Robot-Guided Interventional Device Trajectory Prediction
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Solution Overview
Problem
Robotic systems for interventional procedures face challenges in navigating interventional devices through complex anatomical structures, leading to multiple failed attempts, potential damage, increased radiation exposure, and suboptimal input issues due to the lack of trajectory prediction and visualization.
Innovation Solution
A robotic system that predicts the trajectory of an interventional device based on user control inputs and displays a dynamic line indicating the device's position, using a neural network trained on previous images and control inputs to estimate and visualize the trajectory with uncertainty margins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional robotic systems are used without trajectory prediction, then the system structure remains simple, but the number of failed attempts increases and procedure time extends
Solution Approach 1:
The system performs preliminary trajectory prediction before actual device navigation by processing training images and control inputs through a trained neural network. This advance prediction allows operators to plan paths and avoid obstacles before committing to actual movements, reducing failed attempts and improving reliability without requiring complex real-time control mechanisms
Solution Approach 2:
The system creates a virtual copy of the anatomical structure using training images and generates predicted trajectory visualizations that represent potential device paths. This digital twin approach allows operators to simulate and evaluate different navigation strategies in a virtual environment before executing them physically, reducing risks without adding physical complexity to the actual robotic system
2Object-affected harmful factors
If multiple failed attempts are made without trajectory visualization, then the robotic system can be operated with simple controls, but radiation exposure increases and tissue damage risk increases
Solution Approach 1:
The system predicts and visualizes trajectories in advance before actual device navigation, allowing operators to evaluate potential paths and select optimal routes that avoid anatomical obstacles. This preliminary planning reduces the need for multiple corrective attempts during the procedure, thereby minimizing radiation exposure and tissue damage risks while actually reducing overall procedure time
Solution Approach 2:
The system provides visual feedback by overlaying predicted trajectory lines on anatomical images, showing operators the expected device path before execution. This feedback mechanism allows real-time adjustment of control inputs to optimize the trajectory, reducing failed attempts and the associated radiation exposure and tissue damage risks without extending procedure time
3Productivity
If trajectory prediction is implemented, then the number of failed attempts reduces, but the device complexity increases
Solution Approach 1:
The system uses a trained neural network model that was pre-trained on extensive image data to perform trajectory predictions. During actual procedures, the system only needs to input current images and control commands to the pre-trained model, avoiding the computational complexity of real-time training while maintaining high prediction accuracy and procedure efficiency
Solution Approach 2:
The system introduces a trajectory prediction module as an intermediary between the operator's control inputs and the actual robotic device movement. This intermediary processes control inputs through the trained neural network to generate predicted trajectory visualizations, allowing operators to make informed decisions without directly adding complexity to the robotic control system itself
4Measurement precision
If dynamic trajectory visualization is provided, then control input accuracy improves, but the information processing complexity increases
Solution Approach 1:
The system creates visual copies of the predicted trajectory by overlaying dynamic lines on anatomical images. These visual representations provide operators with intuitive feedback about the expected device path, enabling more accurate control input adjustments without requiring complex quantitative analysis or additional sensing mechanisms
Data Source
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AI summary
A method and system enable estimating and visualizing trajectories of an interventional device guided by a robot and configured for insertion into an anatomical structure. The method includes training a model with regard to predicting trajectories of the interventional device based on training data from previous images and corresponding control inputs; receiving image data from an image showing a current position of the interventional device; receiving untriggered control inputs for controlling the robot to guide future movement of the interventional device; predicting a trajectory of the interventional device by applying the image data and the untriggered control inputs to the trained model; displaying the predicted trajectory of the interventional device overlaid on the image of the anatomical structure; triggering the untriggered control inputs to control the robot to guide movement of the interventional device according to the triggered control inputs when the predicted trajectory is acceptable.