Anatomic Motion Compensation for Dynamic Instrument Registration
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Solution Overview
Problem
Conventional methods for registering interventional instruments with dynamic patient anatomy are inadequate, leading to inconsistencies and a non-intuitive experience due to discontinuous measurements and failure to account for anatomical motion, particularly in regions like the heart, lungs, and kidneys.
Innovation Solution
A method involving the reception of pose datasets for interventional instruments within cyclically moving anatomy, determining pose differentials, and identifying periodic signals to model and track anatomical motion, allowing for improved registration and navigation by comparing instrument poses with preoperative and intraoperative image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EM sensor methods are used to register interventional instruments with dynamic anatomy, then measurement can be obtained at baseline conditions, but inconsistencies occur between perceived and actual instrument positions due to anatomical motion
Solution Approach 1:
The system transitions from static baseline measurements to dynamic continuous tracking by measuring the position of anatomical landmarks and interventional instruments at multiple time points throughout the respiratory cycle, allowing the registration to adapt to anatomical motion in real-time
Solution Approach 2:
The system implements feedback by continuously comparing the measured positions of anatomical landmarks and instruments across multiple respiratory phases, using this information to correct and update the registration transformation matrices, thereby maintaining accuracy despite anatomical motion
2Device complexity
If baseline-only measurements are taken at discrete time points, then measurement process is simplified, but the displayed registration becomes jerky and non-intuitive for the clinician
Solution Approach 1:
The system implements continuous measurement and updating of registration parameters throughout the respiratory cycle rather than discrete baseline-only measurements, creating smooth continuous motion compensation that eliminates jerky display behavior and provides intuitive visual feedback to the clinician
3Loss of time
If preoperative images are used for navigation, then planning is improved, but the images do not account for intraoperative anatomical motion
Solution Approach 1:
The system performs preliminary registration using preoperative images for surgical planning, then continuously updates this registration during surgery by tracking anatomical landmarks through the respiratory cycle, combining the planning benefits of preoperative imaging with the accuracy of intraoperative motion compensation
Solution Approach 2:
The system transforms static preoperative images into a dynamic registration framework that adapts to intraoperative anatomical motion by measuring landmark positions at multiple respiratory phases and updating transformation matrices in real-time, maintaining accuracy throughout the procedure
Data Source
AI summary
A method of modeling a cyclic anatomical motion comprises receiving a pose dataset for an identified point on an interventional instrument retained within and in compliant movement with a cyclically moving patient anatomy for a plurality of time parameters. The method also includes determining a set of pose differentials for the identified point with respect to a reference point at each of the plurality of time parameters and identifying a periodic signal for the cyclic anatomical motion from the set of pose differentials.