X-Ray Scatter Kernel Correction for CBCT Hardware Artifacts
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Solution Overview
Problem
X-ray scatter caused by hardware components of imaging systems degrades the quality of CBCT 2D projection images, leading to visual artifacts and inaccuracies in reconstructed anatomical regions, which affects the accuracy of target volume detection during radiation therapy.
Innovation Solution
A model-based correction method using scatter-estimate kernels is employed to estimate and remove the scatter component contributed by individual hardware-related sources in X-ray projection images, involving convolution of physics kernels with the acquired projection images to generate a corrected image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional CBCT imaging is used to obtain projection images, then the imaging process is simple and fast, but X-ray scatter from hardware components degrades image quality and introduces visual artifacts
Solution Approach 1:
The patent segments the scatter estimation process by creating separate scatter-estimate kernels for different hardware components (collimator, bow-tie filter, detector cover, etc.). Each kernel models the scatter contribution from a specific component, allowing the total scatter to be estimated as a combination of individual component contributions. This segmentation enables accurate scatter correction while maintaining computational efficiency.
Solution Approach 2:
The patent performs preliminary action by pre-calculating and storing scatter-estimate kernels for various hardware components before actual imaging. These kernels are computed based on Monte Carlo simulations and stored in a database, so that during CBCT imaging, scatter can be rapidly estimated by convolving the acquired projection images with the pre-computed kernels, maintaining fast imaging speed while achieving accurate scatter correction.
2Measurement precision
If scatter correction is applied to CBCT images, then image quality improves, but the processing complexity and computational time increase
Solution Approach 1:
The patent uses copying by creating scatter-estimate kernels that replicate the scatter patterns produced by each hardware component. Instead of performing complex real-time simulations during imaging, the system creates simplified kernel representations of scatter distributions from Monte Carlo simulations, then convolves these kernels with the acquired images. This copying approach maintains accuracy while dramatically reducing computational complexity.
Solution Approach 2:
The patent applies parameter changes by transforming the scatter correction problem from a complex iterative optimization problem into a straightforward convolution operation. By changing the mathematical approach from solving integral equations to performing kernel convolution, the system achieves accurate scatter correction with minimal computational overhead and processing complexity.
3Manufacturing precision
If hardware components are added to shape and control the X-ray beam, then beam quality and targeting precision improve, but these same components become sources of X-ray scatter that degrade image quality
Solution Approach 1:
The patent converts the harmful scatter effect into a beneficial correction by measuring and modeling the scatter contribution from each hardware component. The same collimator, bow-tie filter, and detector cover that produce scatter are individually characterized through Monte Carlo simulations, and their scatter patterns are stored as kernels. During imaging, these kernels are used to estimate and subtract the scatter contribution, effectively converting the harmful effect into a correctable artifact that can be removed to improve image quality.
Solution Approach 2:
The patent introduces an intermediary element - the scatter-estimate kernel - that mediates between the hardware components and the final image. Rather than directly dealing with the complex scatter interactions from multiple components, the system uses pre-computed kernels as intermediaries to represent and model the scatter contribution from each component. This intermediary approach simplifies the correction process while maintaining accuracy in removing hardware-related scatter.
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
The method improves the accuracy of CBCT image reconstruction by reducing hardware-related scatter, enhancing image quality and enabling precise detection of target volumes during radiation therapy.
Implementation Method 1
generating an initial X-ray projection image of an object with an imaging beam
Implementation Method 2
estimating a scatter component of the initial X-ray projection image... generating a corrected X-ray projection image by removing the scatter component
Implementation Method 3
Estimating scatter in x-ray images caused by imaging system components using kernels based on beam hardening
Data Source
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AI summary
A computer-implemented method of reducing scatter in an X-ray projection image of an object, the method comprising: generating an initial X-ray projection image of an object with an imaging beam produced by an imaging system; based on a first transmission indicator for the object and on a second transmission indicator for at least one element of the imaging system, selecting (901) a kernel for convolution of the initial projection image; convolving (903) the initial X-ray projection image with the kernel to generate a scatter component of the initial X-ray projection image; and generating (904) a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.