3D Medical Image Reconstruction From 2D X-Ray Using Neural Networks
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Solution Overview
Problem
Existing three-dimensional medical imaging techniques, such as CT and MRI, are inconvenient, expensive, and expose patients to significant radiation or suffer from long imaging times, limiting their widespread application.
Innovation Solution
A method utilizing a deep learning algorithm and neural network to convert two-dimensional X-ray images into three-dimensional reconstructions by incorporating characteristic parameters and geometric deformation considerations, employing convolutional and residual layers to synthesize features, and using a transposed convolutional layer for scale adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT scanning is used to obtain three-dimensional medical images, then imaging quality is improved, but patients are exposed to significant radiation doses
Solution Approach 1:
The patent uses AI-generated three-dimensional images as copies or simulations of actual CT scans. The system takes two-dimensional X-ray images and uses deep learning models to generate three-dimensional reconstructions that replicate the diagnostic quality of CT scans without requiring actual CT radiation exposure. This allows patients to receive high-quality three-dimensional imaging information without the harmful radiation effects of traditional CT scanning.
2Measurement precision
If MRI scanning is used to obtain three-dimensional medical images, then imaging quality is improved, but imaging time becomes much longer
Solution Approach 1:
The patent replaces the mechanical scanning process of MRI with an AI-based computational approach. Instead of using magnetic fields and radio waves to physically scan and reconstruct three-dimensional images (which takes minutes), the system uses deep learning neural networks to rapidly generate three-dimensional images from two-dimensional X-ray projections. This substitution of physical scanning with computational reconstruction dramatically reduces imaging time while maintaining diagnostic quality.
3Productivity
If traditional X-ray imaging is used, then imaging speed is fast, but three-dimensional information is not obtained
Solution Approach 1:
The patent transforms two-dimensional X-ray images into three-dimensional reconstructions by adding the depth dimension through AI processing. The deep learning model analyzes multiple two-dimensional projections and synthesizes them into a three-dimensional representation, effectively adding the missing spatial dimension while preserving the speed advantage of X-ray imaging. This dimensional transformation enables rapid acquisition of both speed and three-dimensional information.
Data Source
AI summary
A method for constructing three dimensional-medical image includes steps of inputting multiple two-dimensional images and a known three-dimensional image into a processing module and inputting a new two-dimensional image into the processing module to obtain a reconstructed three-dimensional image. The processing module utilizes a neural network to build a reconstructed three-dimensional image by unfolding the two-dimensional image to produce a three-dimensional reconstruction.