Material Decomposition Optimization in Image Domain
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Solution Overview
Problem
Current CT imaging systems require complex and costly methods for material decomposition, often involving multiple scans and energy-sensitive detectors, which can be inefficient and increase operational complexity.
Innovation Solution
A method and system for optimizing material decomposition exclusively in the image domain using random perturbation probabilities and prior knowledge of material concentrations, allowing for the optimization of material concentrations in voxels based on neighboring voxel probabilities and known material probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If energy-sensitive detectors or multiple scans are used for material decomposition, then measurement precision is improved, but device complexity and operational cost increase
Solution Approach 1:
The patent uses conventional scintillator-based detectors to capture x-ray attenuation data at different kVp levels, creating multiple copies of the same physical detector system operating at different energy states. This avoids the need for complex energy-sensitive detectors while obtaining the spectral information needed for material decomposition through image domain optimization
Solution Approach 2:
The patent changes the operating parameter (peak kilovoltage kVp) of the x-ray tube to acquire projections at different energy levels (e.g., 80 kVp and 140 kVp). This parameter change allows conventional detectors to capture energy-dependent attenuation data, enabling material decomposition without requiring complex energy-sensitive detector hardware
2Measurement precision
If multiple scans at different kVp levels are performed, then material decomposition accuracy is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary acquisition of projections at multiple kVp levels during a single scan or interleaved rotations, preparing all necessary data in advance. The computationally intensive material decomposition and optimization are then performed in the image domain using pre-acquired data, reducing the need for multiple sequential scans and minimizing scan time
3Measurement precision
If energy-sensitive detectors are used to separate photon energies, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent uses the x-ray tube's variable kVp capability as an intermediary to create different energy spectra, and uses computational algorithms in the image domain as a mediator to separate and analyze materials. This approach achieves energy discrimination without requiring complex energy-sensitive detector hardware, maintaining operational simplicity with conventional CT systems
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 enhances the efficiency and accuracy of material decomposition, reducing the need for multiple scans and complex energy-sensitive detectors, while improving the quality of diagnostic images by optimizing material concentrations in the image domain.
Implementation Method 1
a scintillator for converting x-rays to light energy adjacent the collimator, and photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom
Implementation Method 2
photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom
Implementation Method 3
The intensity of the attenuated beam radiation received at the detector array is typically dependent upon the attenuation of the x-ray beam by the subject
Data Source
AI summary
A system and method for material decomposition optimization in the image domain include a non-transitory computer readable medium has stored thereon a sequence of instructions which, when executed by a computer, causes the computer to access a reconstructed basis material image. For a first voxel of the reconstructed basis material image, the instructions also cause the computer to optimize a concentration of a pair of materials (a,b) in the first voxel exclusively in the image domain and based on a first probability based on random perturbations and a second probability based on random perturbations. The optimization is further based on a third probability based on known materials and a fourth probability based on concentrations of the pair of materials in a pair of voxels neighboring the first voxel.