LLE Motion Correction for Helical Photon-Counting CT Artifacts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Motion-induced image artifacts in X-ray computed tomography (CT) scans, particularly in head and cardiac CT, are exacerbated by the use of X-ray photon-counting detectors (PCDs) due to their complex manufacturing process, which results in a significant number of ineffective pixels and gaps, making traditional motion estimation and correction challenging, especially in helical scans with extended longitudinal fields of view.
Innovation Solution
A method utilizing locally linear embedding (LLE) motion correction algorithm for helical photon-counting CT, which decomposes motion correction into six sub-problems, iteratively solves and updates these sub-problems using incremental updating and refined sampling grids, applies bad pixel masking, unreliable volume masking, and virtual static object removal to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If photon-counting detectors are used to advance CT technology, then image quality and spectral characterization are improved, but the number of bad pixels increases making motion estimation challenging
Solution Approach 1:
The patent extracts and removes bad pixels from the detector array by creating a mask that identifies and excludes ineffective pixels during data processing. This allows the system to maintain the high spectral characterization capabilities of photon-counting detectors while compensating for their reliability issues through computational methods.
Solution Approach 2:
The patent changes the processing parameters by using iterative optimization algorithms that adaptively adjust reconstruction parameters based on the presence of bad pixels. The system modifies the projection data and reconstruction parameters to accommodate detector unreliability while maintaining image quality.
2Volume of moving object
If helical scan is performed for extended longitudinal field of view, then coverage is improved, but motion estimation becomes more difficult
Solution Approach 1:
The patent segments the helical scan data into multiple projection sets corresponding to different angular positions. By dividing the complex helical trajectory into discrete, manageable segments, the system can apply motion correction algorithms to each segment independently, reducing the overall complexity while maintaining extended field of view coverage.
Solution Approach 2:
The patent transforms the motion correction problem from a three-dimensional helical trajectory into a series of two-dimensional projection corrections. By working in the projection domain rather than directly in the image domain, the system simplifies the mathematical complexity while preserving the extended longitudinal field of view.
3Productivity
If traditional analytical reconstruction is applied to data with high bad pixel rates, then processing speed is maintained, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent implements an iterative feedback loop where the reconstruction algorithm continuously adjusts parameters based on the quality of the projection data. The system uses feedback from bad pixel identification to dynamically modify the reconstruction process, maintaining both speed and accuracy by only performing necessary computational steps.
Solution Approach 2:
The patent applies partial correction by focusing computational resources only on the regions and projections affected by bad pixels, rather than processing the entire dataset uniformly. This selective approach maintains processing speed while improving accuracy in the most critical areas.
4Reliability
If bad pixel masking is applied to exclude ineffective pixels, then data quality is improved, but computational steps increase
Solution Approach 1:
The patent performs preliminary bad pixel identification and mask generation before the main reconstruction process. By pre-processing the detector data to create a comprehensive mask of ineffective pixels, the system avoids repeated computational checks during reconstruction, reducing overall complexity while ensuring data quality.
Data Source
AI summary
A method of motion correction image reconstruction for photon-counting CT images includes scanning a subject via a photon-counting CT scanner device to obtain measured projection data; performing, via motion correction circuitry of the motion correction system, a LLE motion correction algorithm on the measured projection data to obtain motion correction data; generating, via reconstruction circuitry of the motion correction system, reconstructed image data from the motion correction data; and outputting corrected image data based, at least in part, on the reconstructed image data. A binary bad pixel mask is applied to exclude contributions from the bad pixels to the measured projection data. An unreliable volume mask is applied to exclude contributions from X-ray beams that passed through unreliable portions of the reconstructed image data. A virtual static object removal algorithm is performed to remove a static object from the measured projection data.