Instrument Localization Filtering for Redundant Spatial Data
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Solution Overview
Problem
Existing minimally invasive medical procedures face challenges in accurately localizing surgical instruments due to redundant or inaccurate spatial information, which can reduce localization accuracy and slow the process.
Innovation Solution
A method involving a computing system that filters spatial information from instruments based on velocity profiles, confidence factors, and shape data to select and weight relevant data records for improved localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more spatial information is received about the instrument, then localization accuracy is improved, but processing time increases and redundant/inaccurate data reduces efficiency
Solution Approach 1:
The patent extracts and filters only the essential spatial information from the instrument while removing redundant and inaccurate data. The system selectively processes spatial records based on criteria such as temporal proximity, spatial consistency, and relevance to the commanded velocity profile, thereby reducing processing load while maintaining localization accuracy.
Solution Approach 2:
The patent applies different filtering criteria to different portions of the spatial information based on their quality and relevance. High-quality, accurate spatial data is processed in detail, while low-quality or redundant data is filtered out or given less weight, optimizing the balance between accuracy and processing efficiency.
2Quantity of substance
If redundant spatial information is included in localization processing, then more data is available for analysis, but localization accuracy decreases
Solution Approach 1:
The system extracts only the useful spatial information from the complete data set, separating high-quality records from redundant or inaccurate ones. Filtering mechanisms remove data that does not contribute to accurate localization, ensuring that only relevant spatial information is used in the final localization calculation.
Solution Approach 2:
The patent changes the parameters used to evaluate spatial data, such as weighting factors, threshold values, and selection criteria. By dynamically adjusting these parameters based on data quality metrics and commanded velocity profiles, the system optimizes which data points are included in localization processing.
3Productivity
If inaccurate spatial data is processed, then more information is utilized, but localization precision deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms that continuously evaluate the quality of spatial data and adjust processing criteria accordingly. Confidence factors and quality metrics provide feedback to filter out inaccurate data while maintaining utilization of valid information, ensuring localization precision is not compromised.
Solution Approach 2:
The patent employs dynamic parameter adjustment based on data quality assessment. When inaccurate data is detected, the system modifies processing parameters such as threshold values, weighting factors, and selection criteria to exclude or down-weight problematic data points, thereby maintaining localization precision.
Data Source
AI summary
A method performed by a computing system comprises providing instructions to a teleoperational assembly to move an instrument within an anatomic passageway according to a commanded velocity profile. The method also comprises receiving a set of spatial information from the instrument positioned within the anatomic passageway and filtering the set of spatial information to select a quantity of spatial data records from the set of spatial information proportionate to the commanded velocity profile.