Contrast Agent Dose Simulation via Spectral Decomposition
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Solution Overview
Problem
Current contrast-enhanced medical imaging techniques often require high doses of contrast agents to ensure diagnostic quality, which can lead to adverse patient side effects, limiting the optimization of scanning protocols and increasing the risk of adverse reactions.
Innovation Solution
A method to retrospectively simulate or create virtual images from existing contrast-enhanced data sets with reduced contrast agent doses by scaling contrast agent data and combining it with non-scaled non-contrast agent data, allowing for the visualization of how lower doses would affect image quality while minimizing patient exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high doses of contrast agents are administered to ensure diagnostic quality, then image quality and diagnostic visibility are improved, but adverse patient side effects increase
Solution Approach 1:
The system performs preliminary simulation of reduced-dose images before actual scanning by using spectral decomposition to create virtual images at different contrast agent doses. This allows clinicians to assess expected image quality at lower doses before administering the actual contrast agent, enabling optimized dose selection that maintains diagnostic quality while minimizing adverse effects.
Solution Approach 2:
The system changes the concentration parameter of contrast agent in simulated images by scaling the contrast agent-only data. By adjusting this parameter, the system generates virtual images at various dose levels (e.g., 50%, 75%, 100% of planned dose) to determine the optimal dose that maintains diagnostic visibility while reducing adverse effects.
2Measurement precision
If high doses of contrast agents are used to maintain diagnostic quality, then image quality is preserved, but optimization of scanning protocols is limited
Solution Approach 1:
The system performs preliminary simulation of reduced-dose images before actual scanning by using spectral decomposition to create virtual images at different contrast agent doses. This allows clinicians to assess expected image quality at lower doses before administering the actual contrast agent, enabling optimized dose selection that maintains diagnostic quality while minimizing adverse effects.
Solution Approach 2:
The system dynamically adjusts contrast agent dose simulations by scaling the contrast agent-only spectral data to represent different dose levels. This dynamic parameter adjustment allows flexible optimization of scanning protocols for different patient populations and clinical scenarios while maintaining diagnostic quality.
3Measurement precision
If high doses of contrast agents are administered to assure sufficient visibility, then contrast agent uptake visibility is improved, but the risk of adverse reactions increases
Solution Approach 1:
The system performs preliminary simulation of reduced-dose images before actual scanning by using spectral decomposition to create virtual images at different contrast agent doses. This allows clinicians to assess expected image quality at lower doses before administering the actual contrast agent, enabling optimized dose selection that maintains diagnostic quality while minimizing adverse effects.
Solution Approach 2:
The system replaces the need for physical trial-and-error dosing with a computational model that uses spectral decomposition and scaling to simulate different dose levels. This substitution of computational modeling for physical experimentation allows accurate prediction of image quality at reduced doses without exposing patients to unnecessary risk.
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
This approach enables clinicians to assess image quality with reduced contrast agent doses, mitigating adverse effects and maintaining diagnostic visibility, thereby optimizing scanning protocols without compromising image quality.
Implementation Method 1
Spectral (or multi-energy) CT utilizes multiple attenuation values acquired simultaneously at multiple different photon energies to solve for photoelectric effect, Compton scattering, and other component(s) (e.g., K-edge) contributions of the mass attenuation coefficient of a material.
Implementation Method 2
Spectral (or multi-energy) CT utilizes multiple attenuation values acquired simultaneously at multiple different photon energies to solve for photoelectric effect, Compton scattering, and other component(s) (e.g., K-edge) contributions of the mass attenuation coefficient of a material.
Data Source
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AI summary
A method includes obtaining a set of energy dependent data generated from a spectral scan. The set of energy dependent data includes a sub-set of data corresponding to only contrast agent. The method further includes separating the sub-set of data from other data of the energy dependent data. The other data includes non-contrast agent data. The method further includes scaling the sub-set of data to change a concentration of the contrast agent in the sub-set of data from that of the sub-set of data. The method further includes visually presenting at least the scaled sub-set of data.