CT Projections Pi-Line Optimization for Misalignment Correction
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Solution Overview
Problem
Computed Tomography (CT) systems face challenges in achieving geometric alignment, sample motion correction, and intensity normalization due to time-dependent misalignment and source intensity variations, which degrade image quality and require high computational overhead or precise mechanics.
Innovation Solution
The method employs pi-line optimization to correct for misalignment and intensity inconsistencies by acquiring and analyzing projections from different angles, determining pairs of opposing projections, and adjusting the scanning trajectory to minimize inconsistencies in pi-line data, allowing for efficient alignment and correction of both static and time-dependent errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CT scanning methods are used, then image acquisition is straightforward, but geometric misalignment and intensity variations degrade reconstruction quality
Solution Approach 1:
The patent performs preliminary alignment and calibration actions before the main imaging acquisition. A calibration scan is executed first to determine transformation parameters that correct for geometric misalignment and intensity variations. These pre-determined parameters are then applied during subsequent imaging scans to maintain consistent alignment without requiring complex real-time corrections.
Solution Approach 2:
The patent implements a feedback mechanism where the system measures actual geometric deviations and intensity variations during scanning, then uses this information to adjust and correct the scanning trajectory and projection data. The calibration process provides feedback on system performance that is used to refine alignment parameters and compensate for time-dependent variations.
2Measurement precision
If high computational overhead methods are used to correct misalignment, then alignment accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs computationally intensive alignment calculations in advance during a calibration phase. Transformation parameters and correction factors are pre-computed and stored for rapid application during actual imaging. This shifts the computational burden from real-time processing to a preliminary setup phase, maintaining high alignment accuracy while reducing processing time during scanning.
Solution Approach 2:
The patent creates a simplified mathematical model or representation of the geometric misalignment and intensity variations based on calibration data. This model serves as a copy or approximation of the complex physical deviations, allowing fast computational correction without requiring full complex simulations during image acquisition and processing.
3Reliability
If extremely precise mechanics are used to maintain alignment, then geometric consistency improves, but system complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical precision requirements with computational correction methods. Instead of relying on extremely precise mechanical positioning systems to maintain geometric consistency, the system uses software-based alignment algorithms that calculate and apply transformation parameters to correct for mechanical imperfections and variations in the scanning trajectory.
Solution Approach 2:
The patent changes the approach from maintaining fixed mechanical parameters to dynamically adjusting computational parameters. The system allows for variations in mechanical positioning but compensates by adjusting transformation parameters, intensity normalization factors, and alignment corrections based on measured deviations from the ideal scanning trajectory.
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 improves the quality of CT reconstructions by achieving sub-pixel accuracy and reducing computational overhead, effectively addressing misalignment and intensity variations, leading to higher symmetry and consistency in scanning trajectories.
Implementation Method 1
a source to provide x-rays, a detector to detect x-rays after having passed through a sample
Data Source
AI summary
Methods and apparatuses are disclosed herein to correct for inconsistencies in CT scans based on pi-lines. An example method at least includes acquiring a plurality of projections of a sample, each projection of the plurality of projections acquired at a different location around the sample based on a trajectory, determining pairs of opposing projections from the plurality of projections based on a respective pi-line, and determining an amount of inconsistency between respective pi-line data for each pair of opposing projections, where the pi-line data is based, at least in part, on attenuation data.