Filtering Patient Reference Sensor Data for Motion Compensation
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Solution Overview
Problem
Existing medical imaging and navigation systems face challenges in accurately compensating for the motion of moving organs, particularly when there is little or no correlation between external motion compensation signals and internal organ motion, as seen in cases like atrial fibrillation, leading to inaccurate representation of medical devices on images.
Innovation Solution
A method and apparatus that filter patient reference sensor readings to suppress small movements caused by skin or head movements, generating a motion compensation function based on filtered readings to correct the position and orientation of medical devices, ensuring accurate superimposition on images despite organ motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patient reference sensor readings are used for motion compensation, then motion compensation is provided, but small movements from skin or head movements reduce accuracy
Solution Approach 1:
A filter is introduced as an intermediary component between the patient reference sensor and the motion compensation function. The filter processes raw sensor readings to distinguish between significant organ motion and negligible skin/head movements, outputting only meaningful motion data for compensation purposes
Solution Approach 2:
The system changes the parameter state of sensor readings by applying filtering criteria. Raw readings are transformed into filtered readings based on magnitude thresholds, converting continuous sensor data into discrete compensated/uncompensated states that reflect actual organ motion versus artifact
2Measurement precision
If filtered PRS readings are used to generate motion compensation function, then accuracy is improved, but processing complexity increases
Solution Approach 1:
The filter applies parameter-based decision logic to sensor readings, changing their processed state based on whether they exceed predetermined thresholds. This simple parameter comparison approach achieves filtering without complex algorithms
Solution Approach 2:
The signal processing is segmented into distinct functional blocks: raw reading acquisition, filtering decision logic, and compensated output generation. This modular segmentation simplifies the overall processing complexity by breaking down the filtering function into manageable stages
Data Source
AI summary
An apparatus includes a positioning system and a patient reference sensor (PRS). The positioning system acquires a plurality of raw PRS readings over time, which can indicate the position and orientation of the PRS. A filter is configured to process the raw PRS readings and output filtered PRS readings. The filter outputs filtered PRS readings as a baseline value while the raw PRS readings stay within a predetermined range and output filtered PRS readings as unchanged raw PRS readings when the raw PRS readings reach or vary outside of the predetermined range. A motion compensation function generated based on at least the filtered PRS readings can be used to correct a subject position and orientation (P&O) to compensate for the motion of the moving region of interest over time.