X-Ray Scatter Correction Using Noisy Estimation and Interpolation
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Solution Overview
Problem
Existing imaging technologies, particularly x-ray-based methods, face challenges in achieving high image quality due to scattered x-ray photons, which cause artifacts like striping, cupping, and smudging, and current compensation methods, such as anti-scatter grids and deep learning, suffer from high computational complexity or dependence on training data, leading to suboptimal performance in real-world scenarios.
Innovation Solution
A method involving a neural network trained with a reduced number of high-quality and low-quality scattered radiation images, simulated using a scatter model, to correct x-ray recordings by estimating and interpolating scattered radiation, reducing computational complexity while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo simulation methods are used for scatter compensation, then measurement precision is improved, but device complexity increases due to high computational complexity
Solution Approach 1:
The patent creates simplified copies of the complex Monte Carlo simulation by training a neural network on a limited set of simulated scatter images. The neural network learns to replicate the scatter estimation function without requiring full Monte Carlo simulations during actual use, thus copying the essential functionality while reducing computational complexity.
Solution Approach 2:
The patent performs scatter simulations in advance to create a training dataset, then uses this pre-computed data to train a neural network. This preliminary action allows the system to capture complex scatter patterns beforehand, enabling fast real-time correction without repeated complex simulations.
2Productivity
If deep learning methods are used for scatter compensation, then productivity is improved through fast processing, but measurement precision deteriorates due to dependence on training data
Solution Approach 1:
The patent uses Monte Carlo simulations to generate accurate reference scatter images that serve as ground truth for training the neural network. This copying approach ensures the neural network learns from physically accurate data, improving measurement precision while maintaining fast processing speeds during inference.
3Device complexity
If the number of simulated scattered radiation images is reduced for training, then device complexity is reduced, but measurement precision may deteriorate due to insufficient training data
Solution Approach 1:
The patent applies partial action by simulating and using only a subset of scattered radiation images for training the neural network, rather than simulating all possible images. This reduces computational effort while the neural network generalizes effectively from this partial dataset to handle various imaging scenarios.
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 achieves high-quality x-ray imaging with reduced computational effort, enabling precise image correction and reconstruction, suitable for real-time interventions, by training the neural network with a smaller dataset and utilizing scatter models to simulate scattered radiation images.
Implementation Method 1
scattered x-ray photons play an important role in the image quality
Implementation Method 2
scattered rays drastically adversely affect the image quality
Data Source
AI summary
A method for training an algorithm for machine learning for a correction of recordings obtained by an imaging apparatus. A number of output images are recorded. Moreover a smaller number of first, high-quality scattered radiation images are simulated from the output images. A corresponding number of second, low-quality scattered radiation images is further simulated, wherein the simulation is undertaken with a number of photons reduced by at least an order of magnitude. The algorithm is trained with the second, low-quality scattered radiation images as input data and the first, high-quality scattered radiation images as output data.