Scatter Correction for Dental Cone-Beam CT Using Neural Networks
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Solution Overview
Problem
Current methods for scatter correction in dental cone-beam CT photography, such as using collimators or anti-scatter grids, are impractical due to structural restrictions or reduce the Field of View, while Monte Carlo simulation-based approaches are time-consuming and not suitable for real-time applications.
Innovation Solution
A scatter correction method using Monte Carlo simulation and artificial neural networks to decompose projection images into primary and scatter images, with the neural network learning from simulated and real data to improve image quality by minimizing scatter effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo simulation is used to estimate scatter distribution, then scatter correction accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent pre-calculates scatter distribution data using Monte Carlo simulation for various anatomical structures and stores them in a lookup table before actual CT scanning. During image reconstruction, the system quickly retrieves pre-computed scatter values based on patient anatomy parameters, avoiding time-consuming real-time simulation while maintaining accurate scatter correction
Solution Approach 2:
The patent creates simplified analytical models that replicate the complex Monte Carlo simulation results. These analytical models use pre-computed scatter kernels and convolution operations to approximate scatter distribution, providing near-Monte-Carlo accuracy with significantly reduced computational burden for real-time processing
2Object-affected harmful factors
If collimators of high aspect ratio are used to physically remove scatter, then scatter influence is minimized, but device complexity and structural restrictions increase
Solution Approach 1:
The patent replaces the mechanical collimator system with a computational scatter correction method. Instead of using physical collimators to block scattered X-rays, the system uses software-based scatter estimation and subtraction algorithms to remove scatter effects from the detected images, eliminating the need for complex high-aspect-ratio collimator structures
Solution Approach 2:
The patent introduces an intermediary computational processing step between X-ray detection and image reconstruction. Scatter correction algorithms act as an intermediary that processes the raw detector data to remove scatter effects before the data is used for final image reconstruction, effectively separating the scatter removal function from mechanical hardware
3Object-affected harmful factors
If anti-scatter grids are used to reduce X-ray scatter, then primary ray transmission is improved, but processing time increases due to grid line artifacts
Solution Approach 1:
The patent replaces the physical anti-scatter grid with a computational approach. Instead of using hardware grids that create line artifacts requiring post-processing removal, the system applies software-based scatter correction that models and subtracts scatter distribution without introducing grid line artifacts, eliminating the need for additional processing to remove grid patterns
4Loss of time
If scatter measurement is performed in only some area of the detector, then processing time is reduced, but Field of View decreases
Solution Approach 1:
The patent develops a universal scatter correction model that processes the entire detector array simultaneously. The analytical scatter estimation algorithm computes scatter distribution across all detector pixels in parallel using convolution operations, maintaining full Field of View coverage while achieving fast processing speeds through efficient computational algorithms
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 effectively enhances image quality by iteratively learning scatter information and removing scatter from dental cone-beam CT images, improving both image quality and processing efficiency.
Implementation Method 1
generating a 2D profile of projection image by Monte Carlo simulation for respective angles by use of the reconstructed 3-dimensional CT image
Implementation Method 2
building and doing learning of artificial neural network, wherein the objective function of the artificial neural network is primary image and scatter image which have been generated in simulation and wherein the input of the artificial neural network is the projection image which have been obtained in reality
Implementation Method 3
rotating X-ray source of cone-beam CT in a predetermined angle while obtaining CT images for respective angles with flat-panel detector
Implementation Method 4
the light has straightness and diffusivity, and the diffusivity of light causes scatter in CT photography
Data Source
AI summary
The present invention relates to scatter correction method and apparatus for dental cone-beam CT. An object of the present invention is improving quality of reconstructed images by processing the scatter correction by learning which uses Monte Carlo simulation and artificial neural network. In order to achieve this object, the scatter correction method is characterized in that the method comprises steps of: rotating X-ray source of cone-beam CT in a predetermined angle while obtaining CT images for respective angles with flat-panel detector so as to reconstruct 3-dimensional CT image; generating a 2D profile of projection image by Monte Carlo simulation for respective angles by use of the reconstructed 3-dimensional CT image; decomposing the 2D profile of projection image so as to separate primary x-ray image and scatter image, wherein the primary x-ray image is unscattered in reaching the detector and wherein the scatter image is generated only by the scatter; building and doing learning of artificial neural network, wherein the objective function of the artificial neural network is primary image and scatter image which have been generated in simulation and wherein the input of the artificial neural network is the projection image which have been obtained in reality; and storing the learning information for the artificial neural network and then applying the learning information to scatter correction.