CT Image Reconstruction with Extended Tam Window Weighting
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Solution Overview
Problem
Motion artifacts and noise in CT images reconstructed using iterative reconstruction techniques due to object motion during scanning are not adequately addressed by existing systems.
Innovation Solution
A method and system utilizing an extended Tam window-based weighting matrix to determine an objective function for iterative reconstruction, incorporating a difference model and regularization item for denoising, which includes adjusting weighting factors based on detector array parameters to reduce motion artifacts and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative reconstruction techniques are used to reconstruct CT images, then image reconstruction is achieved, but motion artifacts and noise are introduced due to object motion during scanning
Solution Approach 1:
The patent applies parameter changes by modifying the objective function through extended Tam window weighting factors. The weighting factors are adjusted based on detector position parameters to compensate for motion effects during iterative reconstruction, thereby reducing motion artifacts and noise while maintaining image reconstruction quality
Solution Approach 2:
The patent implements preliminary action by pre-calculating and applying extended Tam window weighting factors before the iterative reconstruction process. The weighting matrix is determined in advance based on detector array parameters and motion models, allowing the reconstruction algorithm to preemptively correct for expected motion artifacts
2Manufacturing precision
If weighting factors are adjusted based on detector array parameters to reduce motion artifacts, then image quality is improved, but calculation complexity increases
Solution Approach 1:
The patent applies local quality by assigning different weighting factors to different detector elements based on their specific positions in the detector array. The extended Tam window weighting is locally adapted for each detector row and column, allowing precise correction of motion artifacts in different regions while managing computational complexity through localized rather than global optimization
Data Source
AI summary
The present disclosure relates to methods, systems, and non-transitory computer readable mediums for reconstructing an image. Image data may be obtained, wherein the image data may include projection data and may be generated by an imaging device. An objective function associated with a target image may be determined based on the image data. The objective function may include a difference model, wherein the difference model may represent a difference between a projection of the target image and the image data. The difference model may be determined based on a weighting matrix, and the weighting matrix may be determined based on an extended Tam window. The target image may be reconstructed by performing a plurality of iterations based on the objective function.