X-ray CT Iterative Reconstruction for Small FOV Accuracy
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Solution Overview
Problem
The accuracy of small Field of View (FOV) images in X-ray CT devices is compromised due to inaccuracies in local measurement projection data, which are affected by errors in large FOV images, leading to artifacts and noise that deteriorate CT value accuracy.
Innovation Solution
An X-ray CT device and method that includes units for creating background projection data, calculating local measurement projection data, and iteratively correcting local images by eliminating causes of accuracy deterioration in the background region, thereby improving the accuracy of small FOV images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If extensive reconstruction is used to image the entire subject, then the entire subject can be imaged, but the computational time and resources required increase significantly
Solution Approach 1:
The invention divides the CT image into a local region of interest and a background region. Only the local region is subjected to iterative reconstruction, while the background region is handled separately. This segmentation allows high-quality imaging of the critical area without requiring iterative reconstruction of the entire subject, thereby reducing computational time and resources.
Solution Approach 2:
The invention extracts the local region of interest from the complete CT image and performs iterative reconstruction only on this extracted portion. The background region is forward-projected separately and subtracted from the measurement projection data. This extraction approach maintains the ability to image the entire subject while significantly reducing the computational burden.
2Measurement precision
If iterative reconstruction is applied to the entire subject, then image quality improves, but the computational complexity increases
Solution Approach 1:
The invention segments the CT image into local and background regions, applying iterative reconstruction only to the local region where diagnostic information is needed. This maintains high image quality for the region of interest while reducing overall computational complexity by avoiding iterative processing of the entire subject.
Solution Approach 2:
The invention applies different processing qualities to different regions: high-quality iterative reconstruction is applied locally to the region of interest, while the background region undergoes simpler forward projection and subtraction. This local quality approach optimizes image quality where it matters most while minimizing unnecessary computational complexity.
3Object-affected harmful factors
If low-dose exposure is used, then radiation exposure is reduced, but noise and artifacts increase
Solution Approach 1:
The invention employs iterative reconstruction with feedback mechanisms that continuously compare measurement projection data with calculated projection data and adjust the CT image accordingly. This feedback process enables effective noise reduction and artifact suppression in low-dose imaging by leveraging the feedback loop to converge on the most accurate image representation despite reduced signal strength.
Solution Approach 2:
The iterative reconstruction process continues until convergence criteria are met, maintaining continuous adjustment and refinement of the image. This continuous useful action allows the system to progressively improve image quality and reduce noise/artifacts even when starting with low-dose exposure data, extracting maximum information from the limited signal.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach prevents CT value accuracy deterioration by accurately correcting local images and eliminating sources of error in the background region, ensuring high-quality diagnostic images with reduced noise and artifacts at low radiation doses.
Implementation Method 1
an X-ray generation unit for generating an X-ray
Implementation Method 2
an X-ray detection unit for detecting the X-ray after transmitted through a subject
Data Source
AI summary
The background projection data of a background region CT image in the X-ray emission path is calculated. Using measurement projection data and background projection data, the measurement projection data of local regions is calculated. Local region CT images are calculated on the basis of local measurement projection data, and the projection data of a local region CT image in the X-ray emission path is calculated. On the basis of local calculation projection data and local measurement projection data, local CT images are iteratively corrected. When creating background region CT images or calculating background projection data, the cause of calculation accuracy deterioration is eliminated by the use of processing such as smoothing, and without deterioration of CT value accuracy in regions other than the target region, a high-quality CT image is obtained.