Automatic 3D Model Registration in Medical Navigation Systems
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Solution Overview
Problem
Current medical navigation systems require manual and time-consuming registration of 3D models of patient organs, leading to inaccuracies during catheter-based interventions like cardiac ablation.
Innovation Solution
A computer-implemented method that uses positioning sensors on catheters to record spatial coordinates, compute spatial transformations, and automatically register 3D models with point clouds, minimizing error metrics to align the model with the navigation system's reference frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual registration is used to align the 3D model with the navigation system, then the operator can adjust the model position and orientation, but the process becomes slow and complex with reduced precision
Solution Approach 1:
The patent replaces the manual mechanical adjustment process with an automated computational system. The computing unit automatically calculates the optimal spatial transformation by processing the point cloud data and comparing it with the 3D model, eliminating the need for manual dragging and rotating operations while improving both speed and precision.
Solution Approach 2:
The system performs self-registration by automatically computing the alignment between the 3D model and navigation data without requiring operator intervention. The computing unit independently processes the spatial coordinates and model vertices to determine the transformation parameters, making the system self-sufficient in the registration task.
2Reliability
If manual registration is used to align the 3D model with the navigation system, then the operator can adjust the model, but the process becomes complex and subject to inaccuracy
Solution Approach 1:
The patent replaces the manual mechanical adjustment process with an automated computational system. The computing unit automatically calculates the optimal spatial transformation by processing the point cloud data and comparing it with the 3D model, eliminating the need for manual dragging and rotating operations while improving both speed and precision.
Solution Approach 2:
The system creates a digital copy of the spatial coordinates as a point cloud and compares it with the vertices of the 3D model. This computational copying approach allows for precise mathematical comparison and transformation calculation, replacing manual visual alignment and improving accuracy while reducing operational complexity.
3Productivity
If automatic registration is implemented using point cloud and spatial transformation computation, then registration speed and precision are improved, but the system requires processing spatial coordinates and model vertices
Solution Approach 1:
The system performs self-registration by automatically computing the alignment between the 3D model and navigation data without requiring operator intervention. The computing unit independently processes the spatial coordinates and model vertices to determine the transformation parameters, making the system self-sufficient in the registration task.
Solution Approach 2:
The system creates a digital copy of the spatial coordinates as a point cloud and compares it with the vertices of the 3D model. This computational copying approach allows for precise mathematical comparison and transformation calculation, enabling automated processing that improves efficiency despite the increased computational requirements.
Data Source
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AI summary
The invention concerns a computer-implemented method for displaying a 3D model of an organ of a patient on a navigation system display device (15), the method comprising : (S01) Receiving spatial coordinates (SXYZ) from a sensor (131) of a catheter (13) inside a portion (CS) of said organ, which define a reference point cloud (RPC) of said portion of said organ; (S05) Receiving a plurality of points defining a 3D model (IH) of said organ, a subset of points being labelled to define a segmentation (RCS) corresponding to said portion of said organ; (51) Computing a spatial transformation (T) which minimizes an error metric (E) between the result (T(Rcs)) of this spatial transformation on the segmentation and the reference point cloud; (52) Displaying on the display device a landmark (LM) at a current position of the catheter and, simultaneously, the result (IH') of the spatial transformation on the 3D model.