Channelized Preconditioner for Iterative CT Reconstruction
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Solution Overview
Problem
Iterative reconstruction methods in computed tomography (CT) imaging are computationally intensive due to the complexity of geometrical, physical, and statistical models, leading to long computation times, which hinders their adoption in clinical environments despite providing superior image quality and dose characteristics.
Innovation Solution
A channelized preconditioner design is introduced, decomposing the preconditioner into multiple channels that process different frequency sub-bands and spatial orientations, allowing for independent processing and combination to form a transformed gradient vector for image update, effectively addressing space-variant effects and improving convergence speed without excessive computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative reconstruction techniques are used, then image quality and dose characteristics are improved, but computation time increases
Solution Approach 1:
The preconditioner is divided into multiple independent channels, each processing different frequency sub-bands or spatial orientations. This segmentation allows parallel computation across channels, reducing the overall computation time while maintaining the image quality improvements provided by iterative reconstruction
Solution Approach 2:
The invention transforms the gradient vector from image space to channel space through the multi-channel preconditioner. This dimensional transformation enables independent processing of different frequency components or spatial orientations in parallel, effectively reducing computation time while preserving reconstruction quality
2Measurement precision
If iterative reconstruction techniques are used, then image quality is improved, but device complexity increases
Solution Approach 1:
By segmenting the preconditioner into independent channels, the system architecture becomes more modular and manageable. Each channel can be implemented as a separate computational unit, making the overall complex system easier to design, implement, and maintain
Solution Approach 2:
The multi-channel preconditioner introduces adjustable parameters such as channel gains that can be optimized independently. This parameterization allows fine-tuning of the reconstruction process to achieve desired image quality while controlling computational resources, effectively managing system complexity
Data Source
AI summary
The use of the channelized preconditioners in iterative reconstruction is disclosed. In certain embodiments, different channels correspond to different frequency sub-bands and the output of the different channels can be combined to update an image estimate used in the iterative reconstruction process. While individual channels may be relatively simple, the combined channels can represent complex spatial variant operations. The use of channelized preconditioners allows empirical adjustment of individual channels.