Surgical Tool Path Feed Rate Planning Using Tissue Density
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Solution Overview
Problem
Existing robotic surgical systems fail to provide in-depth pre-planning of tool path feed rates based on detailed analysis of patient imaging data and anatomical density, leading to sub-optimal outcomes such as implant fit issues and tool skiving, due to insufficient consideration of tool geometry and anatomical interaction.
Innovation Solution
A computer-implemented surgical planning method that merges anatomical data with tool path data to identify intersections, compute a tool contact factor, and set a planned feed rate factor based on density values, enabling precise control of the robotic manipulator's tool interaction with the anatomical volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If prior feed rate techniques are used that do not account for tool geometry and anatomical intersection, then the system is simpler to operate, but manufacturing precision deteriorates due to implant fit issues and resection errors
Solution Approach 1:
The system performs preliminary analysis of tool-anatomy intersections and density values before the actual resection operation. The planning software computes tool contact factors and determines optimal feed rates in advance by simulating tool paths through the anatomical volume, allowing the surgical team to review and adjust the plan before execution.
Solution Approach 2:
The system creates a virtual copy of the anatomical volume from imaging data and simulates tool interactions within this digital model. By working with a computational replica rather than the actual anatomy during planning, the system can perform complex geometric analyses without risking patient safety or requiring additional physical equipment.
2Productivity
If prior feed rate techniques are used that ignore anatomical density variations, then the cutting process is faster, but tool skiving and tissue damage increase
Solution Approach 1:
The system determines feed rates locally at each point along the tool path based on the specific anatomical density and tool-contact conditions at that location. Rather than applying a uniform feed rate throughout the entire resection, the planning software computes varying feed rate factors that adapt to local bone density variations and tool geometry, optimizing cutting performance while minimizing tissue damage at each specific point.
Solution Approach 2:
The system dynamically adjusts the feed rate parameter based on computed tool contact factors and anatomical density values. The planning software modifies cutting parameters in response to varying anatomical conditions along the tool path, changing feed rates to match the specific mechanical properties of the tissue being removed at each location.
3Reliability
If manual user selection of feed rate is used, then the system is easier to operate, but reliability deteriorates due to sub-optimal cutting forces and implant fit issues
Solution Approach 1:
The planning software automatically computes optimal feed rates and tool paths by analyzing the tool geometry, anatomical volume, and density values. The system performs self-analysis of the surgical plan, calculating tool contact factors and determining appropriate cutting parameters without requiring manual intervention, thereby ensuring consistent, data-driven decisions that improve reliability.
Solution Approach 2:
The system uses computed tool contact factors and density value analysis to provide feedback on the planned resection. The planning software evaluates the interaction between tool geometry and anatomical structures, using this information to adjust and optimize feed rate recommendations, creating a closed-loop planning process that improves implant fit reliability.
Data Source
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AI summary
A computer-implemented surgical planning method is provided. The method includes obtaining anatomical data of an anatomical volume and path data including a tool path along which a tool will move. The method also includes obtaining tool data; merging the path data and the anatomical data; and for a point of the tool path, identifying a location of the point, loading a geometry of the tool at the location, identifying an intersection between the tool and the anatomical volume at the location, determining density values of the anatomical data within the intersection, computing a tool contact factor related to the intersection, setting a planned feed rate factor for the tool based on the density values and the tool contact factor, associating the planned feed rate factor with the point; and outputting cut plan data including the planned feed rate factor associated with the point of the tool path.