Iterative Image Reconstruction Using Voxel-Dependent Scaling
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Solution Overview
Problem
Model-based iterative reconstruction (MBIR) algorithms for computed tomography (CT) imaging face challenges in computation time and resource requirements due to their complexity, leading to slow reconstruction processes despite offering higher quality images with reduced noise and artifacts.
Innovation Solution
The implementation of a simultaneous algorithm that computes a voxel-dependent scaling factor and applies it to the gradient of an objective function to reconstruct images, allowing for adaptive scaling and reduced computation time by using a coefficient map and efficient gradient computation methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based iterative reconstruction algorithms are used to improve image quality and reduce noise, then image quality and noise reduction are improved, but computation time and computational resources increase
Solution Approach 1:
The patent segments the iterative reconstruction process into multiple subsets of projection data, processing them in ordered groups rather than simultaneously. This segmentation allows the algorithm to achieve convergence faster by processing manageable portions of data iteratively, reducing overall computation time while maintaining MBIR image quality benefits
Solution Approach 2:
The patent implements periodic action through ordered subset iteration, where projection data is divided into multiple subsets and processed in repeated cycles. Each cycle processes a subset of data, and after processing all subsets, a full iteration is complete. This periodic processing pattern enables faster convergence compared to traditional simultaneous iterative methods
2Manufacturing precision
If iterative optimization algorithms are used to minimize the objective function, then image reconstruction accuracy is improved, but the number of iterations and computational complexity increase
Solution Approach 1:
The patent segments the projection data into multiple ordered subsets, allowing the optimization algorithm to process data in manageable groups. This segmentation reduces the effective complexity of each iteration while maintaining overall reconstruction accuracy through systematic processing of all data subsets
Solution Approach 2:
The patent applies partial action by processing only a subset of projection data in each iteration rather than the complete dataset. This partial processing approach reduces computational complexity per iteration while the ordered sequence ensures that all data is eventually processed, achieving convergence with fewer total iterations
Data Source
AI summary
A method is provided for iteratively reconstructing an image of an object. The method includes accessing measurement data associated with the image, and using a simultaneous algorithm to reconstruct the image. Using the simultaneous algorithm to reconstruct the image includes determining a scaling factor that is voxel-dependent, and applying the voxel-dependent scaling factor to a gradient of an objective function to reconstruct the image.