Catheter Shape Modeling in Fluoroscopic Imaging
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Solution Overview
Problem
The detection of catheters in fluoroscopic images is challenging due to varying imaging quality, making it difficult for medical professionals to accurately identify and model catheters during procedures.
Innovation Solution
A method and system that detect catheter tip and body candidates using trained shape models, Probabilistic Boosting Trees, and principle component analysis to improve detection accuracy by fitting models to candidates in fluoroscopic images, reducing parameters and enhancing matching likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used on fluoroscopic images, then the detection process is simple, but the detection accuracy is low due to varying imaging quality
Solution Approach 1:
The system performs preliminary actions by pre-training multiple shape models with different parameters before actual detection. These pre-trained models are stored and ready for rapid deployment during fluoroscopic imaging, allowing the system to quickly adapt to varying image qualities without real-time complex computations
Solution Approach 2:
The invention changes parameters by using multiple shape models with different parameters (such as curvature, diameter, and flexibility) to detect catheters under varying imaging conditions. The system selects and adjusts the appropriate model parameters based on the specific fluoroscopic image characteristics, thereby improving detection accuracy across different quality levels
2Measurement precision
If multiple shape models with different parameters are used, then the detection accuracy improves, but the computational complexity increases
Solution Approach 1:
The detection process is segmented into distinct stages: candidate detection, shape model fitting, and parameter optimization. By dividing the complex task into manageable segments, the system reduces computational power requirements at each stage while maintaining overall detection accuracy through the coordinated use of multiple shape models
Solution Approach 2:
The system applies partial action by using a subset of the trained shape models for each specific detection task rather than deploying all models simultaneously. This selective approach reduces computational power consumption while still achieving high detection accuracy by choosing the most appropriate models for the given imaging conditions
3Manufacturing precision
If shape models are fitted to catheter candidates, then the modeling accuracy improves, but the processing time increases
Solution Approach 1:
Shape models are pre-trained and parameter-optimized before actual catheter detection. This preliminary preparation stores the computational results in advance, allowing rapid fitting during real-time fluoroscopic imaging without requiring extensive processing time during the actual detection phase
Solution Approach 2:
The system uses copied instances of shape models with different parameters rather than creating new models during detection. These pre-crafted model copies can be rapidly applied to catheter candidates, achieving high modeling accuracy without the time cost of generating models in real-time
Data Source
AI summary
A method and system for detecting and modeling a catheter in a fluoroscopic image is disclosed. Catheter tip candidates and catheter body candidates are detected in the fluoroscopic image. One of a plurality of trained shape models is fitted to the catheter tip candidates and the catheter body candidates in order to model a shape of the catheter in the fluoroscopic image.