Tomographic Iterative Reconstruction Using Deconvolution Filter
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Solution Overview
Problem
Iterative reconstruction methods for tomographic images are laborious and time-consuming, particularly due to slow convergence speed for high frequency image features and high processor intensity, especially in simultaneous update approaches.
Innovation Solution
The use of a deconvolution filter, such as a ramp filter, is applied during each iteration of the reconstruction process to accelerate convergence, either in the image domain or projection domain, allowing for faster image updates and termination based on a completion criterion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative reconstruction methods are used to improve image quality, then manufacturing precision is improved, but productivity deteriorates due to laborious and time-consuming processing
Solution Approach 1:
A deconvolution filter is applied as a preliminary action during each iteration of the reconstruction process to accelerate convergence. This pre-processing step prepares the image data in advance, enabling faster convergence towards the final high-quality image without requiring excessive iteration cycles.
Solution Approach 2:
The patent changes the parameter of applying a deconvolution filter during the reconstruction iterations. By modifying the reconstruction process to include this filter operation, the convergence speed is accelerated while maintaining image quality, thus resolving the contradiction between precision and productivity.
2Ease of operation
If simultaneous update methods are used to enable parallel processing, then ease of operation is improved, but productivity deteriorates due to slow convergence speed for high frequency image features
Solution Approach 1:
The patent introduces a deconvolution filter as a parameter change that works in conjunction with simultaneous update methods. This modification accelerates the convergence of high frequency image features during parallel processing, maintaining the ease of operation while improving productivity.
3Manufacturing precision
If iterative reconstruction is performed to improve image quality, then manufacturing precision is improved, but use of energy deteriorates due to high processor intensity
Solution Approach 1:
By applying the deconvolution filter as a preliminary action during each iteration, the reconstruction process converges faster, reducing the total number of iterations required. This decreases the cumulative processor power consumption while still achieving high-quality images.
Solution Approach 2:
The introduction of the deconvolution filter parameter changes the energy efficiency profile of the reconstruction process. Although each iteration may require additional filtering operations, the overall reduction in iteration count results in lower total energy consumption.
Data Source
AI summary
The present disclosure relates to iterative reconstruction of images. In certain embodiments, a deconvolution filter is used to approximate the inversion of a Hessian matrix associated with the reconstruction. In one such embodiment, the desired image is not reconstructed directly in the iterative process. Instead, an image is reconstructed that yields the desired image when filtered by the deconvolution filter.