Catheter Navigation Deformable Registration
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Solution Overview
Problem
Current localization systems in cardiac procedures face challenges in integrating external three-dimensional models with catheter navigation systems, particularly due to non-linearities and inhomogeneities, which hinder accurate transformation of measurements onto external images.
Innovation Solution
A method and system that register a catheter navigation system to an external three-dimensional image by using fiducial pairs and algorithms like thin plate splines, mean value coordinates, or radial basis function networks to generate a mapping function that compensates for non-linearities and inhomogeneities, ensuring accurate transformation of measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If an affine transformation is used to transform catheter navigation system measurements to three-dimensional image positions, then the transformation process is simple, but non-linearities and inhomogeneities in the localization field introduce error
Solution Approach 1:
The patent transforms the coordinate system from a simple affine transformation to a deformable registration using thin plate splines. This changes the mathematical parameters from linear transformation coefficients to a set of control points and spline weights that can model non-linear distortions. The system uses fiducial pairs to establish correspondence between localization system coordinates and image coordinates, then applies thin plate spline interpolation to compute the deformation field, thereby achieving accurate position transformation while accounting for non-linearities and inhomogeneities in the localization field.
2Measurement precision
If a deformable registration method is used to account for non-linearities, then position transformation accuracy is improved, but the transformation process becomes more complex
Solution Approach 1:
The patent introduces thin plate splines as an intermediary mathematical tool to bridge the gap between the localization system coordinate space and the image coordinate space. Instead of directly computing a complex non-linear transformation, the system uses fiducial pairs as intermediate reference points to define the deformation field. The thin plate spline function acts as a smooth interpolating surface that passes through these fiducial correspondences, providing an elegant solution that balances accuracy with computational tractability.
3Measurement precision
If fiducial pairs are used to generate a mapping function, then registration accuracy is improved, but the registration process requires more steps
Solution Approach 1:
The patent employs fiducial pairs as pre-established reference points that define the correspondence between localization system coordinates and image coordinates. These fiducial correspondences are collected during an initial registration phase, and the thin plate spline mapping function is pre-computed based on these references. Once the mapping function is established, subsequent catheter position transformations can be performed rapidly using the pre-computed deformation field, thereby reducing the time penalty of the more accurate registration method.
Data Source
AI summary
A method for registering a catheter navigation system to a three-dimensional image generally includes obtaining a three-dimensional image including position information for a plurality of surface points on a part of a patient's body, using a catheter navigation system to place a tool at a location on the surface of the patient's body, measuring position information for the surface location, identifying a corresponding location on the image, associating position information for the surface location and the location identified on the image as a fiducial pair, and using at least one fiducial pair to generate a mapping function. The mapping function transforms points within the coordinate system of the catheter navigation to the coordinate system of the three-dimensional image such that, for each fiducial pair, the mapping error is about zero. Suitable warping algorithms include thin plate splines, mean value coordinates, and radial basis function networks.


