Distortion Compensation in Electromagnetic Tracking via Machine Learning
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Solution Overview
Problem
Electromagnetic tracking systems in medical procedures face accuracy issues due to distorters with varying sizes, geometries, and material compositions within the tracking volume, leading to erroneous object location and orientation measurements.
Innovation Solution
A computer-implemented method using machine learning techniques to compensate for distortion by training a model with inductance data from both distorter-present and distorter-absent scenarios, allowing for accurate sensor tracking by estimating and adjusting for distortion effects in the electromagnetic field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electromagnetic tracking systems are used in medical procedures, then object location and orientation can be determined, but measurement accuracy deteriorates due to distorters with varying sizes, geometries, and material compositions
Solution Approach 1:
The system performs preliminary action by capturing inductance data from the transmitter array before conducting medical procedures. This baseline data is stored and later compared against real-time measurements to detect and compensate for distortion effects caused by distorters in the environment, thereby maintaining measurement accuracy throughout the procedure.
Solution Approach 2:
The system implements feedback by continuously monitoring inductance measurements from the transmitter array and comparing them against stored baseline data. When distortion is detected through these comparisons, the system uses machine learning models to calculate compensation values that are applied to sensor tracking data, creating a closed-loop system that maintains accuracy despite environmental changes.
2Reliability
If inductance measurements are used to detect distortion, then distortion patterns can be identified, but device complexity increases due to additional measurement and processing requirements
Solution Approach 1:
The system applies universality by using the transmitter array's existing inductance measurement capability for multiple purposes: both for normal tracking operations and for distortion detection. The same transmitter coils that generate electromagnetic fields for tracking also serve as inductance sensors, eliminating the need for separate distortion detection hardware and reducing overall system complexity.
Solution Approach 2:
The transmitter array performs self-service by using its own inductance characteristics to detect distortion in the environment. The system monitors changes in its own electromagnetic field properties caused by nearby distorters, allowing it to self-diagnose and compensate for measurement errors without requiring external calibration devices or additional sensor arrays.
3Measurement precision
If machine learning models are trained with inductance data to compensate for distortion, then tracking accuracy improves, but loss of time occurs during model training and data processing
Solution Approach 1:
The system performs preliminary action by capturing and storing baseline inductance data from the transmitter array before conducting medical procedures. This preprocessing of reference data enables the machine learning model to work with pre-prepared information during actual procedures, reducing real-time computational requirements and minimizing time loss during tracking operations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method significantly improves measurement accuracy by identifying and compensating for distortion patterns, ensuring precise tracking of medical devices and instruments within the tracking volume, enhancing the reliability of medical procedures.
Implementation Method 1
an array of electromagnetic transmitters configured to generate an electromagnetic field
Implementation Method 2
receiving a first set of inductance data representing one or more inductance measurements when a distorter is absent, and receiving a second set of inductance data representing one or more inductance measurements when the distorter is present
Data Source
AI summary
A computer-implemented method includes receiving a first set of inductance data and a second set of inductance data, each representing one or more inductance measurements when a distorter is absent and when the distorter is present, respectively. The method includes training a machine learning system for compensating for distortion, in which the machine learning system is configured to generate an estimated value of distortion indicating an amount of distortion present in the electromagnetic field. The method includes receiving a set of inductance data and generating, by the trained machine learning system, the estimated value of distortion using the additional set of inductance data and measured pose data that represents position and orientation information for one or more sensors. The method includes providing the estimated value of the distortion for application to a computing device for calibrating the one or more sensors by compensating for the estimated value of the distortion.