Computed Tomography Misalignment Correction
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Solution Overview
Problem
Computed tomography imaging systems face challenges in accurately correcting misalignments between the x-ray source, sample stage, and detector, leading to blurry or out-of-focus reconstructed images, especially at micrometre or nanometre scales, as existing methods are limited in accuracy and applicability.
Innovation Solution
A computed tomography imaging process that corrects misalignments by processing projection data to generate modified projection images using determined misalignment values, evaluating image quality based on sharpness through spatial information, and refining estimates from lower to higher spatial resolutions, allowing for the selection of optimal misalignment combinations to achieve the highest quality reconstructed images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard reconstruction algorithms are used without misalignment correction, then the imaging process is simple and fast, but the reconstructed images are blurry and out-of-focus
Solution Approach 1:
The patent applies misalignment correction to projection images before reconstruction. The system determines misalignment parameters from the projection data and corrects the projection images using these parameters prior to applying standard reconstruction algorithms, thereby improving image sharpness without requiring complex reconstruction modifications
Solution Approach 2:
The patent introduces an intermediary correction step that processes projection images between data acquisition and reconstruction. This intermediary processing determines misalignment parameters and applies corrections to the projection data, serving as a bridge that improves image quality while maintaining compatibility with standard reconstruction algorithms
2Measurement precision
If misalignment correction is applied using existing methods, then image quality improves, but the method is limited in accuracy and applicability
Solution Approach 1:
The patent determines misalignment parameters (such as lateral offsets and rotational misalignments) from the projection data and applies these parameter corrections to improve image quality. The system evaluates image quality metrics to optimize the correction parameters, achieving higher accuracy across different imaging scenarios
Solution Approach 2:
The patent presents a general method for misalignment correction that can be applied to various tomographic imaging systems and configurations. The correction approach works with different projection data types and reconstruction algorithms, making it broadly applicable across multiple imaging scenarios and systems
3Measurement precision
If correction parameters are optimized for highest image quality, then image sharpness improves, but computational time increases
Solution Approach 1:
The patent evaluates image quality metrics (such as sharpness or focus measures) to assess the effectiveness of misalignment corrections. This feedback mechanism allows the system to optimize correction parameters by comparing image quality before and after correction, iteratively improving results while managing computational resources
Data Source
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Figure 3A~3C
AI summary
A computed tomography imaging process, including: accessing projection data representing two-dimensional projection images of an object acquired using a misaligned tomographic imaging apparatus; and processing the projection data to generate misalignment data representing one or more values that quantify respective misalignments of the tomographic imaging apparatus.