Couch-Bending Correction in CBCT Reconstruction
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Solution Overview
Problem
Existing computed tomography (CT) systems face issues with patient support deflection due to cantilever configurations, leading to unintended vertical deflections and mechanical slippage, which affect image accuracy during scanning.
Innovation Solution
The method involves obtaining projection data at multiple positions of the patient support, reconstructing volumetric images, and determining a corrected image by merging and adjusting these images to correct for deflection, using a processor to align them with a topogram for optimal correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the patient support is translated further away from its base during imaging procedure, then the imaging coverage is improved, but the vertical deflection at the free end increases due to cantilever configuration
Solution Approach 1:
The system performs preliminary measurements of the patient support's deflection state before and during the imaging procedure. By pre-characterizing the cantilever behavior and measuring actual deflection in advance, the system can calculate correction factors that compensate for the vertical deflection occurring during translation, thus maintaining positioning accuracy while allowing extended imaging coverage.
Solution Approach 2:
The system continuously monitors the patient support's position and deflection state during the imaging procedure, using sensors to detect actual deviations from the expected trajectory. This feedback information is fed back to the reconstruction system, which adjusts the geometric corrections in real-time to account for the cantilever deflection, ensuring accurate image reconstruction despite the changing mechanical state of the support.
2Ease of operation
If mechanical components are used to support the patient during translation, then the patient positioning is improved, but mechanical slippage and strains cause unintended deflection
Solution Approach 1:
The system replaces direct reliance on mechanical stability with computational correction methods. Instead of depending on the mechanical rigidity of the patient support to maintain perfect positioning, the system uses image processing and computational algorithms to calculate and correct for the deflection caused by mechanical slippage and strains, thereby achieving reliable positioning despite mechanical imperfections.
Solution Approach 2:
The system changes the parameters used in the image reconstruction process to account for mechanical deformations. By measuring the actual deflection and slippage parameters and incorporating them as correction factors in the reconstruction algorithm, the system compensates for the effects of mechanical instability, maintaining image accuracy despite changes in the mechanical state of the patient support.
3Length of moving object
If the cantilever section of the patient support is lengthened during translation, then the translation range is improved, but the vertical deflection increases
Solution Approach 1:
The system pre-calculates the expected deflection profile for the cantilever section based on its length, material properties, and loading conditions before the imaging procedure. By having this pre-characterized deflection profile, the system can apply appropriate geometric corrections during reconstruction, allowing the cantilever to be lengthened for extended translation range while maintaining accurate support geometry through computational compensation.
Data Source
AI summary
A method for correcting a volumetric image to address error due to deflection of a patient support is provided. The method includes obtaining a first set of projection data and a second set of projection data. The first and second sets of projection data are generated when the patient is at a first position and a second position, respectively, and are usable to reconstruct a first volumetric image and a second volumetric image, respectively. A corrected volumetric image is then determined based on the first and second sets of projection data.