Instrument Localization Data Filtering for Accurate Anatomic Registration
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Solution Overview
Problem
Existing minimally invasive medical procedures face challenges in accurately localizing surgical instruments within patient anatomy due to redundant or inaccurate spatial information, which can reduce localization accuracy and slow the process.
Innovation Solution
A method for filtering spatial information from instrument tracking systems by removing or reducing the weight of redundant, outlier, or low-confidence data points, using techniques such as Iterative Closest Point (ICP) registration and filtering based on spatial relationships and commanded position, to improve localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more spatial information about the instrument is received to increase localization accuracy, then measurement precision is improved, but redundant or inaccurate spatial information reduces localization accuracy and slows the localization process
Solution Approach 1:
The system extracts and removes redundant spatial information from the set of spatial data records obtained from the tracking system. By identifying and eliminating duplicate or unnecessary data points, the system maintains localization accuracy while reducing the computational burden and processing time required for registration with the anatomical model.
Solution Approach 2:
The system applies different processing treatments to different spatial data records based on their quality and relevance. High-confidence, non-redundant records are prioritized for registration, while low-confidence or redundant records are filtered out or given lower weight, thereby optimizing the localization process efficiency without compromising accuracy.
2Measurement precision
If more spatial information about the instrument is received to increase localization accuracy, then measurement precision is improved, but device complexity increases due to filtering requirements
Solution Approach 1:
The system performs preliminary filtering of spatial data records before the registration process. By pre-processing the spatial information to remove redundant and low-confidence records in advance, the system simplifies the subsequent registration operation and reduces the computational complexity of the overall localization system while maintaining high localization accuracy.
3Measurement precision
If redundant spatial information is filtered to improve localization accuracy, then measurement precision is improved, but loss of information occurs if valuable data is incorrectly removed
Solution Approach 1:
The system employs feedback mechanisms to evaluate the quality and redundancy of spatial data records dynamically. By continuously assessing the confidence levels and spatial relationships of data points, the system makes informed decisions about which records to retain or filter, ensuring that valuable spatial information is preserved while removing only truly redundant data, thus maintaining data integrity and localization accuracy.
Data Source
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AI summary
A method is disclosed that comprises moving an instrument positioned at least partly within an anatomic passageway and receiving a first set of spatial information from the instrument. The first set of spatial information includes a plurality of spatial data records including position information for a distal end of the instrument at a plurality of time periods. The method further comprises receiving shape data including a shape of a portion of the instrument from a shape sensor disposed within the instrument. After receiving the shape data, the first set of spatial information is filtered by removing at least one of the plurality of spatial data records based upon the received shape data.