Spectral CT Hounsfield Unit Calibration Phantom
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Solution Overview
Problem
Conventional CT images produced by spectral CT technologies do not accurately replicate the gray scale values of conventional CT images due to dependency on object parameters, leading to biased results when using two basis functions for basis decomposition.
Innovation Solution
Incorporating a calibration measurement of a phantom with reference materials like water and air to adjust basis coefficients, allowing for accurate reproduction of ideal Hounsfield units using a smaller number of basis functions, thereby mimicking conventional CT systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If two basis functions are used for basis decomposition in spectral CT, then the computational complexity is reduced, but the gray scale values become biased and do not accurately reproduce ideal Hounsfield units
Solution Approach 1:
The patent applies preliminary action by performing a calibration measurement with a phantom containing reference materials (water, air, and other references) before processing patient data. This calibration step pre-determines correction factors that are then applied during actual image reconstruction, allowing accurate Hounsfield unit reproduction without requiring increased basis function count.
Solution Approach 2:
The patent changes parameters by introducing calibration-based correction factors that adjust the relationship between spectral CT measurements and Hounsfield units. By modifying the parameter mapping through calibration data from reference materials, the system achieves accurate gray scale reproduction while maintaining the computationally efficient two-basis-function approach.
2Measurement precision
If more basis functions are used to reduce bias in gray scale values, then the accuracy of Hounsfield unit reproduction improves, but the noise in the image increases
Solution Approach 1:
The calibration measurement performed in advance with phantom data pre-determines the optimal correction factors, eliminating the need to increase basis function count during actual patient scanning. This preliminary calibration step resolves the accuracy issue without exposing patients to increased noise.
Solution Approach 2:
The patent introduces calibration reference materials (water, air, and other references) as intermediaries between the spectral CT system and patient imaging. These reference materials provide a known reference framework that mediates the conversion from spectral measurements to accurate Hounsfield units, achieving precision without requiring additional basis functions that would increase noise.
3Object-affected harmful factors
If spectral CT technology is used to remove dependency on object parameters, then image quality improves, but the ability to mimic conventional CT gray scale values deteriorates
Solution Approach 1:
The patent changes the parameter mapping by introducing calibration-based correction factors that adjust spectral CT measurements to match conventional CT gray scale values. This parameter transformation allows the system to maintain the advantages of spectral CT (removing object parameter dependency) while reproducing familiar conventional CT Hounsfield units for radiologist interpretation.
Solution Approach 2:
The patent achieves multi-functionality by enabling the spectral CT system to perform both advanced spectral analysis (removing object parameter dependency) and conventional CT emulation (producing familiar gray scale values). The calibration framework allows the same system to serve both purposes simultaneously, bridging the gap between new technology and established clinical practice.
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 method enables the emulation of various system parameters of conventional CT systems, improving image quality and reducing noise, while accurately representing tissues in ideal Hounsfield units.
Implementation Method 1
The x-ray source emits x-rays, which pass through a subject or object to be imaged and are then registered by the detector. Since some materials absorb a larger fraction of the x-rays than others, an image is formed of the subject or object.
Data Source
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AI summary
There is provided a method and corresponding arrangement for reconstructing an image based on spectral image data acquired for at least two different effective energies. The method comprises obtaining (S1) a first set of spectral image data related to an object to be imaged and a second set of spectral image data related to a calibration phantom including at least one reference material, performing (S2) basis decomposition based on the first set of spectral image data to provide estimated basis images of the object to be imaged with respect to associated basis functions, performing (S3) basis decomposition based on the second set of spectral image data to provide calibrated estimates of reference basis coefficients corresponding to said at least one reference material, and determining (S4) image values representing the object to be imaged based on a system model of an imaging system to be emulated, the estimated basis images and their associated basis functions, and the calibrated estimates of reference basis coefficients.