Instrument Shape Sensing with Multi-Source Localization Feedback
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Solution Overview
Problem
Existing navigation techniques for tubular networks in medical procedures, such as bronchoscopy, face challenges with inaccurate motion estimation of medical devices within the body, leading to incorrect localization and misleading information for physicians.
Innovation Solution
A robotic system that utilizes strain-based shape sensing, combining data from optical fibers, electromagnetic sensors, and imaging devices to adjust and refine shape data for improved navigation, ensuring accurate positioning of instruments within the body.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If strain-based shape sensing is used to determine instrument shape, then shape information can be obtained, but the localization accuracy is insufficient
Solution Approach 1:
The patent combines multiple data sources including strain-based shape sensing, robotic command data, force data, distance data, kinematic model data, and electromagnetic sensor data to comprehensively determine instrument shape and position. This multi-source fusion approach resolves the insufficiency of single-method strain sensing by cross-validating and supplementing information from various sources, thereby improving both localization accuracy and data reliability.
Solution Approach 2:
The system continuously compares strain-based shape data with robotic data and other sensor measurements, using the discrepancies to refine and adjust the shape determination. This feedback loop allows the system to detect and correct localization errors in real-time, ensuring that the instrument position and shape information remain accurate throughout the procedure.
2Measurement precision
If robotic data is combined with strain data, then localization accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the data processing into distinct functional modules: strain data acquisition, robotic data acquisition, force data acquisition, distance data acquisition, kinematic model processing, and electromagnetic sensor processing. Each module handles specific data types independently before integrating them in a coordinated manner, which reduces the overall complexity of processing multiple data streams simultaneously.
Solution Approach 2:
The system employs a multi-functional data processing framework that can handle various types of data (strain, robotic commands, forces, distances, kinematics, electromagnetic signals) through a unified processing architecture. This universal approach allows the system to process diverse data types using common algorithms and processing pipelines, reducing complexity compared to separate processing systems for each data type.
3Reliability
If multiple data sources are integrated, then navigation reliability is improved, but measurement precision requirements increase
Solution Approach 1:
The patent employs parameter transformation and normalization techniques to convert different data types and measurement scales into a common reference frame. The system adjusts and standardizes parameters from various sources (strain values, robotic positions, forces, distances) to ensure they are comparable and consistent, thereby reducing the precision requirements for individual measurements while maintaining overall navigation reliability.
Solution Approach 2:
The system introduces intermediate processing layers and transformation models that act as mediators between different data sources. These intermediaries include coordinate transformation matrices, calibration parameters, and data fusion algorithms that reconcile differences in measurement precision and reference frames, allowing multiple data sources to be integrated without requiring all measurements to be of equal precision.
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
Enhances the accuracy of instrument localization within tubular networks by adjusting strain-based shape data based on robotic and additional data sources, providing reliable real-time navigation and preventing instrument damage.
Implementation Method 1
accessing strain data from an optical fiber positioned within the instrument that is indicative of a strain on a portion of the instrument
Implementation Method 2
combining data from optical fibers, electromagnetic sensors, and imaging devices
Data Source
AI summary
A method of controlling an instrument involves measuring strain of one or more optical fibers associated with an elongate shaft of an instrument, determining a first shape estimation of the elongate shaft based on the measured strain, determining, based on the measured strain, a strain-indicated characteristic of the elongate shaft with respect to a mechanical condition of the elongate shaft, determining an expected range of the mechanical condition of the elongate shaft, determining that the strain-indicated characteristic is outside of the expected range of the mechanical condition, and in response to the determination that the strain-indicated characteristic is outside of the expected range, determining a second shape estimation of the elongate shaft, the second shape estimation being based to a lesser degree on the measured strain compared to the first shape estimation.