Iterative CT Reconstruction with Enlarged Voxels
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Solution Overview
Problem
Current CT imaging systems rely on analytical algorithms like FBP, limiting the availability of iterative reconstruction methods that could enhance image quality with reduced noise, improved resolution, and fewer artifacts.
Innovation Solution
An improved iterative reconstruction method that enlarges voxels and detectors in the projection data, combined with techniques like trapezoidal response kernels, band-suppression post-processing, and adaptive regularization, to iteratively reconstruct images with enhanced spatial resolution and reduced artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative reconstruction is used to improve image quality, then noise is reduced and resolution is enhanced, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the image reconstruction process into multiple iterative steps, where each iteration refines the image estimate progressively. The reconstruction is segmented into forward projection, error calculation, and back-projection phases that are repeated iteratively to gradually improve image quality while managing computational load through staged processing.
Solution Approach 2:
The patent implements dynamics by using adaptive regularization parameters that change during the iterative reconstruction process. The regularization strength is dynamically adjusted based on the current iteration state and image characteristics, allowing the system to balance between noise reduction and detail preservation adaptively throughout the reconstruction process.
2Measurement precision
If iterative reconstruction is used to reduce artifacts, then image quality improves, but processing time increases
Solution Approach 1:
The patent applies periodic action through the iterative nature of the reconstruction process, where the same forward projection and back-projection operations are repeated cyclically. Each periodic iteration progressively reduces artifacts by refining the image estimate, with the periodic repetition enabling gradual artifact suppression while maintaining a structured computational approach.
Solution Approach 2:
The patent implements feedback mechanisms by calculating the difference between measured projection data and simulated projections from the current image estimate, then using this error feedback to adjust and refine the image in subsequent iterations. This feedback loop continues until convergence criteria are met, systematically reducing artifacts through iterative correction.
3Measurement precision
If voxel size is increased to reduce noise, then image quality improves, but spatial resolution decreases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the regularization parameter throughout the iterative reconstruction process. This parameter controls the balance between noise reduction and spatial resolution preservation, allowing the system to adaptively modify the degree of smoothing applied during different stages of reconstruction to optimize both noise reduction and detail preservation.
Solution Approach 2:
The patent implements dynamics through adaptive regularization that adjusts the smoothing strength based on local image characteristics and iteration progress. The regularization parameter is not fixed but dynamically modified during reconstruction, enabling the system to apply stronger smoothing where noise reduction is needed while preserving edges and fine details where spatial resolution is critical.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves image quality by increasing spatial resolution, reducing aliasing and artifacts, and achieving results comparable to high-quality reconstruction images produced by commercial CT systems.
Implementation Method 1
A computed tomography (CT) imaging system typically includes an imaging beam source (e.g., x-ray source or other suitable source) that projects fan- or cone-shaped imaging beams through an object being imaged
Implementation Method 2
Intensity of radiation from the beam received at the detector array depends on attenuation of the imaging beam by the object
Data Source
AI summary
An improved iterative reconstruction method to reconstruct a first image includes generating an imaging beam, receiving said imaging beam on a detector array, generating projection data based on said imaging beams received by said detector array, providing said projection data to an image reconstructor, enlarging one of a plurality of voxels and a plurality of detectors of the provided projection data, reconstructing portions of the first image with the plurality of enlarged voxels or detectors, and iteratively reconstructing the portions of the first image to create a reconstructed image.