Insertion Trajectory Checkpoint Optimization for Image-Guided Steering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated medical devices face challenges in optimizing checkpoint locations during the insertion and steering of medical instruments within the body, leading to potential harm to non-target regions, increased risk of human error, and reduced procedural accuracy.
Innovation Solution
A computer-implemented method and system that utilizes data analysis, including machine learning and artificial intelligence algorithms, to predict and optimize checkpoint locations along a trajectory for medical instrument insertion, considering real-time obstacles and anatomical changes, thereby enhancing safety and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated medical devices use fixed linear insertion trajectories, then the device complexity is reduced and ease of operation is improved, but the manufacturing precision and reliability of reaching the target accurately deteriorates due to inability to adapt to anatomical variations
Solution Approach 1:
The system transitions from fixed linear trajectories to dynamic non-linear trajectories that adapt in real-time based on imaging feedback and anatomical variations. The robotic device can adjust its insertion path dynamically during the procedure to maintain accuracy despite patient movement or anatomical differences.
Solution Approach 2:
The system incorporates real-time imaging feedback (CT, fluoroscopy, ultrasound) to continuously monitor the medical instrument's position and adjust the insertion trajectory accordingly. This closed-loop feedback mechanism allows the system to correct deviations and adapt to unexpected anatomical conditions during insertion.
2Manufacturing precision
If more checkpoints are added along the insertion trajectory to improve accuracy, then the manufacturing precision is improved, but the loss of time increases due to more frequent trajectory updates and imaging scans
Solution Approach 1:
The system uses a hybrid approach where critical checkpoints require full imaging verification while less critical segments can proceed with reduced monitoring. This selective level of scrutiny maintains accuracy where needed while minimizing time loss in lower-risk zones.
Solution Approach 2:
The system performs preliminary trajectory planning and simulation before the actual insertion, pre-identifying optimal checkpoint locations and potential anatomical obstacles. This advance preparation reduces the need for extensive real-time adjustments and imaging during the procedure.
3Reliability
If real-time imaging and data processing are implemented to optimize checkpoint locations, then the reliability and safety are improved, but the use of energy and device complexity increase
Solution Approach 1:
The system uses multi-functional imaging equipment that can serve both diagnostic and guidance purposes. The same imaging system is used for pre-procedure planning, real-time guidance, and post-procedure verification, reducing the need for separate dedicated systems and associated energy consumption.
Solution Approach 2:
The system replaces extensive manual monitoring and adjustment by physicians with automated image processing algorithms and robotic control systems. This substitution reduces the need for continuous human observation and physical adjustment, optimizing the balance between automation and energy use.
Data Source
AI summary
Provided are computer-implemented methods and systems for generating and/or utilizing model(s) for determining optimized checkpoint locations along a trajectory in an image-guided procedure for inserting a medical instrument to a target in a body of a patient based, inter alia, on data related to an automated medical device and/or to operation thereof.