Predictive Tissue Displacement Compensation System
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Solution Overview
Problem
Existing methods for compensating neurally triggered or self-caused tissue displacement in medical procedures, such as surgery and imaging, face challenges in predicting and effectively counteracting involuntary movements like heartbeats, respiration, and muscular tremors, which can lead to unacceptable compensation delays due to complex and unpredictable interactions between various physiological movements.
Innovation Solution
A method that involves sampling position data from index markers and source activity data, correlating them to generate predictive correspondence, and using this data to control displacement means, allowing for compensation of tissue displacement in both macro and micro ranges, with options for physical damping and software control of acceleration/deceleration to minimize artificial displacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motor means are controlled by microprocessor means based on position data acquired by monitoring tissue movement, then the spatial relation to the tissue is maintained constant over time, but the time lag between registration and counter measure does not allow acceptable compensation of neurally triggered displacement
Solution Approach 1:
The system performs preliminary action by predicting future tissue displacement based on current and past displacement patterns. Instead of reacting to displacement after it occurs, the control system calculates predicted future positions and proactively adjusts the apparatus position in advance, eliminating the reactive time lag inherent in traditional monitoring-and-correcting systems.
Solution Approach 2:
The system transitions from static, reactive compensation to dynamic, predictive compensation. By continuously updating the prediction model with new displacement data and adjusting the apparatus position in real-time based on predicted future displacement, the system adapts to the dynamic nature of tissue movement, particularly neurally triggered movements that follow physiological patterns.
2Measurement precision
If the apparatus is displaced by motor means in a mirroring manner to trace tissue movement, then the focus on tissue is maintained, but the complex and unpredictable interactions between physiological movements make prediction difficult
Solution Approach 1:
The system changes the approach from attempting to model complex physiological interactions to using empirical parameter-based prediction. By analyzing actual displacement patterns and deriving prediction parameters directly from observed tissue movement data, the system bypasses the need to understand or model the underlying complex physiological mechanisms, simplifying the prediction system while maintaining accuracy.
3Stability of the object's composition
If patient is immobilized on a support to prevent movement during surgery, then external movement is reduced, but tissues under autonomous control still move and cannot be prevented from self-caused displacement
Solution Approach 1:
The system extracts and isolates the autonomous tissue displacement component from the overall patient movement problem. By using prediction algorithms that specifically target and compensate for self-caused tissue displacement (such as heartbeat-induced motion, respiration, or peristalsis), the system addresses the autonomous movement issue separately from general patient positioning, allowing immobilization to effectively stabilize the patient while the prediction system handles the remaining autonomous tissue motion.
Data Source
AI summary
A system for compensating a neurally triggered or other self-caused displacement of a tissue of an animal including man comprises a base provided with a means for immobilizing the animal or body part thereof comprising the tissue, one or more displacement means in contact with the base, a means for sampling position data from a marker disposed in or in the proximity of the tissue, a means for sampling source activity data from one or more sources of displacement, microprocessor means for correlating position data and source activity data to generate a predictive correspondence, a means for controlling the one or more displacement means by source activity data based on the predictive correspondence. Also disclosed is a corresponding method and use.