Neural Network Infers Spectral CT Data from Non-Spectral Scans
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Solution Overview
Problem
Non-spectral computed tomography (CT) scanners lack the ability to generate spectral volumetric image data, which is essential for elemental or material composition analysis, due to increased cost and complexity from additional hardware and data processing requirements in spectral CT scanners.
Innovation Solution
A non-spectral CT scanner equipped with a radiation source, detector array, and a neural network module that processes non-spectral data to produce spectral volumetric image data, trained with both spectral and non-spectral data to infer spectral information without additional hardware like multi-layer detectors or kVp switching circuitry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If additional detector layers, x-ray tubes, or kVp switching circuitry are added to enable spectral imaging, then spectral information capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical spectral imaging hardware (multi-layer detectors, multiple x-ray tubes, kVp switching circuitry) with a software-based neural network system. The neural network is trained on spectral data and then applied to non-spectral CT data to generate spectral information, substituting mechanical/electrical complexity with computational processing.
Solution Approach 2:
The patent creates a computational model (neural network) that copies the spectral imaging capability without requiring actual spectral imaging hardware. The network learns spectral patterns from training data and reproduces spectral information from non-spectral input, effectively copying the functional capability without the physical infrastructure.
2Loss of information
If additional detector layers, x-ray tubes, or kVp switching circuitry are added to enable spectral imaging, then spectral information capability is improved, but system cost increases
Solution Approach 1:
The patent replaces expensive spectral imaging hardware with a software-based neural network solution. This substitution eliminates the need for costly multi-layer detectors, additional x-ray tubes, and kVp switching circuitry, significantly reducing system manufacturing costs while maintaining spectral information capability.
Solution Approach 2:
The patent uses a computational model (neural network) that can be trained once and applied indefinitely to non-spectral CT data. This approach is much cheaper than acquiring and maintaining multiple physical spectral imaging systems, providing a cost-effective solution that can be deployed across existing non-spectral CT scanners.
3Loss of information
If spectral CT scanner hardware is used, then spectral volumetric image data can be generated, but data acquisition and processing complexity increase
Solution Approach 1:
The patent performs preliminary training of the neural network using spectral data from spectral CT scanners. Once trained, the network can process non-spectral data without requiring the complex hardware acquisition systems. This preliminary action separates the complex training phase from the routine application phase, simplifying ongoing data acquisition and processing.
Solution Approach 2:
The patent replaces complex spectral data acquisition hardware and processing pipelines with a neural network that processes standard non-spectral CT data. This substitution eliminates the need for specialized data acquisition protocols and simplifies the processing workflow while still generating spectral volumetric image data.
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
Enables the generation of spectral volumetric image data from non-spectral data, reducing system costs and complexities while maintaining accurate elemental or material composition analysis, by leveraging neural networks to extract hidden spectral information.
Implementation Method 1
a processor configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data
Implementation Method 2
an x-ray tube rotates around an examination region located between the x-ray tube and the detector array, and emits polychromatic radiation that traverses the examination region
Implementation Method 3
The detector array detects radiation that traverses the examination region and generates projection data
Data Source
AI summary
A non-spectral computed tomography scanner includes a radiation source configured to emit x-ray radiation, a detector array configured to detect x-ray radiation and generate non-spectral data, and a memory configured to store a spectral image module that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.