Spinal Cord Neuromodulation Programming Interface
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Solution Overview
Problem
The process of selecting electrical stimulation parameters for spinal cord stimulation is time-consuming and often relies on trial-and-error, lacking a systematic approach to effectively tailor parameters for individual patients.
Innovation Solution
A computer-based tool that utilizes functional images of the spinal anatomy, electric field modeling, and neuromodulation settings to assist in planning and performing spinal cord neuromodulation by determining optimal electrode positions and activation volumes to target specific functional regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional trial-and-error method is used to select stimulation parameters, then flexibility in parameter selection is maintained, but the programming process becomes time-consuming and cumbersome
Solution Approach 1:
The system performs preliminary actions by automatically determining electrode positions relative to functional spinal regions using pre-acquired imaging data and electric field models. The volume of activation is calculated in advance for different parameter settings, allowing clinicians to select optimal parameters without time-consuming trial-and-error during patient programming sessions.
Solution Approach 2:
The system creates a virtual copy of the patient's spinal anatomy with functional region mapping and simulates electrode placement and stimulation effects in this digital model. This virtual representation allows parameter optimization to be performed on the copy before applying settings to the actual patient, significantly reducing programming time.
2Adaptability or versatility
If intuitive or idiosyncratic methodology is used for parameter selection, then clinical flexibility is preserved, but treatment precision and effectiveness are reduced
Solution Approach 1:
The system divides the spinal cord into distinct functional regions (such as dorsal, ventral, lateral columns) with different neurological functions. Electric field models calculate the volume of activation specific to each region, allowing precise targeting of particular functional areas while preserving the ability to customize treatment based on individual patient needs and symptom locations.
Solution Approach 2:
The system replaces intuitive manual parameter selection with computer-based electric field modeling and automated calculation of activation volumes. This substitution of mechanical/clinical judgment with computational analysis provides objective, precise targeting while maintaining adaptability through software-configurable parameters and multiple modeling options.
3Measurement precision
If detailed electric field modeling and volume of activation calculation are performed, then stimulation precision is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The system segments the complex electric field modeling task into manageable components: (1) acquiring patient-specific anatomical imaging data, (2) segmenting the spinal cord into functional regions, (3) placing virtual electrodes in the model, (4) calculating electric field distribution, and (5) determining volume of activation for each functional region. This segmentation reduces overall complexity while maintaining precision.
Solution Approach 2:
The computationally intensive electric field modeling and volume of activation calculations are performed in advance during treatment planning, before the actual spinal cord stimulation device is activated. This preliminary computation allows the implanted device to operate with simpler, pre-determined parameter settings, reducing the complexity burden on the implanted system.
Data Source
AI summary
A tool for assisting in the planning or performing of electrical neuromodulation of a patient's spinal cord. The tool may have various functions and capabilities, including calculating a volume of activation, registering an electrode(s) shown in a radiologic image, constructing functional images of the patient's spinal anatomy, targeting of neuromodulation, finding a functional midline between multiple electrodes, determining the three-dimensional position of multiple electrodes, and/or accommodating for electrode migration. In certain embodiments, the tool can be embodied as computer software or a computer system.