Lead Programming System Using Image Registration for Neuromodulation
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Solution Overview
Problem
The process of programming leads for therapeutic neuromodulation, such as spinal cord stimulation, is time-consuming and difficult due to the challenge of transferring parameters from test leads to permanently implanted leads, which may change positions, and the complexity of obtaining and processing medical images for alignment and parameter adjustment.
Innovation Solution
An automated or semi-automated lead programming system uses image registration and geometric algorithms to align pre- and post-implantation images, combined with optical cameras and machine learning for image segmentation, to adjust lead parameters based on anatomical correlations and minimize the need for network setups and PHI agreements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image processing and lead parameter transfer are used, then measurement precision can be maintained, but programming time increases significantly
Solution Approach 1:
The system creates a digital copy of the pre-implantation lead configuration and anatomical structure from imaging data. This digital twin is then used to automatically calculate and transfer parameters to the post-implantation lead configuration, eliminating manual measurement and reducing programming time while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical image processing and visual alignment methods with automated computational algorithms. Image registration techniques automatically align pre- and post-implantation images, and geometric algorithms compute lead parameter transformations, substituting manual operations with automated digital processing.
2Productivity
If automated image registration algorithms are implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The complex image registration process is divided into separate functional modules: image acquisition, image registration, lead detection, parameter calculation, and verification. Each module performs a specific function independently, making the overall complex system more manageable and easier to implement through standardized components.
Solution Approach 2:
The system introduces an intermediary computational layer that processes imaging data and generates lead parameters automatically. This intermediary processing layer acts as a bridge between raw imaging data and final lead programming parameters, simplifying the interaction between different system components and reducing overall complexity.
3Productivity
If lead parameters are transferred without accounting for lead movement, then programming time is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The system dynamically adjusts lead parameters based on the actual post-implantation lead position detected in images. Rather than using fixed pre-programmed parameters, the system calculates updated parameters that account for any lead movement or positioning variations, maintaining precision while enabling faster programming through automated adaptation.
Solution Approach 2:
The patent implements a feedback loop where post-implantation images are used to verify and adjust lead parameters. The system compares actual lead positions with intended positions and automatically modifies parameters to compensate for deviations, ensuring manufacturing precision is maintained even when lead movement occurs.
Data Source
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AI summary
Systems and methods for programming a lead are provided. A first image of an anatomical region depicting an anatomical element and at least a portion of an initial lead and an initial lead parameter associated with the initial lead are received. A second image of the anatomical region depicting at least a portion of the anatomical element and at least a portion of an implanted lead is also received. An implanted lead parameter may be generated for the implanted lead based on a combination of the initial lead parameter and a correlation of the first image with the second image.