SYSTEMS AND METHODS FOR MODEL-BASED OPTIMIZATION OF SPINAL CORD STIMULATION ELECTRODES AND DEVICES

• US 20170161454A1
• Filed: 07/01/2015
• Published: 06/08/2017
• Est. Priority Date: 07/03/2014
• Status: Active Grant

• Current Assignee: Duke University
• Original Assignee: Duke University
• Inventors: Grill, Warren M., Howell, Bryan
• Granted Patent: US 10,096,386 B2
• Time in Patent Office: Days
• CPC Class Codes: A61N 1/0551 Spinal or peripheral nerve electrodes
• CPC Class Codes: A61N 1/36185 Selection of the electrode configuration
• CPC Class Codes: A61N 1/37241 providing test stimulations
• CPC Class Codes: G06F 17/13 Differential equations using digital differential analysers G06F7/64
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Systems and methods for model-based optimization of spinal cord stimulation electrodes and devices are disclosed. According to an aspect a method includes providing a patient-specific electroanatomical model including the spine, spinal cord, and a map of target neural elements and non-target neural elements. The method also includes using model electrodes to stimulate the target neural elements. Further, the method includes determining differences in activation thresholds between the target neural elements and the non-target neural elements in a plurality of different configurations of the model electrodes. The method also includes generating an optimal spinal cord stimulation electrode configuration based on the determined differences in activation thresholds.

Claims

1. A method comprising:

at least one processor and memory for;

providing an electroanatomical model of a patient spine and spinal cord including a map of target neural elements and non-target neural elements;

using model electrodes to simulate electrical stimulation of the target and non-target neural elements;

determining differences in activation thresholds between the target neural elements and the non-target neural elements in a plurality of different configurations of the model electrodes; and

generating an optimal spinal cord stimulation electrode configuration based on the determined differences in activation thresholds.

2. The method of claim 1, wherein the target neural elements are dorsal column fibers, and the non-target neural elements are dorsal root fibers.

3. The method of claim 1, wherein using model electrodes comprises applying a model rectangular pulse to the electrodes.

4. The method of claim 1, wherein providing a patient electroanatomic model comprises:

capturing one of a magnetic resonance image or a computer tomography image of a spine, spinal cord, dorsal columns, and dorsal roots of the patient; and

constructing the map based on the one of a magnetic resonance image or a computer tomography image.

5. The method of claim 1, further comprising producing a spinal cord stimulation (SCS) device including electrodes based on the optimal spinal cord stimulation electrode configuration.

6. The method of claim 5, wherein the electrodes of the SCS device correspond to the optimal spinal cord stimulation electrode configuration.

7. The method of claim 5, further comprising:

implanting the SCS device in the patient;

acquiring images of the SCS device and surrounding tissue to assess the location of and tissue response to presence and operation of the SCS device; and

generating another spinal cord stimulation electrode configuration based on the device location and the tissue response.

8. The method of claim 1, wherein generating an optimal spinal cord stimulation electrode configuration comprises optimizing electrode geometry and spacing in a spinal cord stimulation (SCS) device based on the determined differences in activation thresholds.

9. The method of claim 8, further comprising determining stimulation parameters for the electrodes based on the determined differences in activation thresholds.

10. The method of claim 8, wherein the SCS device has one of a longitudinal bipolar and tripolar configuration.

11. The method of claim 1, further comprising:

constructing a curve, p(x), of the proportion of the non-target neural elements activated versus selected proportions of the target neural elements;

calculating the area under the curve;

quantifying stimulation efficiency by calculating electrical energy consumed by a stimulation pulse activating the target neural elements;

determining a cost function of different configurations of the model electrodes based on the calculated area under the curve and the stimulation efficiency.

12. The method of claim 11, wherein quantifying stimulation efficiency comprises applying the following equation:

E=∫

l(t)V(t)dt,

wherein l and V are the applied stimulation voltage and current, respectively.

13. The method of claim 1, wherein generating an optimal spinal cord stimulation electrode configuration comprising selecting one or more of a contact number, a contact polarity, and an electrode position for the model electrodes.

14. The method of claim 13, wherein selecting comprises selecting the one or more of a contact number, a contact polarity, and an electrode position for the model electrodes to utilize minimal energy to activate target neural elements with minimal co-activation of non-target neural elements.

15. The method of claim 13, wherein selecting comprises selecting the one or more of a contact number, a contact polarity, and an electrode position for the model electrodes to minimize a cost function defined by a linear or non-linear combination of weighted measures of selectivity and efficiency.

16. A system of providing a spinal cord stimulation (SCS) device, the system comprising:

at least one processor and memory configured to;

provide an electroanatomical model of a patient spine and spinal cord including a map of target neural elements and non-target neural elements;

use model electrodes to simulate electrical stimulation of the target and non-target neural elements;

determine differences in activation thresholds between the target neural elements and the non-target neural elements in a plurality of different configurations of the model electrodes; and

generate an optimal spinal cord stimulation electrode configuration based on the determined differences in activation thresholds; and

a spinal cord stimulation (SCS) device including electrodes positioned based on the optimal spinal cord stimulation electrode configuration.

17. The system of claim 16, wherein the target neural elements are dorsal column fibers, and the non-target neural elements are dorsal root fibers.

18. The system of claim 16, wherein the at least one processor and memory is configured to apply a model rectangular pulse to the electrodes.

19. The system of claim 16, wherein the at least one processor and memory is configured to:

capture one of a magnetic resonance image or a computer tomography image of a spine, spinal cord, dorsal columns, and dorsal roots of the patient; and

construct the map based on the one of a magnetic resonance image or a computer tomography image.

20. The system of claim 16, the at least one processor and memory is configured to produce a spinal cord stimulation (SCS) device including electrodes based on the optimal spinal cord stimulation electrode configuration.

21. The system of claim 20, wherein the electrodes of the SCS device correspond to the optimal spinal cord stimulation electrode configuration.

22. The system of claim 20, wherein the at least one processor and memory is configured to:

acquire images of the SCS device while implemented in a patient and surrounding tissue to assess the location of and tissue response to operation of the SCS device; and

generate another spinal cord stimulation electrode configuration based on the device location and the tissue response.

23. The system of claim 16, wherein the at least one processor and memory is configured to optimize electrode geometry and spacing in a spinal cord stimulation (SCS) device based on the determined differences in activation thresholds.

24. The system of claim 23, wherein the at least one processor and memory is configured to determining stimulation parameters for the electrodes based on the determined differences in activation thresholds.

25. The system of claim 23, wherein the SCS device has one of a longitudinal bipolar and tripolar configuration.

26. The system of claim 16, wherein the at least one processor and memory is configured to:

construct a curve, p(x), of the proportion of the non-target neural elements activated versus selected proportions of the target neural elements;

calculate the area under the curve;

quantify stimulation efficiency by calculating electrical energy consumed by a stimulation pulse activating the target neural elements;

determine a cost function of different configurations of the model electrodes based on the calculated area under the curve and the stimulation efficiency.

27. The system of claim 26, wherein the at least one processor and memory is configured to apply the following equation:

E=∫

l(t)V(t)dt,

wherein l and V are the applied stimulation voltage and current, respectively.

28. The system of claim 16, wherein the at least one processor and memory is configured to select one or more of a contact number, a contact polarity, and an electrode position for the model electrodes.

29. The system of claim 28, wherein the at least one processor and memory is configured to select the one or more of a contact number, a contact polarity, and an electrode position for the model electrodes to utilize minimal energy to activate target neural elements with minimal co-activation of non-target neural elements.

30. The system of claim 16, wherein the at least one processor and memory is configured to select one or more of a contact number, a contact polarity, and an electrode position for the model electrodes so as to minimize a cost function defined by a linear or non-linear combination of weighted measures of selectivity and efficiency.