CT Perfusion Imaging via Pseudo-PET Clustering Analysis
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Solution Overview
Problem
Conventional CT imaging systems generate perfusion viability maps with noise, making it difficult for physicians familiar with PET imaging to assess cardiac perfusion, and the color schemes and indicia used are different from those in PET and SPECT systems, complicating diagnosis.
Innovation Solution
A method and system for classifying myocardium voxels into viable and non-viable clusters based on density and location using a CT imaging system, generating images that mimic PET-like perfusion viability maps by converting Hounsfield units to pseudo-PET values, allowing for easier comparison with nuclear medicine images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional CT imaging systems generate perfusion viability maps, then functional information about blood flow through microvasculature is obtained, but the maps include noise that reduces physician's ability to measure or evaluate perfusion
Solution Approach 1:
The patent creates a pseudo-PET image that copies the visual appearance and color scheme of conventional PET perfusion images while being generated from CT data. This allows physicians familiar with PET imaging to interpret CT-derived perfusion information without the noise problems inherent in conventional CT perfusion maps.
Solution Approach 2:
The patent transforms Hounsfield units from CT imaging into pseudo-PET values through clustering analysis and parameter transformation. This changes the parameter scale and representation to match PET imaging conventions, improving both image quality and physician interpretability.
2Adaptability or versatility
If conventional CT imaging systems generate perfusion maps, then perfusion information is provided, but the colors and indicia are different from PET and SPECT systems making it difficult for physicians to assess cardiac perfusion
Solution Approach 1:
The patent generates pseudo-PET images that replicate the color schemes, indicia, and visual characteristics of conventional PET and SPECT perfusion images. This copying approach ensures that physicians can interpret the images using their existing knowledge without needing to learn new visualization conventions.
Solution Approach 2:
The patent makes CT imaging data serve multiple functions by transforming it into a format that fulfills both the diagnostic capabilities of CT and the familiar visualization requirements of PET/SPECT imaging, creating a universal image that works across different imaging modalities.
3Measurement precision
If conventional MPI technique is implemented using PET or SPECT imaging systems, then perfusion measurements are obtained, but scan times are increased and radiation exposure to the subject increases
Solution Approach 1:
The patent replaces the nuclear medicine-based PET/SPECT imaging mechanism with CT-based imaging combined with contrast agent enhancement and clustering analysis. This substitution maintains perfusion measurement precision while utilizing CT's faster acquisition speed and reducing radiation exposure.
Solution Approach 2:
The patent transforms CT Hounsfield unit data into pseudo-PET values through parameter transformation and clustering, enabling CT data to represent perfusion information in a format equivalent to PET measurements but acquired much faster with lower radiation dose.
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 solution improves image clarity and consistency, enabling physicians to diagnose cardiac perfusion more effectively while reducing radiation exposure by generating images that are comparable to conventional PET images, thus facilitating faster and more accurate assessments.
Implementation Method 1
a Computed Tomography (CT) imaging system. The CT imaging system includes an x-ray source, a detector configured to receive x-rays from the x-ray source
Data Source
AI summary
A method of displaying image data for a tissue of an organ includes acquiring a three-dimensional (3D) projection dataset using a Computed Tomography (CT) imaging system, performing a segmentation of the 3D projection dataset that includes a plurality of voxels, performing a perfusion viability cluster analysis to identify myocardium voxels, grouping the myocardium voxels into viable clusters and non-viable clusters based on a density and a location of the myocardium voxels, and generating an image of the myocardium and a coronary tree using the viable clusters and the non-viable clusters. An imaging system and a non-transitory computer readable medium are also described herein.