PET Image Reconstruction Using Autocorrelation Feature Images
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Solution Overview
Problem
Low-count PET image reconstruction in PET imaging technology results in high noise and low signal-to-noise ratio, making it challenging for clinical applications due to the ill-conditioned inverse estimation problem.
Innovation Solution
The method involves acquiring a prior image with an anatomical image and an autocorrelation feature image determined by a gray-level co-occurrence matrix, and using these features with an iterative algorithm to reconstruct the PET image, improving the signal-to-noise ratio and accuracy of tumor image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If low-count PET projection data is used for reconstruction, then injection dose of radiotracer and scan time are reduced, but the reconstructed image quality deteriorates with high noise and low signal-to-noise ratio
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing autocorrelation feature images and their corresponding feature values from high-count PET data before the actual low-count reconstruction process. These pre-computed features serve as prior information that guides the reconstruction of low-count data, eliminating the need to perform complex autocorrelation calculations during the reconstruction phase itself.
Solution Approach 2:
The patent introduces autocorrelation feature images and their feature values as intermediary elements that mediate between the low-count projection data and the final reconstructed image. These intermediaries encode spatial and textural prior information that helps constrain the ill-posed inverse estimation problem, enabling quality reconstruction from limited data without requiring high injection doses or long scan times.
2Loss of substance
If low-count PET projection data is used for reconstruction, then economic cost is reduced, but the reconstructed image quality deteriorates with high noise
Solution Approach 1:
The patent pre-computes autocorrelation feature images and extracts their feature values from reference high-count PET data before the low-count reconstruction process. This preliminary action creates a library of prior information that can be applied during low-count reconstruction, allowing the system to achieve acceptable image quality with reduced radiotracer injection doses by leveraging pre-analyzed spatial and textural patterns.
Solution Approach 2:
The autocorrelation feature values serve as intermediary parameters that bridge the gap between low-count projection data and high-quality reconstructed images. These intermediaries encapsulate prior knowledge about tissue autocorrelation patterns, enabling the reconstruction algorithm to distinguish signal from noise even when the input data has poor signal-to-noise ratio due to low radiotracer doses.
3Device complexity
If traditional reconstruction methods are used on low-count data, then the reconstruction process is simple, but the reconstructed image has poor quality and high noise
Solution Approach 1:
The patent performs the computationally intensive autocorrelation calculation and feature extraction in advance, before the actual reconstruction process. This preliminary action separates the complex prior information generation from the reconstruction step itself, allowing the reconstruction algorithm to use pre-computed autocorrelation feature values as constraints, thereby maintaining relative simplicity in the reconstruction phase while achieving superior image quality.
Solution Approach 2:
The patent introduces autocorrelation feature values as intermediary constraints in the reconstruction objective function. These intermediaries provide additional information about the expected autocorrelation structure of the image, guiding the reconstruction algorithm to produce higher quality images from low-count data without requiring excessively complex reconstruction methodologies.
Data Source
AI summary
A method, device and equipment for reconstructing a PET image are provided. The method includes acquiring a prior image comprising an anatomical image and an autocorrelation feature image, the autocorrelation feature image being determined based on gray-level co-occurrence matrix of the anatomical image; and acquiring a feature value of the prior image; reconstructing the PET image according to the feature value and an iterative algorithm.