Interventional Device Navigation With Neural Position Prediction
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Solution Overview
Problem
Navigating interventional devices within the anatomy, such as guidewires and catheters, is challenging due to the lack of a one-to-one correspondence between manipulations of the proximal end and the resulting movement of the distal end, requiring repetitive manual adjustments to achieve desired movements.
Innovation Solution
A computer-implemented method using a neural network to predict future positions of interventional device portions based on shape data, providing navigation guidance by inputting the data into a neural network trained to forecast future positions and display corresponding confidence estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a physician manually manipulates the proximal end of an interventional device to navigate the distal end within the anatomy, then the physician can guide the device to a desired location, but the navigation process requires repetitive adjustments and is time-consuming due to the lack of one-to-one correspondence between proximal manipulation and distal movement
Solution Approach 1:
The system performs preliminary action by using a neural network to predict the future position of the distal end based on the current shape and proximal manipulations. This prediction is displayed to the physician before the actual movement occurs, allowing the physician to make more informed and fewer adjustments, thereby reducing navigation time while maintaining accuracy.
Solution Approach 2:
The system implements feedback by displaying the predicted future position of the distal end to the physician in real-time. This feedback loop allows the physician to see the anticipated outcome of manipulations before executing them, reducing the need for repetitive trial-and-error adjustments and decreasing overall navigation time.
2Productivity
If a robotic controller is used to automatically manipulate the proximal end of the interventional device, then navigation efficiency may be improved, but the complexity of the control system increases
Solution Approach 1:
The neural network prediction system acts as an intermediary between the physician's manipulations and the actual device navigation. Instead of implementing a complex robotic controller, the system uses software-based prediction to bridge the gap, providing enhanced navigation efficiency while avoiding the mechanical complexity of robotic actuators.
Solution Approach 2:
The system replaces the mechanical complexity of robotic controllers with a software-based neural network prediction system. This substitution maintains or improves navigation efficiency by providing intelligent guidance, while eliminating the need for complex mechanical robotic manipulation systems.
Data Source
AI summary
A computer-implemented method of providing navigation guidance for navigating an interventional device within the anatomy, includes: receiving (S110) interventional device shape data (110) representing a shape of the interventional device (120) at one or more time steps (t1 . . . n), the time steps including at least a current time step (tn): inputting (S120) the interventional device shape data (110) into a neural network (130) trained to predict, from the interventional device shape data (110), a future position (140) of one or more portions of the interventional device (120) at one or more future time steps (tn+1 . . . n+k), and a corresponding confidence estimate (150) for the one or more future positions (140); and displaying (S130) the predicted one or more future positions (140), and the corresponding predicted confidence estimate (150).