Neural Network Cone-Beam Artifact Reduction in CT Imaging
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Solution Overview
Problem
Computed tomography (CT) scans, particularly cone-beam CT, suffer from imaging artifacts that degrade image quality, hindering clinical applications.
Innovation Solution
A medical image processing apparatus and method using a trained neural network to reduce cone-beam artifacts by separating images into low-frequency and high-frequency components, with the neural network trained on pairs of images with and without artifacts, and applying these models to correct images with cone-beam artifacts during scanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional CT reconstruction methods are used, then the imaging process is simple and fast, but imaging artifacts appear that degrade image quality
Solution Approach 1:
The system performs preliminary actions by acquiring additional projection data at different cone angles during the scanning process, and pre-trains multiple neural network models for different anatomical structures and scanning protocols before actual image reconstruction, so that artifact correction can be applied efficiently during scanning
Solution Approach 2:
The patent introduces trained neural network models as intermediaries between the raw projection data and the final reconstructed image. These models act as mediators that process the projection data to eliminate cone-beam artifacts while preserving diagnostic image quality, resolving the contradiction between simple reconstruction and artifact-free images
2Manufacturing precision
If deep learning frameworks are integrated to correct cone-beam artifacts, then image quality improves, but computational time and hardware costs increase
Solution Approach 1:
The patent segments the image correction task by training separate neural network models for different anatomical structures (head, body, pelvis) and different scanning protocols. This segmentation allows the system to apply only the necessary model for each specific case, reducing overall computational time compared to using a single comprehensive deep learning framework
Solution Approach 2:
The system changes parameters by selecting different pre-trained models based on the specific anatomical structure and scanning protocol being used. This parameter-based model selection optimizes computational efficiency by matching the correction algorithm to the specific imaging conditions, rather than applying a fixed complex deep learning framework to all cases
3Manufacturing precision
If a single trained model is used for all scanning conditions, then the system is simple to operate, but accuracy decreases for specific anatomical structures or protocols
Solution Approach 1:
The patent applies local quality by training separate neural network models optimized for specific anatomical structures (head, body, pelvis) and specific scanning protocols. Each model has specialized quality characteristics tailored to its designated application, ensuring high correction accuracy for each specific case rather than using a generic single model
4Manufacturing precision
If multiple trained models are maintained for different parameters, then correction accuracy for specific cases improves, but device complexity and memory requirements increase
Solution Approach 1:
The system achieves universality by designing a modular architecture where multiple trained models can be stored and selected based on the specific scanning conditions. The framework is designed to handle different anatomical structures and protocols through a unified interface, allowing the same system to serve multiple specialized functions without requiring separate hardware for each model
Data Source
Figure 1A~1B
Figure 2A
Figure 2B
AI summary
According to one embodiment, a medical image processing apparatus includes a processor and an acquirer. The processor inputs, to a trained model trained based on a first image acquired with a first x-ray beam having a first cone angle and a second image including a cone-beam artifact and acquired using simulation based on the first image, a third image including a cone-beam artifact and acquired with a second x-ray beam having a second cone angle larger than the first cone angle to generate a fourth image corresponding to the third image with a reduced cone-beam artifact. The acquirer acquires the third image.