Spinal Cord Stimulation Parameter Optimization via Computational Modeling
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Solution Overview
Problem
The current methods for selecting spinal cord stimulation parameters for treating chronic pain are inefficient and time-consuming, relying on trial-and-error approaches that do not guarantee optimal results due to the complexity of electrode array design, placement, and stimulation settings, which affects the selective activation of dorsal column fibers without activating dorsal root fibers.
Innovation Solution
A patient-specific computational modeling approach is used to optimize spinal cord stimulation parameters, including lead geometry, electrode array placement, and contact selection, employing a cost function to minimize the activation of dorsal root fibers while maximizing the activation of dorsal column fibers, utilizing a genetic algorithm for numerical optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If trial-and-error approach is used to select spinal cord stimulation parameters, then parameter selection can be performed without complex modeling, but the process becomes time-consuming and does not guarantee optimal results
Solution Approach 1:
The system performs preliminary computational modeling and optimization before actual parameter selection. A patient-specific computational model is constructed using pre-operative imaging data, and the optimal stimulation parameters are calculated in advance through automated optimization algorithms, eliminating the need for time-consuming trial-and-error programming during clinical procedures.
Solution Approach 2:
The system creates a virtual copy of the patient's spinal cord anatomy through computational modeling. This digital twin allows for simulation and optimization of stimulation parameters in silico before applying them to the actual patient, enabling precise parameter selection without repeated physical adjustments.
2Reliability
If manual programming based on patient sensation and feedback is used, then clinical programming can be performed without automated systems, but the process is expensive and provides no assurances of optimality
Solution Approach 1:
The system replaces manual clinical programming with automated computational optimization. Instead of relying on clinicians to manually adjust parameters based on patient feedback, the system uses automated algorithms that compute optimal parameters by minimizing a cost function that quantifies the difference between desired and actual neural activation patterns.
Solution Approach 2:
The system systematically varies stimulation parameters (amplitude, pulse width, frequency, electrode configuration) within the computational model to identify the optimal combination. The optimization algorithm explores the parameter space and converges on settings that maximize dorsal column activation while minimizing dorsal root activation, providing guaranteed optimality.
3Reliability
If high stimulation amplitude is used to activate dorsal column fibers, then adequate pain relief can be achieved, but dorsal root fibers are also activated causing discomfort
Solution Approach 1:
The system optimizes the spatial distribution of stimulation to achieve local selectivity. By carefully selecting which electrodes to activate and at what amplitudes, the system creates a localized stimulation pattern that concentrates energy on dorsal column fibers while sparing dorsal root fibers. The cost function explicitly penalizes activation of non-target fibers, enabling selective stimulation.
Solution Approach 2:
The system applies partial activation strategies where not all electrodes are activated at maximum amplitude. Instead, a subset of electrodes is selectively activated at optimized amplitudes that are sufficient to activate dorsal column fibers without exceeding the threshold for dorsal root fiber activation. The optimization determines the minimal necessary stimulation to achieve therapeutic effect.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the time required to select effective stimulation parameters and increases treatment efficacy by optimizing the selectivity and efficiency of spinal cord stimulation, ensuring better pain management with reduced electrical energy consumption.
Implementation Method 1
determine a distribution of a plurality of electrical stimuli for a given patient-specific model
Data Source
AI summary
Systems, methods, and devices are disclosed for optimizing patient-specific stimulation parameters for spinal cord stimulation. A patient-specific anatomical model is developed based on a pre-operative image, and a patient-specific electrical model is developed based on the anatomical model. The inputs to the electric model are chosen, and the model is used to calculate a distribution of electrical potentials within the modeled domain. Models of neural elements are stimulated with the electric potentials and used to determine which elements are directly activated by the stimulus. Information about the models inputs and which neural elements are active is applied to a cost function. Based on the value of the cost function, the inputs to the optimization process may be adjusted. Inputs to the optimization process include lead/electrode array geometry, lead configuration, lead positions, and lead signal characteristics, such as pulse width, amplitude, frequency and polarity.