Z-effective Value Determination Using Sparse Multi-Energy CT Data
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Solution Overview
Problem
Current multi-energy imaging modalities, such as dual-energy CT scanners and line scanners, face challenges in accurately determining the effective atomic number (z-effective) of objects, especially in crowded environments, due to high costs, false alarm rates, and limited data resolution, which complicates the differentiation of threat items with similar densities but different atomic numbers.
Innovation Solution
A method and system that generate z-effective values for voxels in a CT density image using an iterative approach with sparse multi-energy projection data, combining CT density images with synthetic multi-energy projection data to refine estimates until they match measured data, thereby improving the accuracy of atomic number determination without requiring high-resolution data from energy-resolving detectors or multiple radiation sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dual-energy CT scanners are used to determine z-effective values, then measurement precision is improved, but device complexity and manufacturing cost increase due to requiring costly energy-resolving detectors and high voltage sources
Solution Approach 1:
The patent creates a virtual copy of the multi-energy measurement process through simulation. Instead of physically measuring multi-energy projections with complex detectors, the system simulates what the measurements would be based on the CT density image and estimated z-effective values, then iteratively refines the estimates to match the sparse real measurements.
Solution Approach 2:
The patent introduces an intermediary simulation process that bridges the gap between simple CT density measurements and the desired z-effective values. The simulation acts as a mediator that translates density information into multi-energy projection data, which is then compared with sparse real measurements to derive z-effective values without requiring complex direct measurement hardware.
2Device complexity
If dual-energy line scanners are used to reduce cost, then device complexity is reduced, but measurement precision deteriorates due to limited views and higher false alarm rates
Solution Approach 1:
The patent performs preliminary action by first acquiring a complete CT density image that contains all necessary information for z-effective determination. This preliminary full-volume imaging allows subsequent iterative refinement without needing multiple limited views, as the simulation can generate projections from any angle based on the complete 3D density data.
Solution Approach 2:
The patent replaces the mechanical limitation of limited scanner views with a computational approach. Instead of physically acquiring data from multiple angles with a line scanner, the system uses iterative simulation and optimization to mathematically determine z-effective values from the CT density image and sparse measurements, substituting mechanical complexity with computational processing.
3Device complexity
If sparse multi-energy projection data is used instead of high-resolution data, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent implements a feedback loop where estimated z-effective values are used to simulate multi-energy projections, which are then compared with sparse real measurements. The difference between simulated and measured data feeds back into updating the z-effective estimates, iteratively improving accuracy despite using sparse input data.
Solution Approach 2:
The patent uses partial action by acquiring only sparse multi-energy projection data rather than complete high-resolution multi-energy datasets. The iterative simulation process compensates for the sparsity by using the CT density image to fill in missing information and refine estimates, achieving sufficient precision with less data acquisition complexity.
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 allows for accurate determination of z-effective values with reduced computational complexity, enhancing the ability to differentiate between objects based on atomic number rather than just density, thereby improving detection performance and reducing false alarms in security and medical imaging applications.
Implementation Method 1
the object is exposed to radiation photons (e.g., such as X-rays, gamma rays, etc.)
Implementation Method 2
an image(s) is formed based upon the radiation absorbed and/or attenuated by the interior aspects of the object
Data Source
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AI summary
Z-effective (e.g., atomic number) values are generated for one or more sets of voxels in a CT density image using sparse (measured) multi-energy projection data. Voxels in the CT density image are assigned a starting z-effective value, causing a CT z-effective image to be generated from the CT density image. The accuracy of the assigned z-effective values is tested by forward projecting the CT z-effective image to generate synthetic multi-energy projection data and comparing the synthetic multi- energy projection data to the sparse multi-energy projection data. When the measure of similarity between the synthetic data and the sparse data is low, the z-effective value assigned to one or more voxels is modified until the measure of similarity is above a specified threshold (e.g., with an associated confidence score), at which point the z-effective values substantially reflect the z-effective values that would be obtained using a (more expensive) dual-energy CT imaging modality.