CT Perfusion Imaging Beam Hardening Correction
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Solution Overview
Problem
Conventional CT perfusion imaging is affected by beam hardening artifacts, leading to erroneous results in calculating perfusion parameters like rCBF and rCBV due to distorted images caused by the assumption of a mono-energetic X-ray source, which is not accurate for clinical scanners, especially when substantial bone is present.
Innovation Solution
The method involves decomposing projection data into energy-dependent components, such as photo-electric and Compton components, and administering agent-based imaging procedures to generate agent-based volumetric image data, reducing beam hardening artifacts by accounting for the actual energy dependence, using a system that can switch between different emission voltages for multi-energy acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simplified assumption of a mono-energetic X-ray source is made for image reconstruction, then the reconstruction process is simplified, but beam hardening artifacts occur causing image distortion and erroneous perfusion parameter calculations
Solution Approach 1:
The patent applies parameter changes by transitioning from a simplified mono-energetic X-ray source model to a multi-energy spectrum model that accurately represents clinical CT scanners. This involves changing the energy parameters of the X-ray source to include multiple energy levels, which resolves beam hardening artifacts while maintaining reconstruction feasibility through updated attenuation coefficients and energy-dependent calculations.
2Productivity
If conventional image reconstruction methods are used, then the processing is computationally efficient, but beam hardening artifacts distort the images and affect contrast enhancement measurements
Solution Approach 1:
The patent applies preliminary action by performing energy-dependent decomposition of the X-ray spectrum and calculating energy-weighted attenuation coefficients before the main image reconstruction process. This pre-processing step prepares corrected projection data that accounts for beam hardening effects, enabling accurate perfusion parameter calculations without significantly increasing overall computation time.
3Measurement precision
If multi-energy acquisitions with different emission voltages are implemented, then beam hardening artifacts are reduced, but the acquisition time and system complexity increase
Solution Approach 1:
The patent applies partial action by implementing a simplified multi-energy approach that uses only two emission voltages (e.g., 80 kV and 140 kV) rather than continuous multi-energy sampling. This partial implementation provides sufficient beam hardening correction for perfusion imaging while minimizing additional acquisition time and system complexity compared to full spectral CT methods.
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 generation of quantitative maps and reduction of beam hardening artifacts, providing reliable cerebral blood flow and volume measurements by isolating agent-based components and mitigating the distortion caused by anatomical structures.
Implementation Method 1
A radiation source 110, such as an x-ray tube, is supported by and rotates with the rotating gantry 104 around the examination region 106. The radiation source 110 emits radiation
Implementation Method 2
A radiation sensitive detector array 114 is also supported by the rotating gantry 104 and subtends an arc across from the radiation source 110, opposite the examination region 106. The detector array 114 detects radiation that traverses the examination region 106 and generates projection data indicative thereof
Implementation Method 3
A processor or projection data decomposer 118 decomposes the projection data into different energy-dependent components. As described in greater detail below, in one instance the decomposer 118 decomposes the projection data into photo-electric and Compton components
Implementation Method 4
the decomposer 118 decomposes the projection data into photo-electric and Compton components
Data Source
Figure 1
Figure 2~3
AI summary
A method includes decomposing, with a decomposer (118), agent-based time series projection data for an object or a subject into at least an agent based component. A projection data decomposer (118) includes a time series decomposer (204) that determines agent-based projection data based on agent-based time series projection data based on at least two energy dependent components. A computer readable storage medium containing instructions which, when executed by a computer, cause the computer to perform the act of: determining an agent-based component of agent-based time series projection data utilizing at least two components of the agent-based time series projection.