Electromagnetic Imaging Using GNN Scattering Reconstruction
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Solution Overview
Problem
Existing electromagnetic imaging technologies, such as MRI and CT, are expensive, bulky, and time-consuming, while low-power electromagnetic imaging methods face challenges in spatial resolution and computational complexity, especially in differentiating ischemic strokes from healthy tissue, and require significant computational resources.
Innovation Solution
A computer-implemented process using a trained message-passing graph neural network (GNN) to analyze scattering data from an array of antennas, encoding antenna locations and scattering parameters as a graph, and generating image data by applying an attention mechanism and update functions to infer internal features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MRI and CT imaging modalities are used, then high image quality and diagnostic accuracy are achieved, but the equipment is expensive, bulky, and time-consuming
Solution Approach 1:
The patent replaces complex mechanical imaging systems (MRI, CT) with an electromagnetic field-based system using antenna arrays. Instead of using large magnetic resonance or X-ray equipment, the invention uses electromagnetic waves in the 100 MHz to 4 GHz range to penetrate tissue and collect scattering data, achieving portable, low-cost imaging while maintaining diagnostic capability
Solution Approach 2:
The patent creates a computational model that copies and simulates the complex imaging process through software algorithms. The graph neural network creates a virtual representation of internal tissue structures by processing electromagnetic scattering data, replacing the need for physical complex imaging hardware while achieving similar diagnostic outcomes
2Device complexity
If low-power electromagnetic imaging is used, then portability and affordability are improved, but spatial resolution and ability to differentiate ischemic strokes deteriorate
Solution Approach 1:
The patent transitions from traditional 2D image reconstruction to a 3D graph-based representation where antennas, tissue regions, and scattering interactions form a multi-dimensional graph structure. This allows the system to capture and process spatial relationships in three dimensions, improving resolution despite using low-power electromagnetic fields
Solution Approach 2:
The patent changes the frequency parameter of electromagnetic waves to the 100 MHz to 4 GHz range, which provides optimal penetration depth and spatial resolution for brain imaging. By adjusting frequency parameters and using graph neural networks to process the data, the system achieves differentiation of ischemic strokes with portable equipment
3Loss of information
If antenna arrays with dedicated transmit-receive channels are used, then complete scattering parameter matrices are collected, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex N×N scattering parameter matrix into meaningful graph components: nodes representing antennas and tissue regions, edges representing scattering interactions. This segmentation allows the graph neural network to process only relevant relationships rather than the entire data matrix, reducing computational complexity while preserving essential scattering information
Solution Approach 2:
The patent extracts and isolates the most informative scattering parameters from the complete matrix by creating a graph representation that focuses on significant tissue-antenna interactions. The graph structure extracts only the essential scattering data needed for imaging, eliminating redundant information and reducing computational burden
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 GNN-based approach efficiently generates accurate images of internal features, overcoming computational limitations and improving spatial resolution, particularly in differentiating ischemic and hemorrhagic strokes, with reduced computational requirements and data intensity.
Implementation Method 1
an array of antennas configured to define an imaging domain for receiving an object to be imaged
Implementation Method 2
scattering data representing measurements of electromagnetic wave scattering by internal features of an object
Data Source
AI summary
A computer-implemented process for electromagnetic imaging, the process including the steps of: accessing scattering data representing measurements of electromagnetic wave scattering by internal features of an object, each said measurement representing scattering of electromagnetic waves emitted by a corresponding antenna of an array of antennas disposed about an imaging domain containing at least a portion of the object, and as measured by a corresponding antenna of the array of antennas; and processing the scattering data to generate image data representing a spatial location and size of at least one internal feature of the object within the imaging domain; wherein the processing includes applying a trained message-passing graph neural network (GNN) to a graph of nodes representing spatial locations of the antennas and edges representing the measurements.