Coronary Sinus Catheter Electrode Tracking in Fluoroscopy
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Solution Overview
Problem
Conventional tracking algorithms face difficulties in detecting and tracking coronary sinus catheter electrodes in fluoroscopic images due to large image variations, similar nearby structures, and cluttered backgrounds, which complicates atrial fibrillation ablation procedures.
Innovation Solution
A learning-based approach is employed to initialize and track coronary sinus catheter electrodes using a flexible algorithm that localizes and tracks electrodes in each frame of a fluoroscopic image sequence, utilizing a probabilistic boosting tree for detection and affine transformations for model candidates, capable of handling few or many electrodes with deformation and motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional tracking algorithms are used to detect coronary sinus catheter electrodes in fluoroscopic images, then the tracking process can be performed with standard methods, but the detection accuracy deteriorates due to large image variations, nearby similar structures, and cluttered backgrounds
Solution Approach 1:
The patent applies preliminary action by initializing a catheter electrode model in the first frame based on input locations of CS sinus catheter electrodes. This pre-initialization creates a reference model that guides subsequent tracking, allowing the system to anticipate electrode positions and handle image variations more effectively before the actual tracking begins in the second frame.
Solution Approach 2:
The patent implements feedback through the probabilistic tracking process where model candidates are generated, scored, and selected based on probability scores. The selected model from one frame becomes the basis for tracking in the next frame, creating a continuous feedback loop that refines electrode position detection over time while adapting to image variations and maintaining precision despite cluttered backgrounds.
2Measurement precision
If a robust learning-based approach is used to track coronary sinus catheter electrodes, then the detection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing the tracking problem into distinct stages: initialization in the first frame, candidate generation in the second frame, probability scoring, and model selection. This segmentation of the complex learning-based approach into manageable steps reduces overall algorithmic complexity while maintaining high detection precision through systematic processing.
Solution Approach 2:
The patent uses partial action by generating multiple catheter electrode model candidates and then selecting only the best one based on probability scores. This approach avoids the need to process all possible electrode configurations, reducing computational complexity while maintaining precision by focusing computational resources on the most likely candidates rather than exhaustively evaluating every possibility.
3Ease of operation
If tracking is performed in real-time during atrial fibrillation ablation procedures, then the clinical utility improves, but the processing speed requirements increase
Solution Approach 1:
The patent enhances clinical usability through preliminary action by initializing the electrode model in the first frame before real-time tracking begins. This pre-processing step establishes a reliable baseline that accelerates subsequent real-time tracking operations, allowing the system to meet speed requirements for clinical use without sacrificing accuracy during the actual ablation procedure.
Solution Approach 2:
The patent improves processing speed for real-time clinical application by generating multiple model candidates and using probability-based selection rather than exhaustive analysis. This partial action approach processes only the necessary candidate models with high probability scores, enabling real-time tracking during atrial fibrillation ablation procedures while maintaining the precision needed for safe and effective clinical operation.
Data Source
AI summary
A method and system for detecting and tracking coronary sinus (CS) catheter electrodes in a fluoroscopic image sequence is disclosed. An electrode model is initialized in a first frame of the fluoroscopic image sequence based on input locations of CS sinus catheter electrodes in the first frame. The electrode model is tracked in subsequent frames of the fluoroscopic image sequence by detecting electrode position candidates in the subsequent frames of the fluoroscopic image sequence using at least one trained electrode detector, generating electrode model candidates in the subsequent frames based on the detected electrode position candidates, calculating a probability score for each of the electrode model candidates, and selecting an electrode model candidate based on the probability score.