Iterative Tomographic Reconstruction Using Multi-Grid Filtering
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Solution Overview
Problem
Conventional tomographic imaging techniques face limitations in achieving rapid convergence and high-quality reconstruction of tomographic images, particularly in nano CT applications, where existing methods are time-consuming and sensitive to noise.
Innovation Solution
An iterative reconstruction method employing Back Projection, mathematical filtering, and Forward Projection techniques, with a multi-grid approach to accelerate convergence and enhance image quality, using a coarse initial tomogram and progressively finer grids, and incorporating geometrical transformations to optimize image mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional tomographic reconstruction methods are used, then the reconstruction process is simple to implement, but the convergence is slow and computational time is excessive
Solution Approach 1:
The patent applies preliminary action by performing a coarse reconstruction first to obtain an initial tomogram, then using this initial result to guide subsequent fine reconstructions. This preliminary coarse-to-fine approach accelerates convergence by providing a head start for the iterative refinement process, reducing the total computational time required.
Solution Approach 2:
The reconstruction process is segmented into multiple stages: coarse reconstruction stage and fine reconstruction stage. Each stage uses appropriate filtering strategies tailored to its resolution level. This segmentation allows the system to efficiently handle different frequency components at appropriate computational costs, improving overall reconstruction speed.
2Measurement precision
If high-resolution reconstruction is pursued, then image quality improves, but noise sensitivity increases and computational complexity increases
Solution Approach 1:
The patent applies local quality by using different filtering strategies for different frequency ranges and reconstruction stages. Coarse reconstructions use stronger filtering to suppress noise, while fine reconstructions use milder filtering to preserve detail. This localized adaptation of filtering strength optimizes the balance between noise suppression and detail preservation at each resolution level.
Solution Approach 2:
The filtering parameters are made dynamic rather than static. The filter strength and type are adjusted adaptively based on the current reconstruction stage and the observed convergence behavior. This dynamic adjustment allows the system to optimize computational resources while maintaining high image quality and reducing noise sensitivity.
3Manufacturing precision
If iterative refinement is applied to improve image quality, then reconstruction accuracy improves, but the number of iterations increases computational time
Solution Approach 1:
The coarse reconstruction serves as a preliminary action that provides a good initial estimate for the fine reconstruction. This preliminary step reduces the number of iterations needed in the subsequent fine refinement stage, as the iterative process starts from a closer approximation to the final solution, thereby improving processing efficiency.
Solution Approach 2:
The patent maintains continuity of useful action by seamlessly transitioning from coarse to fine reconstruction stages. The output of the coarse stage becomes the input for the fine stage, creating a continuous refinement process. This continuous approach avoids redundant computations and maintains momentum in the convergence process.
Data Source
AI summary
A method of investigating a specimen using a tomographic imaging apparatus, by performing, in multiple iterations, the following steps:(i) Using a Back Projection technique to produce an initial tomogram from a set of initial images;(ii) Subjecting said initial tomogram to a mathematical filtering operation, thereby producing an adjusted tomogram;(iii) Using a Forward Projection technique on said adjusted tomogram to dissociate it into a set of calculated images;(iv) Repeating steps (i)-(iii) until said calculated images satisfy an acceptance criterion.