Computed Tomography Reconstruction With Fewer Scan Projections
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Solution Overview
Problem
Traditional nondestructive evaluation systems are inadequate for additive manufacturing parts with complex geometries, as they require lengthy scan times and high costs, limiting the feasibility of computed tomography as a full-scale inspection solution.
Innovation Solution
The use of an iterative algebraic reconstruction technique, such as Simultaneous Iterative Reconstruction Technique (SIRT), which integrates prior knowledge data to reduce the number of scan images needed, allowing for faster CT scan times and maintaining reconstruction quality by utilizing a priori information during the reconstruction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional FDK algorithm is used for reconstruction, then accurate three-dimensional reconstruction can be achieved, but lengthy scan time is required to collect sufficient projection images
Solution Approach 1:
The system performs preliminary alignment and registration of prior knowledge data (such as CAD models or reference scans) before the actual CT scan. This pre-processing step enables the reconstruction algorithm to start with an initial model that is already close to the final result, reducing the number of projection images needed to achieve accurate reconstruction
Solution Approach 2:
The invention changes the reconstruction approach from traditional FDK to an iterative algebraic reconstruction technique (ART) that incorporates prior knowledge data. This parameter change in the reconstruction algorithm allows the system to achieve accurate three-dimensional reconstructions with fewer projection images, thereby reducing scan time while maintaining measurement precision
2Reliability
If full-scale computed tomography inspection is implemented, then comprehensive quality control can be achieved, but high cost and lengthy scan time limit feasibility
Solution Approach 1:
The system applies partial action by using only the necessary number of projection images required for accurate reconstruction, rather than collecting excessive data. By integrating prior knowledge data, the system determines the minimum sufficient dataset needed, reducing scan time and improving productivity while maintaining comprehensive quality control capability through the iterative reconstruction process
3Loss of time
If iterative algebraic reconstruction technique with prior knowledge data is used, then scan time can be reduced, but system complexity increases
Solution Approach 1:
The system introduces an intermediary alignment and registration module that bridges the prior knowledge data and the projection images. This intermediary component automatically aligns the prior knowledge model with the actual scan data, simplifying the overall system architecture by handling the complexity of data integration in a standardized manner, thus reducing scan time without excessively increasing system 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 results in a 3× to 10× reduction in CT scan time, significantly lowering the cost of inspection and qualification, while maintaining reconstruction quality similar to traditional methods, and can be implemented in any existing CT system setup.
Implementation Method 1
The CT system captures a set of projection images using a reduced number of projections during a scan time
Implementation Method 2
capturing X-ray projection images
Data Source
AI summary
A method for using computed tomography for non-destructive evaluation of a part, comprising: providing a computed tomography system; loading predetermined computed tomography system setup information; customizing settings with predetermined algorithmic parameters and functions depending on part features; processing projection counts; processing prior knowledge data relevant to the part; creating masking data from the customized settings; pre-processing reconstruction results based on the customized settings; performing algorithmic adjustments based on prior knowledge data and part geometry; performing algorithm adjustments during iterations; and post-processing reconstruction results.