Neuromodulation system

Abstract

A neuromodulation system comprising: at least one input means for inputting patient data into the neuromodulation system; at least one model calculation and building means for building a patient model, the patient model describing the anatomy and/or physiology and/or pathophysiology and the real and/or simulated reaction of the patient on a provided and/or simulated neuromodulation; at least one computation means for using the patient model (M) and calculating the impact of the provided and/or simulated neuromodulation. The present invention further relates to a method for providing neuromodulation.

Claims

1. A neuromodulation system comprising: at least one input module for inputting patient data into the neuromodulation system; at least one model calculation and building module for building a patient model, the patient model comprising three-dimensional reconstructions of dorsal roots of the patient, the three-dimensional reconstructions coupled with electrophysiology models of multiple types of nerve fibers included in the three-dimensional reconstructions of the dorsal roots; and at least one computation module for calculating an impact of neuromodulation by determining a difference between a target and a simulated percentage activation of nerve fibers for multiple combinations of dorsal root and nerve fiber type using the three-dimensional reconstructions and the coupled electrophysiology models.

2. The neuromodulation system according to claim 1, wherein the system further comprises an output device for outputting at least one of pre-operative planning data, intra-operative planning data, or post-operative planning data.

3. The neuromodulation system according to claim 2, wherein the pre-operative planning data include at least one of surgical incision placement data, optimal electrode placement data, eligibility data of the patient, and assessment data of in silico benefit for decision making.

4. The neuromodulation system according to claim 2, wherein the intra-operative planning data include at least one intra-operative imaging data, the at least one intra-operative planning data including data acquired via a magnetic resonance imaging (MM), computed tomography (CT), Fluoroimaging, X-Ray, interventional radiology (IR), video, laser measuring, optical visualization and imaging system, real-time registration, navigation system imaging, electroencephalogram (EEG), electrocardiogram (ECG), electromyography (EMG), or mechanical feedback imaging systems.

5. The neuromodulation system according to claim 2, wherein the post-operative planning data include at least one of a recommended electrode configuration, electrode design, plan, stimulation waveforms, or timings schedule for neuromodulation events.

6. The neuromodulation system according to claim 2, wherein output device provides visualization of at least one of electric currents, potentials, information on location, or probability of depolarization of nerve fibers and/or neurons.

7. The system according to claim 1, wherein the patient data is acquired via a patient data acquisition modality communicatively coupled to the input module, the patient data acquisition modality including one of a MRI, a CT, a Fluoroimaging, an X-Ray, an IR, a video, a laser measuring, an optical visualization and imaging system, a real-time registration, a navigation system imaging, an EEG, an ECG, an EMG, or a mechanical feedback imaging system.

8. The system according to claim 1, wherein the at least one model calculation and building module or the at least one computation module is communicatively and operatively coupled to at least one of an implantable pulse generator or a spinal implant having a plurality of electrodes.

9. A method for providing neuromodulation, comprising at least the steps of: inputting patient data of a patient; building a patient model, the patient model comprising three-dimensional reconstructions of dorsal roots of the patient, the three-dimensional reconstructions coupled with electrophysiology models of multiple types of nerve fibers included in the three-dimensional reconstructions of the dorsal roots; and calculating an impact of neuromodulation by determining a difference between a target and a simulated percentage activation of nerve fibers for multiple combinations of dorsal root and nerve fiber type using the three-dimensional reconstructions and the coupled electrophysiology models.

10. The method according to claim 9, further comprising a step of outputting at least one of pre-operative planning data, intra-operative planning data or post-operative planning data.

11. The method according to claim 10, wherein the pre-operative planning data includes at least one of surgical incision placement, optimal electrode placement, eligibility of the patient, or assessment in silico benefit for decision making.

12. The method according to claim 10, wherein, the intra-operative planning data includes at least one intra-operative imaging data, the at least one intra-operative imaging data acquired via a MRI, a CT, a Fluoroimaging, an X-Ray, an IR, a video, a laser measuring, an optical visualization and imaging system, a real-time registration, a navigation system imaging, an EEG, an ECG, an EMG, or a mechanical feedback imaging system.

13. The method according to claim 10 wherein the post-operative planning data includes at least one recommended electrode configuration, electrode design, plan, stimulation waveforms, or timings schedule for neuromodulation events.

14. The method according to claim 9, wherein the method further comprises outputting a visualization of at least one of electric currents; potentials; or information on: locations of neurons or nerve fibers, or probabilities of depolarization of neurons or nerve fibers.

15. The method according to claim 9, further comprising determining a desired placement of a lead comprising a plurality of electrodes according to the patient model.

16. The method according to claim 9, wherein the patient model further comprises a three-dimensional reconstruction of at least one of a spinal cord, a vertebral column, an epidural fat, a pia mater, a dura mater, a ventral root, cerebro-spinal fluid, white matter of the patient, or grey matter of the patient.

17. The method according to claim 9, wherein the patient model further includes a model of a lead, the lead including a plurality of electrodes.

18. The method according to claim 9, further comprising determining, according to the patient model, at least one of an electrode configuration or stimulation parameter for a nerve fiber or neuron population within a spinal cord of the patient.

19. The method according to claim 18, wherein the stimulation parameter includes frequency, amplitude, pulse width or polarity applied to a plurality of electrodes of a lead.

Description

BRIEF DESCRIPTION OF THE FIGURES

(1) Further details and advantages of the present invention shall now be disclosed in connection with the drawings.

(2) FIG. 1 shows a schematic overview of an embodiment of the neuromodulation system according to the present invention, with which the method according to the present invention may be performed;

(3) FIG. 2 shows an example of a patient model build from patient data by the model calculation and building module, according to the present invention as disclosed in FIG. 1;

(4) FIG. 3 shows an example of how a patient model as shown in FIG. 2 is built from patient data by the model calculation and building module, according to the present invention as disclosed in FIG. 1;

(5) FIG. 4 shows an example of optimization of electrode position and stimulation configuration with the system disclosed in FIG. 1;

(6) FIG. 5 shows an example of neurofunctionalization with the system disclosed in FIG. 1; and

(7) FIG. 6 shows a high level flow chart illustrating an example method for patient-specific neuromodulation.

DETAILED DESCRIPTION

(8) FIG. 1 shows a schematic overview of an embodiment of the neuromodulation system 10 according to the present invention, with which the method according to the present invention may be performed. The system 10 may include a device 102 with an input module 112, a model calculation and building module 14, a computation module 16, a memory 104, a processor 106, and a communication subsystem 108, though other components and modules may also be included as known to those of skill in the art including, but not limited to, a controller, a microcontroller, a telemetry system and/or a training device. Further, additionally or alternatively, one or more of the input module 12, the model calculation and building module 14, and the computation module 16 may include one or more processors, such as processor 106, and memory, such as memory 106.

(9) In some aspects, as shown in FIG. 1, the device 102 may be coupled to a user input device 121, an output device 124, an electrode array 126 comprising one or more electrodes, a pulse generator 128, and one or more sensors 130. In one example, the output device may be a display screen, or a portion of a display screen. While the device 102 is shown with a plurality of peripheral devices, the particular arrangement may be altered by those of skill in the art such that some or all of the components are incorporated in a single or plurality of devices as desired.

(10) Collectively, the various tangible components or a subset of the tangible components of the neuromodulation system may be referred to herein as “logic” configured or adapted in a particular way, for example as logic configured or adapted with particular software, hardware, or firmware and adapted to execute computer readable instructions. The processors may be single core or multicore, and the programs executed thereon may be configured for parallel or distributed processing. The processors may optionally include individual components that are distributed throughout two or more devices, which may be remotely located and/or configured for coordinated processing. One or more aspects of the logic subsystem may be virtualized and executed by remotely accessible networked computing devices configured in a cloud computing configuration, that is, one or more aspects may utilize ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Clouds can be private, public, or a hybrid of private and public, and may include Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). In some aspects, logic and memory may be integrated into one or more common devices, such as an application specific integrated circuit, field programmable gate array, or a system on a chip.

(11) In some embodiments, device 102 may be any computing or mobile device, for example, mobile devices, tablets, laptops, desktops, PDAs, and the like, as well as virtual reality devices or augmented reality devices. Thus, in some embodiments, the device 102 may include an output device, and thus a separate output device 124 or user input device 121 may not be necessary. In other aspects, the device may be coupled to a plurality of displays.

(12) Memory 104 generally comprises a random-access memory (“RAM”) and permanent non-transitory mass storage device, such as a hard disk drive or solid-state drive. Memory 104 may store an operating system as well as the various modules and components discussed herein. It may further include devices which are one or more of volatile, non-volatile, dynamic, static, read/write, read-only, random access, sequential access, location addressable, file addressable and content addressable.

(13) Communication subsystem 108 may be configured to communicatively couple the modules within device 102 as well as communicatively coupling device 102 with one or more other computing and/or peripheral devices. Such connections may include wired and/or wireless communication devices compatible with one or more different communication protocols including, but not limited to, the Internet, a personal area network, a local area network (LAN), a wide area network (WAN) or a wireless local area network (WLAN). For example, wireless connections may be WiFi, Bluetooth®, IEEE 802.11, and the like.

(14) As shown in FIG. 1,

(15) The system 10 comprises an input module 12.

(16) The input module 12 is configured for inputting patient data D into the neuromodulation system 10. In one example, patient data D may be acquired via a patient data acquisition modality 140, which may be one of MRI, CT, Fluoroimaging, X-Ray, IR, video, laser measuring, optical visualization and imaging means, real-time registration, navigation system imaging, EEG, ECG, EMG, mechanical feedback and the like.

(17) Alternatively, the system 10 could comprise more than one input module 12.

(18) The system 10 further comprises a model calculation and building module 14.

(19) The model calculation and building module 14 is configured for building a patient model M, the patient model M describing the anatomy and/or physiology and/or pathophysiology and the real and/or simulated reaction of the patient on a provided and/or simulated neuromodulation. For example, the model calculation and building module 14 may generate the patient model M according to patient data D input via the input module 12.

(20) Alternatively, the system 10 could comprise more than one model calculation and building module 14.

(21) The system 10 further comprises a computation module 16.

(22) The computation module 16 is configured for using the patient model M and calculating an impact of a provided and/or simulated neuromodulation. In one example, calculating the impact may be include calculating one or more neurofunctionalization parameters including but not limited to one or more of electric currents, potentials, information on the location and/or probability of the depolarization of nerve fibers and/or neurons. The one or more neurofunctionalization parameters may enable visualization of excitation of target nerves in order to better understand neuromodulation and/or neuromodulation therapy.

(23) Alternatively, the system 10 could comprise more than one computation module 16.

(24) In this embodiment, the input module 12 is connected to the model calculation and building module 14.

(25) The connection between the input module 12 and the model calculation and building module 14 is a direct and bidirectional connection.

(26) However, in an alternative embodiment, an indirect and/or unidirectional connection could be generally possible.

(27) In this embodiment, the connection between the input module 12 and the model calculation and building module 14 is a wireless connection.

(28) However, in an alternative embodiment, a cable-bound connection could be generally possible.

(29) In this embodiment, the input module 12 is connected to computation module 16.

(30) The connection between the input module 12 and the computation module 16 is a direct and bidirectional connection.

(31) However, in an alternative embodiment, an indirect and/or unidirectional connection could be generally possible.

(32) In this embodiment, the connection between the input module 12 and the computation module 16 is a wireless connection.

(33) However, in an alternative embodiment, a cable-bound connection could be generally possible.

(34) In this embodiment, the model calculation and building module 14 is connected to computation module 16.

(35) The connection between the model calculation and building module 14 and the computation module 16 is a direct and bidirectional connection.

(36) However, in an alternative embodiment, an indirect and/or unidirectional connection could be generally possible.

(37) In this embodiment, the connection between the model calculation and building module 14 and the computation module 16 is a wireless connection.

(38) However, in an alternative embodiment, a cable-bound connection could be generally possible.

(39) In this embodiment, the input module 12 inputs patient data D on the anatomy and/or physiology and/or pathophysiology of a patient into the system 10.

(40) In other words, the input module 12 reads patient data D.

(41) In this embodiment, the patient is a patient suffering from SCI.

(42) In this embodiment, the patient is a patient suffering from motor dysfunction.

(43) In an alternative embodiment, the patient could be a patient suffering from impaired motor dysfunction and/or impaired autonomic function.

(44) In this embodiment, patient data D are obtained by one of MRI, CT, Fluoroimaging, X-Ray, IR, video, laser measuring, optical visualization and imaging means, real-time registration, navigation system imaging, EEG, ECG, EMG, mechanical feedback and the like.

(45) In this embodiment, the model calculation and building module 14 builds, based on the patient data D provided by the input module 12, a patient model M.

(46) In this embodiment, the patient model M describes the anatomy of the patient and the real reaction of the patient on provided neuromodulation.

(47) Alternatively, and/or additionally, the patient model M could describe the physiology and/or pathophysiology and the simulated reaction of the patient on provided and/or simulated neuromodulation.

(48) In this embodiment, the computation module 16 uses the model M and calculates the impact of the provided neuromodulation.

(49) Not shown in FIG. 1 is that the system 10 may further comprise an output device for outputting at least one of pre-operative planning data, intra-operative planning data and/or post-operative planning data. Additionally or alternatively, the one or more of pre-operative planning data, intra-operative planning data and post-operative planning data may be output via the output device 124 coupled to the system 10, as shown in FIG. 1.

(50) Not shown in FIG. 1 is that the pre-operative planning data could include at least one of surgical incision placement, optimal electrode E placement, eligibility of the patient, assessment of in-silico benefit for decision making, cf. FIG. 4.

(51) Not shown in FIG. 1 is that the intra-operative planning data could include at least one intra-operative imaging data such as MRI, CT, Fluoroimaging, X-Ray, IR, video, laser measuring, optical visualization and imaging module, real-time registration, navigation system imaging, EEG, ECG, EMG, mechanical feedback and the like, cf. FIGS. 2 and 3.

(52) Not shown in FIG. 1 is that the post-operative planning data could include at least one recommend optimum electrode E configuration, electrode E design, plan, stimulation waveforms, timings schedule for neuromodulation events and the like.

(53) Not shown in FIG. 1 is that the output device could provide visualization, e.g. 3D visualization, of at least one of electric currents, potentials, information on the location and/or probability of the depolarization of nerve fibers and/or neurons, cf. FIG. 5.

(54) Not shown in FIG. 1 is that the system 10 is a system for restoring motor and/or autonomic function in a patient.

(55) Not shown in FIG. 1 is that the system could enable to determine optimal stimulation parameters (such as frequency, amplitude and/or pulse width) for the specific recruitment of Act nerve fibers of at least one dorsal root.

(56) In general, one or more processors of the system 10 may include executable instructions in non-transitory memory that when executed may perform a method for providing neuromodulation, the method comprising at least the steps of: inputting patient data D; building a patient model M, the patient model M describing the anatomy and/or physiology and/or pathophysiology and the real and/or simulated reaction of the patient on provided and/or simulated neuromodulation; calculating the impact of the provided and/or simulated neuromodulation.

(57) The method could further comprise the step of outputting at least one of pre-operative planning data, intra-operative planning data and/or post-operative planning data.

(58) The method could further comprise the step of providing visualization of at least one of electric currents, potentials, information on the location and/or probability of the depolarization of nerve fibers and/or neurons are provided.

(59) FIG. 2 shows an example of a patient model 250 (e.g., patient model M described above with respect to FIG. 1) built by the model calculation and building module 14 according to the present invention as disclosed in FIG. 1. The patient model 250 may be generated by using patient data D from an imaging scan 200 acquired via a modality, such as clinical 3T MRI modality.

(60) In this embodiment, the model calculation and building module 14 of the system 10 disclosed in FIG. 1 builds a patient model 250 describing the anatomy of a patient.

(61) In this embodiment, the system 10 further comprises an output device for outputting intra-operative planning data.

(62) In this embodiment, the output device is connected to the input module 12, the model calculation and building module 14 and the computation module 16 of the system 10 via a wireless connection.

(63) In this embodiment, the connection is a wireless connection and bidirectional connection.

(64) However, in an alternative embodiment, a wired (e.g., a cable-bound) and/or unidirectional connection could be generally possible.

(65) In an alternative embodiment, the output device could be connected to only one or at least one of to the input module 12, the model calculation and building module 14 and the computation module 16 of the system 10.

(66) In this embodiment, the model calculation and building module 14 builds a patient model 250 based on patient data D.

(67) In this embodiment, the patient data D is intra-operative planning data.

(68) In this embodiment, the patient data D is imaging data obtained by a 3T MRI scanner. In some embodiments, the patient data D is imaging data obtained by an MRI scanner.

(69) In this embodiment, the patient model 250 is a 3D reconstruction of the patient data 200.

(70) In other words, the patient model 250 is a 3D reconstruction of the MRI scan.

(71) In this embodiment, the output device provides visual information via a display.

(72) In other words, the shown embodiment is, at least partly, visual information provided by the output device.

(73) In this embodiment, the output device provides the patient model 250 built by the model calculation and building module 14.

(74) In an alternative embodiment, the patient model 250 could be or could comprise a 2D reconstruction of the patient data D.

(75) In this embodiment the patient model 250 comprises a 3D reconstruction of the spinal cord S, vertebrates V, epidural fat EF, pia mater PM, dura mater DM, dorsal roots P, ventral roots A, cerebro-spinal fluid CSF, the white matter W and the grey matter G of a patient.

(76) In this embodiment, the patient model 250 is combined with a model of a lead L comprising multiple electrodes for providing neuromodulation.

(77) Not shown in this embodiment is that the computation module 16 uses the patient model 250 and calculates the impact of the neuromodulation provided by the lead L.

(78) Not shown in this embodiment is that, via a user interface of the output device, a user could edit the patient model 250, e.g. by zooming in and/or zooming out and/or rotating and/or adding and/or changing colors.

(79) FIG. 3 schematically shows an example of how a patient model, such as patient model 250 as shown in FIG. 2 is built from patient data D by a model calculation and building module of a system, such as the model calculation and building module 14 of system 10 according to the present invention as disclosed in FIG. 1. In the present example, the patient data D acquired via a clinical MRI scan is shown at 302. The model calculation and building module may then employ a segmentation algorithm to generate a segmented image 304 using the patient data D. Upon segmentation, a model 306, may be generated by the model calculation and building module. The model 306 is depicted as a 3D model; it will be appreciated that other types of models may be generated using patient data D.

(80) In this embodiment, the system further comprises an output device for outputting patient data D, which may include intra-operative planning data. In one example, the patient data D may be output via a display portion 310 of the output device.

(81) In this embodiment, the output device is connected to an input module, such as the input module 12, the model calculation and building module and a computation module, such as computation module 16 of the system 10 via a wireless connection, cf. FIG. 1.

(82) In this embodiment, the intra-operative planning data is an MRI image.

(83) In this embodiment, the output device provide visual information via a display portion 310 of a display. In some examples, the patient data D (that is, MRI image in this example) shown at 302, the segmented image 304, and the model 306 may be displayed adjacent to each other on the display. Alternatively, the display may output a user-selected image (e.g., user may select a desired image and/or data to view via the display).

(84) In this embodiment, the output device provide the patient model 306 build by the model calculation and building module 14. Another example patient model M is shown at FIG. 2.

(85) Not shown in FIG. 3 is that in general, the system enables semi-automatic reconstruction of patient's anatomy, such as the spinal cord S, the vertebrae V, the epidural fat EF, the pia mater PM, the dura mater DM, the posterior roots or dorsal roots P, the anterior roots or ventral roots A, the rootlets R, the cerebro-spinal fluid CSF, the white matter W, the grey matter G, the intervertebral discs I, based on image thresholding and/or Kalman-filtering and/or various algorithms.

(86) Not shown in FIG. 3 is that in generally, a computational pipeline could be established by the system 10 to automatically create 2D and/or 3D models, e.g. 3D Finite Element Method models (FEM), from these reconstructions, to obtain anisotropic tissue property maps, discretize the model, perform simulations using an electro-quasi-static solver and couple these simulations with electrophysiology models of the spinal cord and/or dorsal roots.

(87) In this embodiment, the system, via model 306, describes a patient's anatomy in terms of every tissue in the spinal cord S area.

(88) In this embodiment, the system, via model 306, describes a patient's anatomy in terms of a volume of every tissue in the spinal cord S area.

(89) In this embodiment, the system, via model 306, describes the patient's anatomy in terms of every tissue in the spinal cord S area, including crucial trajectories of the spinal roots R, enabling to segment out all tissues including the spinal roots R for an individual patient and to implement spinal rootlets to fit the geometrical area between the entry point of one root versus the next.

(90) FIG. 4 shows an example of optimization of electrode E position and stimulation configuration with the system 10 disclosed in FIG. 1.

(91) In this embodiment, the system 10 disclosed in FIG. 1 further comprises output device for outputting pre-operative planning data, cf. FIGS. 2, 3.

(92) In this embodiment, the output device provide visual information via a display.

(93) In other words, the shown embodiment is, at least partly, visual information provided by the output device.

(94) In general, the output device may comprise a user interface, enabling the user to change pre-operative planning data.

(95) In this embodiment, the pre-operative planning data comprise optimal electrode E placement.

(96) In other words, the system 10 enables optimal placement of a lead L comprising multiple electrodes E.

(97) In this embodiment a lead L comprising multiple electrodes E is superimposed on a patient model M.

(98) In general, EES can be utilized for enabling motor functions by recruiting large-diameter afferent nerve fibers within the dorsal roots P.

(99) Electrode E positioning and stimulation configuration has an immense effect on the selectivity of this recruitment pattern and is dependent on the anatomy of each subject.

(100) In this embodiment, the system 10 disclosed in FIG. 1 is used for optimization of electrode E position and stimulation configuration for enabling motor function.

(101) In this embodiment, left hip flexors and right ankle extensors should be stimulated with a lead L comprising multiple electrodes E.

(102) In this embodiment, L1 and S2 dorsal roots should be stimulated by electrodes E of the lead L

(103) In particular, the cost function could be:

(104) Calculate the selectivity through a distance function
dist(j)=sqrt[(sum_i(w_i*(x_desired_i(j)−x_achieved_i(j))))**2]
with x being the percentage of a specific type of nerve fiber being activated within one dorsal root P and i being a combination of dorsal roots P and neve fiber types that have been initialized and j being the current used; Reiterate the selectivity index for a multitude of different lead L positions; Find the minimal distance among all lead L positions; Take the dist(j) function for that position for all possible active sites; Minimize it through superposition of the active sites to calculate the multipolar configuration.

(105) Alternatively, and/or additionally, the system 10 could optimize electrode E position and stimulation configuration for treatment of autonomic dysfunction.

(106) FIG. 5 shows an example of neurofunctionalization with the system 10 disclosed in FIG. 1.

(107) In this embodiment, the system 10 disclosed in FIG. 1 further comprises output device, cf. FIG. 2.

(108) In this embodiment, the output device provide visual information on a display.

(109) In other words, the shown embodiment is visual information provided by the output device.

(110) In general, the output device could provide visualization of at least one of electric currents, potentials, information on the location and/or probability of the depolarization of nerve fibers and/or neurons.

(111) In general, the output device could provide 3D visualization.

(112) In this embodiment, the output device provides neurofunctionalization of a patient 3D FEM model M.

(113) In this embodiment, spinal cord S, grey matter G, white matter W and dorsal roots R comprising myelinated axons AX (nerve fibers) are shown.

(114) In this embodiment, simulations are performed using an electro-quasi-static solver.

(115) In this embodiment, simulations of excitation after provided neuromodulation are performed.

(116) In this embodiment, the simulations are coupled with electrophysiology models.

(117) In this embodiment, the simulations are coupled with a nerve fiber-based electrophysiology model.

(118) In this embodiment, a myelinated axon AX is shown in detail.

(119) In this embodiment, a myelinated fiber AX (e.g. Aα-sensory fiber) with nodes of Ranvier N is shown. Nodes of Ranvier N are uninsulated and enriched in ion channels, allowing them to participate in the exchange of ions required to regenerate the action potential.

(120) In this embodiment, the output device provide visualization of information on the location of the depolarization of a nerve fiber, in particular an axon AX after providing neuromodulation to the spinal cord S.

(121) Finally, this embodiment illustrates some components of a compartmental cable model by showing the lumped elements used to model the ion-exchange at the nodes of Ranvier N.

(122) In general, realistic compartmental cable models can automatically be created within the personalized 3D FEM models, including but not limited to, Aα-, Aβ-, Aδ-, C-sensory fibers, interneurons, α-motoneurons and efferent nerves, as well as dorsal column projections. In an alternative embodiment, the output device could provide visualization of information on the location and/or probability of the depolarization of nerve fibers and/or neurons.

(123) In general, the system 10 could automatically determine the optimal stimulation parameters for recruiting a nerve fiber and/or neuron population with the spinal cord of a patient.

(124) Turning to FIG. 6, it shows a high-level flowchart illustrating an example method 600 for providing neuromodulation according to one or more of a patient's individual anatomy, need, and response. Method 600 is described with regard to systems, components, and methods of FIGS. 1-5, though it should be appreciated that method 600 may be implemented with other systems, components, and methods without departing from the scope of the present disclosure. Method 600 may be implemented as computer executable instruction in the memory 104 executed by the processor 106 of the device 102.

(125) At 602, method 600 includes inputting patient data. Inputting patient data includes reading imaging datasets via an input module, such as input module 12, from a modality, such as modality 140. Example modalities that may be used to acquire the patient data may include MRI, CT, Fluoroimaging, X-Ray, IR, video, laser measuring, optical visualization and/or other imaging module, real-time registration, navigation system imaging, EEG, ECG, EMG, mechanical feedback and the like.

(126) At 604, method 600 includes generating a patient model, such as patient model 250 and 306, and/or generating one or more of real reaction and simulated reaction of the patient in response to one or more of a provided neuromodulation and a simulated neuromodulation. The generation of the patient model and/or one or more of the real reaction and the simulated reaction may be performed via a model calculation and building module, such as model calculation and building module 14 at FIG. 1. Generating the patient model and/or generating one or more of real reaction and simulated reaction of the patient includes, at 606, generating and outputting (e.g., output via output device 124 of system 10 and/or a output device within system 10) one or more of pre-operative planning data, intra-operative planning data, and post-operative planning data. The pre-operative planning data may include at least one of surgical incision placement, optimal electrode placement, eligibility of the patient, in-silico assessment of benefit for decision making. The intra-operative planning data may include at least one intra-operative imaging data such as MRI, CT, Fluoroimaging, X-Ray, IR, video, laser measuring, optical visualization and imaging, real-time registration, navigation system imaging, EEG, ECG, EMG, mechanical feedback and the like. The post-operative planning data may include at least one recommended optimum electrode configuration, stimulation waveforms, timings schedule for neuromodulation events and the like. Further, at 606, one or more of electric currents, potentials, information on the location and/or probability of the depolarization of nerve fibers and/or neurons may be generated and output.

(127) Those having skill in the art will appreciate that there are various logic implementations by which processes and/or systems described herein can be affected (e.g., hardware, software, and/or firmware), and that the preferred vehicle will vary with the context in which the processes are deployed. “Software” refers to logic that may be readily readapted to different purposes (e.g. read/write volatile or nonvolatile memory or media). “Firmware” refers to logic embodied as read-only memories and/or media. Hardware refers to logic embodied as analog and/or digital circuits. If an implementer determines that speed and accuracy are paramount, the implementer may opt for a hardware and/or firmware vehicle; alternatively, if flexibility is paramount, the implementer may opt for a solely software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware. Hence, there are several possible vehicles by which the processes described herein may be effected, none of which is inherently superior to the other in that any vehicle to be utilized is a choice dependent upon the context in which the vehicle will be deployed and the specific concerns (e.g., speed, flexibility, or predictability) of the implementer, any of which may vary.

(128) The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood as notorious by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. Several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in standard integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and/or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies equally regardless of the particular type of signal bearing media used to actually carry out the distribution. Examples of a signal bearing media include, but are not limited to, the following: recordable type media such as floppy disks, hard disk drives, CD ROMs, digital tape, flash drives, SD cards, solid state fixed or removable storage, and computer memory.

(129) In a general sense, those skilled in the art will recognize that the various aspects described herein which can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or any combination thereof can be viewed as being composed of various types of “circuitry.” Consequently, as used herein “circuitry” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one Application specific integrated circuit, circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), circuitry forming a memory device (e.g., forms of random access memory), and/or circuits forming a communications device. (e.g., a modem, communications switch, or the like)

(130) It will be appreciated that the configurations and routines disclosed herein are exemplary in nature, and that these specific embodiments are not to be considered in a limiting sense, because numerous variations are possible. The subject matter of the present disclosure includes all novel and non-obvious combinations and sub-combinations of the various systems and configurations, and other features, functions, and/or properties disclosed herein.

(131) The following claims particularly point out certain combinations and sub-combinations regarded as novel and non-obvious. Such claims should be understood to include incorporation of one or more such elements, neither requiring nor excluding two or more such elements. Other combinations and sub-combinations of the disclosed features, functions, elements, and/or properties may be claimed through amendment of the present claims or through presentation of new claims in this or a related application. Such claims, whether broader, narrower, equal, or different in scope to the original claims, are also regarded as included within the subject matter of the present disclosure.