Non-Contrast CT Functional Imaging with Radiomics and Virtual PET
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Solution Overview
Problem
Current PET-CT imaging techniques face limitations such as patient motion between scans, high costs, radiation exposure, local complications from radiotracers, and allergies, making it difficult to accurately superimpose functional and structural images.
Innovation Solution
A method to identify functional features from non-contrast CT images using radiomic analysis, generative adversarial networks, and classification models, eliminating the need for radiotracers and contrast agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PET-CT imaging is performed with radiotracers, then functional features can be detected, but patient motion between scans and registration accuracy deteriorate
Solution Approach 1:
The patent combines functional and structural imaging into a single CT scan procedure. By integrating both imaging modalities into one scan, patient motion between separate scans is eliminated, ensuring accurate co-registration while maintaining functional feature detection capabilities through radiomic analysis
Solution Approach 2:
The patent creates virtual PET images from CT data using deep learning techniques. Instead of requiring actual PET scanning with radiotracers, the system generates synthetic PET-like images that replicate functional information, thereby eliminating motion-related registration errors while preserving functional detection accuracy
2Measurement precision
If radiotracers are administered to patients, then functional imaging can be performed, but production costs and facility requirements increase
Solution Approach 1:
The patent uses CT scan data to generate virtual PET images through deep learning models. This copying approach replicates the functional imaging capability without requiring actual radiotracers, cyclotrons, or specialized PET facilities, making functional imaging accessible through standard CT scanners
Solution Approach 2:
The patent extracts functional information from standard CT scan data through radiomic analysis and deep learning. By separating functional feature extraction from the need for radiotracers, the system achieves functional imaging using only widely available CT technology
3Measurement precision
If radiotracers and contrast agents are injected into patients, then imaging quality improves, but local complications and allergic reactions increase
Solution Approach 1:
The patent extracts functional imaging capability from the radiotracer injection process. By obtaining functional information from standard CT data without injections, the system eliminates local complications from needle insertions and allergic reactions to radiotracers and contrast agents
Solution Approach 2:
The system creates virtual functional images from CT data, copying the functional imaging effect without the harmful side effects of radiotracer and contrast agent administration
Data Source
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AI summary
Methods, apparatus and computer readable media are provided for identifying functional features from a computed tomography (CT) image.. The CT image may be a contrast-enhanced CT image or a non-contrast CT image. According to some examples, methods, apparatus and computer readable media are also provided for using machine learning to identify functional features from CT images. According to some examples, simulated functional image datasets such as simulated PET images or simulated SUV images are generated from a received CT image.