3D Data-Driven Laser Orientation Planning for Robotic Surgery
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Solution Overview
Problem
Robotic laser surgery faces challenges in minimizing errant tissue overcutting due to incorrect laser orientation, as existing methods rely on vision or user inputs and fail to effectively model laser-tissue interaction, leading to potential damage to healthy tissue during surgical procedures.
Innovation Solution
A 3D data-driven geometric model is developed to predict tissue cavity shapes, converting the laser orientation planning problem into a collision-minimization problem, using a Gaussian-based model to estimate optimal laser orientations that minimize tissue ablation and prevent over-irradiation of healthy tissue, with the help of projected gradient descent methods for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If laser orientation is not optimized, then surgical procedure is simpler, but healthy tissue overcutting increases
Solution Approach 1:
The system performs preliminary 3D mapping of the tissue cavity and obstacle boundary before laser ablation, and pre-calculates the optimal laser orientation using projected gradient descent optimization. This advance planning ensures that the laser is oriented correctly from the start, minimizing healthy tissue overcutting without requiring complex real-time adjustments during the procedure.
Solution Approach 2:
The patent introduces a 3D geometric model as an intermediary representation between the physical tissue and the laser control system. This model includes the tissue cavity surface, obstacle boundary, and predicted ablation profile, allowing the optimization algorithm to calculate optimal laser orientation without directly interacting with the complex biological tissue, thus reducing harmful effects while managing system complexity.
2Manufacturing precision
If 3D data-driven model is used to predict tissue cavity shapes, then laser orientation precision is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex physical experimentation and trial-and-error orientation adjustment with a 3D data-driven geometric model and projected gradient descent optimization algorithm. This computational approach directly calculates the optimal laser orientation based on the tissue cavity geometry and obstacle boundary, achieving high precision without requiring extensive physical testing or complex hardware modifications.
3Measurement precision
If optimal laser orientation is calculated using projected gradient descent, then tissue ablation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs the computationally intensive projected gradient descent optimization in advance, before the actual laser ablation begins. By pre-calculating the optimal laser orientation based on the 3D scanned tissue cavity and obstacle boundary, the system achieves high ablation accuracy while minimizing the time required during the critical surgical procedure itself.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach optimizes robotic laser orientation to minimize healthy tissue overcutting during pathological tissue resection, ensuring precise control and reducing the risk of collateral damage by predicting the shape of tissue cavities and guiding laser orientation to maintain safe distances from obstacle boundaries.
Implementation Method 1
A single laser pulse can create a 3D volumetric cavity on the tissue surface
Implementation Method 2
laser energy delivery to tissues ensures optimal treatment of targeted lesions
Data Source
AI summary
The present disclosure describes a method comprising ablating a substrate with a laser at an orientation to create a cavity in the substrate, scanning the cavity, and creating a three-dimensional surface for the cavity. The method further includes storing the three-dimensional surface in a dataset. The dataset includes a laser projected distance as an independent variable and a depth of cut as a dependent variable. The method further includes fitting parameters of a gaussian-based model for the laser and the substrate based on the dataset. The present disclosure also describes a method providing a pre-ablation surface, labeling a three-dimensional obstacle boundary that separates material to be remove by a laser and material to remain, and determining an orientation of the laser that results in a predicted post-ablation surface that does not intersect the three-dimension obstacle boundary.