Iterative CT Reconstruction Using Dual Optimization to Cut Noise
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Solution Overview
Problem
Conventional CT image reconstruction methods face challenges with reduced dose or insufficient data, leading to degraded image quality with noise and artifacts, particularly in cardiac imaging scenarios.
Innovation Solution
A multi-step iterative image reconstruction process incorporating a first optimization operation and a second optimization operation using a machine learning model to enhance image quality, addressing issues of noise and artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional filtered back projection or FBP-based iteration is used for CT image reconstruction, then the reconstruction process is simple and fast, but image quality deteriorates significantly when reduced dose or insufficient data is used
Solution Approach 1:
The patent segments the iterative reconstruction process into multiple stages: initial image reconstruction using FBP, followed by multiple iteration steps with different optimization operations. Each iteration step processes the image through a first optimization operation (using projection data) and a second optimization operation (using image data), progressively improving image quality while maintaining computational efficiency through structured decomposition of the reconstruction task.
2Object-affected harmful factors
If iterative reconstruction with regularization terms (total variation, GGMRF) is used to suppress noises, then noise suppression is achieved, but image quality deteriorates with massive artifacts and cartoon sense
Solution Approach 1:
The patent dynamically changes the optimization parameters and operations across different iteration steps. The first optimization operation uses projection data with weight parameters, while the second optimization operation uses image data with different weight parameters. This parameter variation allows the system to suppress noise effectively in early iterations while avoiding over-regularization and artifacts in later iterations, adapting the regularization strength to the current reconstruction state.
Solution Approach 2:
The patent implements a dynamic iterative process where the optimization operations adapt to the current iteration state. The first and second optimization operations are applied sequentially with varying weights and parameters, allowing the reconstruction algorithm to dynamically adjust its behavior between noise suppression and artifact prevention based on the progression of the iteration process.
3Measurement precision
If multiple iteration steps with multiple optimization operations are implemented, then image quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the complex optimization process into two distinct operations applied in sequence during each iteration step. The first optimization operation processes projection data to generate an updated image, while the second optimization operation processes this updated image to generate the final optimized image. This segmentation allows each operation to be simpler and more specialized, reducing overall computational complexity while maintaining high image quality through cumulative improvements across multiple iterations.
Data Source
AI summary
The present disclosure relates to systems and methods for image reconstruction. The systems and methods may obtain an initial image to be processed. The systems and methods may also generate a reconstructed image by performing a plurality of iteration steps on the initial image. At least one of the plurality of iteration steps may include a first optimization operation and a second optimization operation. The first optimization operation may include receiving an image to be processed in the iteration step and determining an updated image by preliminarily optimizing the image to be processed. The second optimization operation may include determining, using an optimizing model, an optimized image based on the updated image and designating the optimized image as a next image to be processed in a next iteration step or designating the optimized image as the reconstructed image.