Dual Energy Spectral CT Noise Reduction via Correlated Noise Segmentation
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Solution Overview
Problem
Current dual energy spectral CT systems suffer from noise propagation in material density and monochromatic images due to correlated noise, which existing noise reduction schemes are limited in addressing effectively.
Innovation Solution
The system and method involve obtaining CT scan data with two or more incident energy spectra, decomposing it into basis material images, generating monochromatic images based on noise levels, and reducing noise in these images to produce final, noise-minimized images by separating and reducing both negative and non-negative correlated noise components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dual energy spectral CT systems acquire data at multiple energy levels to improve material discrimination, then material density and monochromatic images can be generated, but correlated noise propagates and degrades image quality
Solution Approach 1:
The patent segments the correlated noise into negative correlated noise and non-negative correlated noise components. This segmentation allows different noise reduction techniques to be applied to each component separately, effectively reducing overall noise while preserving image quality and material discrimination capability
Solution Approach 2:
The patent extracts and removes the correlated noise components from the dual energy spectral CT images through specific noise reduction schemes. By taking out the harmful correlated noise while retaining the useful signal, the system achieves both noise reduction and preservation of material discrimination accuracy
2Object-affected harmful factors
If existing noise reduction schemes are applied to reduce noise in monochromatic images, then some noise can be reduced, but they are limited in addressing correlated noise effectively
Solution Approach 1:
The patent changes the approach to noise reduction by specifically targeting correlated noise parameters rather than applying general noise reduction. By modifying the noise reduction parameters and methods to account for the specific characteristics of correlated noise in dual energy spectral CT, the system achieves more effective noise reduction while preserving measurement precision
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 significantly reduces noise in monochromatic and material density images, improving diagnostic imaging data quality by minimizing correlated noise, particularly non-negative correlated noise, which is often dominant.
Implementation Method 1
an x-ray source that emits a beam of x-rays toward an object to be imaged
Implementation Method 2
a detector that receives the x-rays attenuated by the object
Implementation Method 3
Two physical processes dominate the x-ray attenuation: (1) Compton scatter and the (2) photoelectric effect
Implementation Method 4
a system derives the behavior at a different energy based on a signal from two regions of photon energy in the spectrum: the low-energy and the high-energy portions of the incident x-ray spectrum
Data Source
AI summary
An imaging system includes an x-ray source, a detector, a data acquisition system (DAS) operably connected to the detector, and a computer operably connected to the DAS. The computer is programmed to obtain CT scan data with two or more incident energy spectra, decompose the obtained CT scan data into projection CT data of a first basis material and a second basis material, generate a first basis material image and a second basis material image using the decomposed projection CT data, generate a first monochromatic image from the first basis material image and the second basis material image at a first energy that is selected based on an amount of correlated noise at the first energy, noise-reduce the first monochromatic image to generate a noise-reduced first monochromatic image, and generate a final monochromatic image based at least on the noise-reduced first monochromatic image.