Flexible Needle Steering via Inverse Kinematics
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Solution Overview
Problem
Current methods for percutaneous needle insertion, especially with flexible needles, face challenges in real-time control and accurate targeting due to tissue deformation and non-intuitive control, leading to potential injury and inefficiency in avoiding sensitive tissues and organs.
Innovation Solution
A computer-controlled robotic system that uses imaging to determine the needle position and employs an inverse kinematics algorithm to calculate necessary maneuvers for the needle base, modeling flexible needle insertion as a linear beam supported by virtual springs, allowing for real-time path planning and optimization to minimize tissue pressure and avoid obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If thin and flexible needles are used to reduce tissue damage and enable curved trajectories, then tissue damage is reduced and curved paths are enabled, but control difficulty increases due to non-minimum phase behavior
Solution Approach 1:
The patent implements a closed-loop control system that uses imaging (fluoroscopy, CT, or MRI) to continuously monitor needle position and provides feedback to the control system. This allows real-time adjustment of control inputs to compensate for the non-minimum phase behavior of flexible needles, enabling accurate control despite their inherent instability and difficulty in manual operation.
Solution Approach 2:
The patent replaces manual mechanical control with an automated robotic system that computes control inputs based on a biomechanical model of the needle-tissue interaction. The system uses inverse dynamics and optimization algorithms to calculate the required base maneuvers, substituting intuitive human control with model-based automated control that can handle the complex non-minimum phase dynamics.
2Manufacturing precision
If rigid needles are used for accurate placement under image guidance, then placement accuracy is improved, but tissue pressure increases causing injurious effects
Solution Approach 1:
The patent changes the key parameter of needle flexibility, transitioning from rigid to flexible needles. The system models the flexible needle as a beam and uses biomechanical principles to predict its deformation under tissue forces. By controlling the flexible needle's base maneuvers and accounting for its elastic deformation, the system achieves accurate placement while distributing pressure along the needle length rather than concentrating it at the tip.
3Adaptability or versatility
If beveled tip needles are used to achieve steering through lateral deflection, then steering capability is achieved, but control complexity increases due to rotation requirements
Solution Approach 1:
The patent extracts the steering function from the needle tip geometry (beveled tip requiring rotation) and transfers it to the needle base control. By controlling the base maneuvers and using a biomechanical model to predict needle deflection, the system achieves steering without requiring complex tip rotations, simplifying the control interface and making the system more intuitive to operate.
4Adaptability or versatility
If flexible needles are used to navigate curved trajectories avoiding sensitive tissues, then ability to avoid obstacles is improved, but predictability of needle motion deteriorates due to tissue deformation
Solution Approach 1:
The patent performs preliminary action by computing the needle's biomechanical model and predicting its motion in advance. The system calculates the expected needle deflection and trajectory based on the applied base maneuvers and tissue properties before actual insertion. This predictive modeling allows the control system to pre-compensate for tissue deformation effects, improving motion predictability and enabling reliable navigation along planned curved trajectories.
Data Source
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AI summary
A robotic system for steering a flexible needle during insertion into soft-tissue using imaging to determine the needle position. The control system calculates a needle tip trajectory that hits the desired target while avoiding potentially dangerous obstacles en route. Using an inverse kinematics algorithm, the maneuvers required of the needle base to cause the tip to follow this trajectory are calculated, such that the robot can perform controlled needle insertion. The insertion of a flexible needle into a deformable tissue is modeled as a linear beam supported by virtual springs, where the stiffness coefficients of the springs varies along the needle. The forward and inverse kinematics of the needle are solved analytically, enabling both path planning and correction in real-time. The needle shape is detected by image processing performed on fluoroscopic images. The stiffness properties of the tissue are calculated from the measured shape of the needle.