified, standardized interface for handling multiple types of errors
3Manufacturing precision
If comprehensive calibration correcting all error factors is performed, then image quality is improved, but the processing time and computational load increase
Solution Approach 1:
The patent pre-computes calibration kernels for various deviation scenarios and stores them in lookup tables before actual imaging operations. During calibration, the system only needs to query the pre-computed kernels based on measured deviation parameters, transforming a complex real-time calculation into a simple retrieval operation that significantly reduces processing time while maintaining comprehensive correction
Solution Approach 2:
The patent transforms the calibration problem from solving complex differential equations in real-time to evaluating pre-computed kernel functions with simple parameter substitutions. By changing the mathematical parameters and representation of the calibration data, the system achieves the same comprehensive correction effect with much lower computational complexity
Data Source
AI summary
The present disclosure may provide a calibration system and a method for imaging field. The method may include obtaining a calibration model of a target imaging device. The calibration model may include at least one convolutional layer, and the at least one convolutional layer may include at least one candidate convolution kernel. The method may also include determining a target convolution kernel based on the at least one candidate convolution kernel of the calibration model. The method may also include determining calibration information of the target imaging device based on the target convolution kernel. The calibration information may be used to calibrate at least one of a device parameter of the target imaging device or imaging data acquired by the target imaging device.