CT Vessel Motion Compensation with Adaptive Parameter Tuning
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Solution Overview
Problem
Existing cardiac CT imaging systems face challenges in accurately compensating for cardiac motion due to variations in vessel length, curvature, and image acquisition protocols, leading to suboptimal motion estimation and compensation.
Innovation Solution
Adaptive parameter tuning is applied to customize the size of vessel region masks, the number and positions of control points, and motion estimation techniques based on unique patient-specific characteristics, optimizing the motion field for improved image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed parameter motion compensation methods are used, then the processing workflow is simple, but the motion estimation accuracy deteriorates due to variations in vessel length, curvature, and patient-specific characteristics
Solution Approach 1:
The patent implements dynamic parameter adaptation where mask sizes, control point configurations, and motion estimation parameters are automatically adjusted based on detected vessel characteristics such as length, curvature, and diameter. This transforms the static fixed-parameter approach into a dynamic system that adapts to patient-specific anatomy, resolving the contradiction between workflow simplicity and motion estimation accuracy.
Solution Approach 2:
The system changes multiple parameters including mask size, control point density, and motion estimation algorithm settings based on detected vessel features. By automatically adjusting these parameters according to the specific imaging scenario and patient anatomy, the system maintains high motion estimation accuracy without requiring manual intervention, thus preserving workflow simplicity while improving precision.
2Measurement precision
If adaptive parameter tuning is applied to customize parameters based on patient-specific characteristics, then the motion estimation accuracy is improved, but the computational load increases
Solution Approach 1:
The patent segments the motion compensation process into distinct stages: initial image reconstruction, vessel detection and characterization, parameter selection based on vessel features, and motion estimation using customized parameters. This segmentation allows the system to apply computationally intensive adaptive parameter tuning only where necessary (during motion estimation) while using simpler methods for initial processing, thereby reducing overall computational load while maintaining accuracy.
Solution Approach 2:
The system performs preliminary vessel detection and parameter selection before the computationally intensive motion estimation phase. By pre-determining the optimal parameters based on easily detectable vessel characteristics, the system avoids repeated parameter optimization during motion estimation, significantly reducing computational load while maintaining high accuracy.
3Manufacturing precision
If adaptive parameter tuning is applied to customize parameters for individual patients, then the image quality is improved, but the processing time increases
Solution Approach 1:
The patent performs vessel detection, characterization, and parameter selection as preliminary steps before the main motion compensation and image reconstruction phases. By determining optimal parameters in advance based on detected vessel features, the system avoids time-consuming iterative optimization during the main processing phase, thereby reducing overall processing time while maintaining high image quality through patient-specific parameter customization.
Data Source
AI summary
An apparatus for performing parameter adaptation for motion compensation in a computed tomography (CT) imaging system is provided. The apparatus includes processing circuitry configured to receive projection data acquired from imaging an object using the CT imaging system, reconstruct, based on the received projection data, an image of the object, without performing motion compensation, identify a vessel in the reconstructed image, the vessel including a plurality of vessel slices, determine, based on features of the identified vessel, parameters to be used during motion estimation of the identified vessel, estimate a vessel motion field using the determined parameters, and reconstruct, based on the received projection data and the estimated vessel motion field, a motion-compensated image of the object.