Stereotactic Navigation Bone Collision Detection
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Solution Overview
Problem
Existing surgical trajectory planning systems lack reliable and reproducible techniques for estimating skull thickness, leading to challenges in avoiding bone collisions during brain surgeries, where varying skull thickness can result in collisions at different anatomical locations.
Innovation Solution
The development of computerized surgical trajectory planning systems that use 3D representations of a patient's cranial region, adapted from imaging data, to compute surface normals and skull thickness. These systems predict potential bone collisions by determining the maximum acceptable angle for inserting a surgical instrument through a cylindrical hole, taking into account the computed skull thickness and the radius of the hole.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgical trajectory planning is performed without accurate skull thickness estimation, then the surgical procedure can be performed quickly, but bone collisions occur at different anatomical locations due to varying skull thickness
Solution Approach 1:
The system performs preliminary computation of skull thickness and surface normals using 3D cranial region representations before the surgical procedure begins. This allows the surgical trajectory to be pre-planned and optimized to avoid bone collisions, eliminating the need for real-time complex calculations during surgery while ensuring reliable collision prediction.
Solution Approach 2:
The system creates a 3D digital copy (representation) of the patient's cranial region from imaging data. This virtual model allows accurate measurement of skull thickness and computation of surface normals without physically measuring the actual skull, enabling reliable bone collision prediction while keeping the physical surgical equipment simple.
2Object-affected harmful factors
If 3D representations and computational algorithms are used to compute skull thickness and predict bone collisions, then bone collision risk is reduced, but surgical time increases due to additional planning requirements
Solution Approach 1:
All complex computational tasks including 3D representation generation, skull thickness calculation, and trajectory optimization are completed before the surgical procedure begins. This preliminary action eliminates time-consuming calculations during surgery, reducing intraoperative delays while maintaining low trauma risk through accurate pre-planning.
Solution Approach 2:
The system replaces manual measurement and estimation methods with automated computational algorithms that process 3D imaging data. This substitution reduces the time required for trajectory planning compared to traditional manual methods, while providing more accurate bone collision prediction that minimizes patient trauma risk.
3Ease of operation
If the targeting cannula is positioned at the exterior surface of the skull, then the surgical instrument can be easily inserted, but the varying skull thickness makes it difficult to predict and avoid bone collisions
Solution Approach 1:
The system creates a 3D digital copy of the patient's cranial region from imaging data, allowing precise measurement of skull thickness at any location. This virtual measurement approach provides accurate skull thickness data without requiring physical measurement tools during surgery, maintaining ease of instrument insertion while enabling precise collision prediction.
Solution Approach 2:
The system transitions from 2D surface-based targeting to 3D volumetric analysis of the skull. By computing skull thickness as a third dimension in the 3D cranial representation, the system accurately predicts bone collision risk while maintaining the simplicity of linear trajectory insertion through the targeting cannula.
Data Source
AI summary
Systems and methods are provided that automatically determine whether a prospective surgical trajectory will collide with a patient's skull using a 3D representation of the patient's cranial region (including the patient's scalp, skull, and brain) adapted from imaging data of the patient's cranial region (e.g., MRI data or CT data). Taking a particular patient's varied skull thickness into account, examples can determine bone collision during a trajectory planning stage before or after a stereotactic frame is mounted to the patient. Accordingly, examples may preemptively alert a clinician to a potential bone collision before the prospective surgical trajectory is underway/has been executed, thereby reducing the risk of bone collision during the surgical procedure.