Fluoroscopy Probe Tracking via Visual Inference
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Solution Overview
Problem
Existing methods for aligning coordinate systems in fluoroscopy and ultrasound imaging during cardiac surgery face challenges due to low accuracy in probe detection caused by ambiguity, noise, and clutter in fluoroscopic images, leading to unreliable single-frame detection.
Innovation Solution
A method utilizing visual tracking over a sequence of fluoroscopic images, combined with machine-learnt classifiers and template matching, to detect the pose of a transesophageal echocardiography probe, improving detection accuracy by considering temporal consistency and using markers for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-frame detection is used to detect probe pose, then detection speed is maintained, but detection accuracy deteriorates due to probe appearance ambiguity, noise, and clutter
Solution Approach 1:
The system performs preliminary detection on the first frame to establish an initial pose estimate, then uses this as a starting point for tracking in subsequent frames. This preliminary action allows the system to leverage temporal information without requiring complex real-time processing of every frame from scratch.
Solution Approach 2:
The visual tracking module continuously refines the probe pose estimate across multiple frames by maintaining temporal consistency. Instead of performing isolated detections, the system continuously updates the pose by combining information from successive frames, thereby improving accuracy through the continuous integration of useful visual information.
2Measurement precision
If visual tracking over sequence of frames is implemented, then detection accuracy is improved through temporal consistency, but processing time and computational load increase
Solution Approach 1:
The system performs a preliminary detection on the first frame to establish an initial pose estimate, which serves as a starting point for subsequent tracking. This preliminary action reduces the computational burden on later frames by providing a head start, avoiding the need to process every frame from scratch.
Solution Approach 2:
The system dynamically adjusts the detection strategy based on the frame sequence. The first frame undergoes full detection processing, while subsequent frames use streamlined tracking algorithms that leverage the initial detection results. This dynamic approach optimizes processing time by adapting the computational intensity to the specific needs of each frame in the sequence.
3Reliability
If template matching is used for out-of-plane parameter detection, then detection robustness is improved, but detection speed decreases due to computational intensity
Solution Approach 1:
The system performs template matching on the first frame to establish initial out-of-plane parameter estimates. These preliminary estimates serve as starting points for subsequent tracking, reducing the need for repeated intensive template matching operations on every frame and thereby improving overall detection speed while maintaining robustness.
Data Source
AI summary
A probe pose is detected in fluoroscopy medical imaging. The pose of the probe through a sequence of fluoroscopic images is detected. The detection relies on an inference framework for visual tracking overtime. By applying visual tracking, the pose through the sequence is consistent or the pose at one time guides the detection of the probe at another time. Single frame drop-out of detection may be avoided. Verification using detection of the tip of the probe and/or weighting of possible detections by separate detection of markers on the probe may further improve the accuracy.