Tomographic Image Reconstruction Using Background Projection Data
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Solution Overview
Problem
Current non-invasive imaging techniques face challenges in reconstructing high-resolution images of targeted fields of view (FOV) within the imaging system's scan FOV, as iterative reconstruction methods fail to accurately account for signals from outside the targeted FOV, leading to artifacts and increased computational complexity.
Innovation Solution
The method involves deriving background projection data for areas outside the targeted FOV and using it in the reconstruction process, allowing for reduced artifacts and computational efficiency by selecting the appropriate reconstruction methodology based on pixel comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative reconstruction techniques are used for targeted FOV less than scan FOV, then image resolution can be improved, but artifacts appear at the periphery and quantitative accuracy deteriorates due to unaccounted signals from outside the targeted FOV
Solution Approach 1:
The patent divides the reconstruction process into two distinct segments: (1) deriving background projection data from a preliminary reconstruction of the entire scan FOV, and (2) using this background data to correct the targeted FOV reconstruction. This segmentation allows the system to handle signals from outside the targeted FOV separately, preventing them from causing artifacts while maintaining high resolution in the region of interest.
Solution Approach 2:
The patent performs a preliminary reconstruction of the entire scan FOV to derive background projection data before performing the targeted FOV reconstruction. This preliminary action captures signals from outside the targeted FOV that would otherwise contaminate the final image, allowing the targeted reconstruction to proceed with corrected data that eliminates peripheral artifacts and improves quantitative accuracy.
2Measurement precision
If the full scan FOV is reconstructed at high resolution to obtain the targeted FOV image, then image quality is improved, but computational time and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the necessary background projection data from the full scan FOV reconstruction without performing a complete high-resolution reconstruction of the entire FOV. By taking out only the background information needed to correct the targeted FOV, the system achieves high-quality targeted images while avoiding the excessive computational burden of fully reconstructing the entire scan FOV at high resolution.
Solution Approach 2:
The patent applies high resolution only to the targeted FOV where it is needed, while using the full scan FOV reconstruction solely for deriving background projection data. This local quality approach ensures that computational resources are focused on the region of interest, maintaining high image quality where required while minimizing overall computational time and storage requirements.
3Device complexity
If signals from outside the targeted FOV are not accounted for in reconstruction, then computational complexity is reduced, but peripheral artifacts appear and quantitative accuracy deteriorates
Solution Approach 1:
The patent introduces background projection data as an intermediary element that mediates between signals from outside the targeted FOV and the final image reconstruction. This intermediary allows the system to account for external signals without directly incorporating them into the targeted FOV reconstruction, thereby maintaining image accuracy while keeping computational complexity manageable through efficient data utilization.
Data Source
AI summary
Methods for performing image reconstruction that include deriving background projection data for an area outside a targeted field of view of a tomographic image, and reconstructing the tomographic image of the targeted field of view, wherein the background projection data is used in the reconstruction. Methods for selecting a reconstruction methodology that include determining a number of pixels in a reconstructed image for a first reconstruction methodology, determining a number of pixels in a reconstructed image for a second reconstruction methodology, comparing the number of pixels for the first reconstruction methodology and the number of pixels for the second reconstruction methodology, and selecting the reconstruction methodology for image reconstruction based on the comparison of the number of pixels. Imaging systems implementing these methods are also provided.