US20210153942A1

US20210153942A1 - Neuromodulation system

Info

Publication number: US20210153942A1
Application number: US17/106,030
Authority: US
Inventors: Mathieu SCHELTIENNE; Edoardo Paoles; Jeroen Tol; Jurriaan Bakker
Original assignee: GTX Medical BV
Current assignee: Onward Medical NV
Priority date: 2019-11-27
Filing date: 2020-11-27
Publication date: 2021-05-27
Also published as: DE19211698T1; US11839766B2; EP3827871A1; US20210154475A1

Abstract

Neuromodulation systems and corresponding methods for providing neuromodulation are disclosed. The neuromodulation systems can include at least one input module for inputting patient data into the neuromodulation system. The systems can further include at least one model calculation and building module 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. The systems can further include at least one computation means for using the patient model (M) and calculating the impact of the provided and/or simulated neuromodulation.

Description

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority to European Patent Application No. 19211698.6 filed on Nov. 27, 2019. The entire contents of the above-listed application is hereby incorporated by reference for all purposes.

TECHNICAL FIELD

Disclosed embodiments relate to a neuromodulation system, in particular a neuromodulation system for restoring motor function and/or autonomic function in a patient suffering from impaired motor and/or autonomic function after spinal cord injury (SCI) or neurologic disease.

BACKGROUND AND SUMMARY

Decades of research in physiology have demonstrated that the mammalian spinal cord embeds sensorimotor circuits that produce movement primitives. These circuits process sensory information arising from the moving limbs and descending inputs originating from various brain regions in order to produce adaptive motor behaviors.
SCI interrupts the communication between the spinal cord and supraspinal centers, depriving these sensorimotor circuits from the excitatory and modulatory drives necessary to produce movement.
Epidural Electrical Stimulation (EES) of the spinal cord is a clinically accepted method for the treatment of chronic pain and has been approved by the Food and Drug Administration (FDA) since 1989. Recently, several preclinical and clinical studies have demonstrated the use of EES applied to the lumbo-sacral levels of the spinal cord for the improvement of leg motor control after spinal cord injury. For example, EES has restored coordinated locomotion in animal models of SCI, and isolated leg movements in individuals with motor paralysis.
Moreover, EES can potentially be used for treatment of autonomic dysfunction. Autonomic dysfunction may comprise altered and/or impaired regulation of at least one of blood pressure, heart rate, thermoregulation (body temperature), respiratory rate, immune system, gastro-intestinal tract (e.g. bowel function), metabolism, electrolyte balance, production of body fluids (e.g. saliva and/or sweat), pupillary response, bladder function, urethral or anal sphincter function, or sexual function.
Moreover, EES can potentially be used for treatment of autonomic dysreflexia, spasticity, altered and/or impaired sleep behavior and/or pain. EES as a neuromodulation strategy can work by recruiting specific neuron populations through direct and indirect pathways. In the case of recovery of locomotion, EES applied over the lumbosacral spinal cord activates large-diameter, afferent fibers within the posterior roots which in turn activate motoneuron pools through synaptic connections, which in turn activate the muscles innervated by the corresponding neurons. Hence, specific spinal roots are linked to specific motor functions.
EP 3184145 A1 discloses systems for selective spatiotemporal electrical neurostimulation of the spinal cord. A signal processing device receiving signals from a subject and operating signal-processing algorithms to elaborate stimulation parameter settings is operatively connected with an Implantable Pulse Generator (IPG) receiving stimulation parameter settings from said signal processing device and able to simultaneously deliver independent current or voltage pulses to one or more multiple electrode arrays. The electrode arrays are operatively connected with one or more multi-electrode arrays suitable to cover at least a portion of the spinal cord of said subject for applying a selective spatiotemporal stimulation of the spinal circuits and/or dorsal roots, wherein the IPG is operatively connected with one or more multi-electrode arrays to provide a multipolar stimulation. Such system allows achieving effective control of locomotor functions in a subject in need thereof by stimulating the spinal cord, in particular the dorsal roots, with spatiotemporal selectivity.
In order to activate a muscle selectively a specific electric field can be generated within the spinal cord of a patient. The spatial characteristics of this electrical field can depend on the anatomical dimensions of the patient. However, anatomical dimensions can vary greatly between subjects. In order to increase efficacy and safety of ESS the position and configuration of the stimulation paradigms should be known prior to the surgical implantation of the spinal cord implant.
US 2018104479 A1 discloses systems, methods, and devices for optimizing patient-specific stimulation parameters for spinal cord stimulation, in order to treat pain. A patient-specific anatomical model is developed based on one or more pre-operative images, 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 model's 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.
The disclosed embodiments can support improved placement of a spinal implant (e.g. a lead comprising multiple electrodes) in a patient suffering from impaired motor and/or autonomic function after SCI or neurologic disease. A neuromodulation system consistent with the disclosed embodiments can include 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 describing at least one of an anatomy and/or physiology, pathophysiology, or a real (or simulated) reaction of the patient to a provided (or simulated) neuromodulation; and at least one computation module for using the patient model and calculating the impact of the provided (or simulated) neuromodulation.
Disclosed embodiments can provide a multi-layer computational framework for the design and personalization of stimulation protocols. EES protocols for neuromodulation purposes for a patient can be provided in order to enable patient-specific neuromodulation. The disclosed embodiments include a pipeline combining image thresholding and Kalman-filtering and/or specific algorithms for at least partially automatically reconstructing the patient's anatomy, such as the spinal cord, the vertebrae, the epidural fat, the pia mater, the dura mater, the posterior roots or dorsal roots, the anterior roots or ventral roots, the rootlets, the cerebro-spinal fluid (CSF), the white matter, the grey matter and/or the intervertebral discs from a dataset obtained by an imaging method. The disclosed embodiments further include a pipeline for automatically creating 2D and/or 3D model(s), e.g. 3D Finite Element Method models (FEM), from these reconstructions, obtaining anisotropic tissue property maps, discretizing the automatically created model(s), perform simulations using an electro-quasi-static solver and couple these simulations with electrophysiology models, in particular neuron-based and/or nerve fiber based electrophysiology models, of the spinal cord and/or dorsal roots. These pipelines can be implemented using at least one input module, at least one model calculation and building module, and at least one computation module. Overall, patient-specific neuromodulation, specifically adapted to the patient's needs and anatomy, may be enabled.
The system may be used in a method for the treatment of motor impairment and/or restoring motor function. Motor function may comprise all voluntary postures and movement patterns, such as locomotion. The system may be used in a method for the treatment of autonomic dysfunction and/or restoring autonomic function. The system may be used in a method for the treatment of autonomic dysreflexia, spasticity, altered and/or impaired sleep behavior and/or pain.
In some embodiments, the system can be used to configure a neuromodulation system based on patient data and/or feedback information (e.g. as a generic system decoupled from an implanted neuromodulation system).
In some embodiments, the system can enable detailed modeling of a patient's anatomy. The system can model tissue in the spinal cord, including trajectories of the spinal roots (dorsal and/or ventral roots). The system can segment out such tissues, including the spinal roots for an individual patient. The system can model spinal rootlets fitting the geometrical area between the entry point of one spinal root versus the next.
In some embodiments, a computational pipeline to automatically create anisotropic tissue property maps in the 3D reconstruction and overlay them as conductivity maps over the 3D FEM model may be provided.
In some embodiments, the system can establish a computational pipeline to automatically create topologically and neurofunctionally realistic compartmental cable models 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.
The system may be enabled to determine optimal stimulation parameters (such as frequency, amplitude and/or pulse width and/or polarity) and/or optimal electrode configuration for the specific recruitment of Act nerve fibers of at least one dorsal root. In particular, the system may enable determination of improved stimulation parameters and/or improved electrode configuration for the specific recruitment of Act nerve fibers (but not all fibers) of at least one dorsal root. In particular, the system may enable determination of improved stimulation parameters and/or improved electrode configuration for the specific recruitment of Act nerve fibers but not of AP nerve fibers and/or AS nerve fibers and/or C nerve fibers of the at least one dorsal root. In particular, the system may enable determination of improved stimulation parameters (such as frequency, amplitude and/or pulse width, and/or polarity) and/or improved electrode configuration for the specific recruitment of Act nerve fibers in at least one dorsal root but not AP nerve fibers in the dorsal column. These improvement in selective stimulation or recruitment can support improvements in elicitation of motor responses. The disclosed embodiments can provide improved neuromodulation, which may at least partially restore motor function, thereby benefiting patient with SCI and/or motor dysfunction. Alternatively, and/or additionally, the improved neuromodulation can at least partially restore autonomic function.
In particular, a cost function for optimizing lead position may be used to determine a selectivity index. For example, the selectivity index may be calculated 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 and i being a combination of dorsal roots and neve fiber types that have been initialized and j being the current used. The determination can include:
Recalculating the selectivity index for a multitude of different lead positions;
Find the minimal distance among all lead 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.
In some embodiments, the system may establish a pipeline to couple the results of a previous calculation to the compartmental cable models to calculate the depolarization of individual nerve fibers and/or neurons as well as the travelling of action potentials. In some embodiments, the electrophysiological response may be validated in personalized models created through this pipeline against their real-life counterparts. In some embodiments, this may enable to decode the mechanisms of neuromodulation as well as explore neural circuitry, especially specifically for a person with spinal cord injury and/or injury of nerve fibers (also referred to as a patient).
In some embodiments, this framework may be used to determine the optimal placement of a spinal implant, such as a lead and/or an electrode array, in an individual subject prior to the actual surgery. Additionally, and/or alternatively, a genetic algorithm may automatically determine the optimal stimulation paradigms for recruiting a nerve fiber and/or neuron population within the spinal cord of the subject.
EES may be utilized for enabling motor functions by recruiting large-diameter afferent nerve fibers within the posterior roots. Electrode positioning and/or stimulation configuration may affect the selectivity of this recruitment pattern and may be dependent on the anatomy of each subject. Currently these parameters can only be determined by time-consuming, invasive, and often unsuccessful trial and error procedures. In some embodiments, the system may enable improvement of electrode position and/or stimulation configuration for enabling improved motor function as the computational pipeline of the system enables that these parameters can be determined automatically and non-invasively for each subject and/or patient.
Similarly, EES may affect the autonomic nervous system through activation of specific spinal roots. The determination of electrode position and/or a stimulation protocol may follow similar logic as for motor function but may have a different goal. In some embodiments, the system may enable optimization of electrode position and stimulation configuration for the treatment of autonomic dysfunction.
In some embodiments, the system may enable development of improved electrode arrays and/or leads and/or electrode designs for neuromodulation therapies (e.g., for patient-specific neuromodulation therapies). Disclosed embodiments can support assessment, prior to surgery, of the suitability of leads (e.g., in a lead portfolio with different sizes/electrode configurations) for an individual patient. Conventional selection or design of electrodes and/or electrode arrays and/or leads for neuromodulation can depend on experience and extensive testing in animals and humans. Such testing can be expensive, time-consuming, ineffective, and hazardous. The disclosed embodiments may provide a virtual population of personalized computational models may be created from imaging datasets to optimize the electrode and/or electrode array and/or lead design in-silico, before testing safety and efficacy in-vivo. In some embodiments, this may also reduce the number of animals required for animal studies.
In some embodiments, the input module may be configured and arranged for reading imaging datasets, e.g. from MRI, CT, Fluoroimaging, X-Ray, IR, video, laser measuring, optical visualization and/or other imaging systems, real-time registration, navigation system imaging, EEG, ECG, EMG, mechanical feedback and the like.
In some embodiments, imaging datasets may be or may comprise high-resolution imaging datasets on individual subjects and/or patients. In some embodiments, high-resolution imaging datasets may be obtained by high-resolution imaging machines that have the capacity to reveal the complete anatomy of the spinal cord, the vertebrae, the epidural fat, the pia mater, the dura mater, the posterior roots/dorsal roots, the anterior roots/ventral roots, the rootlets, the white matter, the grey matter, the intervertebral discs and/or the CSF of individual patients.
The input module may enable a user, e.g. a therapist, a physiotherapist, a physician, a trainer, a medical professional and/or a patient directly to provide patient data. In some embodiments, the input module may be or may comprise a user interface of an input device.
In some embodiments, the system may further comprise an output device, such as a display unit, for outputting at least one of pre-operative planning data, intra-operative planning data and/or post-operative planning data. In some embodiments, the output device may provide visual information concerning or representing the pre-operative planning data, intra-operative planning data and/or post-operative planning data. In some embodiments, such visual information can provide a user (e.g. a surgeon and/or therapist) with anatomical and/or physiological and/or pathophysiological data concerning a patient, which can support selection of optimal neuromodulation therapy configurations.
In some embodiments, 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. In some embodiments, this has the advantage that optimal stimulation, specifically adapted to a patient's needs is enabled and/or surgery procedures are kept as short as possible, without harming the patient by unnecessary trial-and error procedures.
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. This has the advantage that the patient's anatomy including any injured tissue and/or anatomical peculiarities and/or physiology and/or pathophysiology is revealed, and the planned therapy can be adapted specifically to the patient's needs.
In some embodiments, the post-operative planning data may include at least one recommend optimum electrode configuration, stimulation waveforms, timings schedule for neuromodulation events and the like. This may enable that the neuromodulation and/or neuromodulation therapy may be adapted to specific tasks and, at the same time, to the patient's needs. Overall, this may enable optimal neuromodulation outcome.
In some embodiments the output device may 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. In some embodiments, this may be referred to as neurofunctionalization, enabling visualization of excitation of target nerves in order to better understand neuromodulation and/or neuromodulation therapy.
In some embodiments, the system may be used for percutaneous electrical stimulation, transcutaneous electrical nerve stimulation (TENS), epidural electrical stimulation (EES), subdural electrical stimulation (SES), functional electrical stimulation (FES) and/or all neurostimulation and/or muscle stimulation applications.
Further, the system may additionally comprise at least one of a sensor, a sensor network, a controller, a programmer, a telemetry module, a communication module, a stimulator, e.g. an implantable pulse generator and/or a lead comprising an electrode array comprising at least one electrode (up to multiple electrodes).
Alternatively and/or additionally, the system may be connected to a system comprising at least one of a sensor, a sensor network, a controller, a programmer, a telemetry module, a communication module, a stimulator, e.g. an implantable pulse generator, a lead comprising multiple electrodes and/or a memory, wherein stimulation parameters and/or electrode configuration and/or tasks may be stored in the memory and the patient may start training without post-operative functional mapping.
Further, the system may be implemented using one or more computing devices (e.g., a mobile computing device, a desktop computer or workstation, a computing cluster, a cloud computing platform, or the like). The system may be a closed-loop system or an open-loop system.
It is also possible that the system allows both closed-loop and open loop functionality. In this regard, the user may switch between these options or there may be routines or control elements that can do or propose such a switch from closed-loop to open-loop and vice versa.
A method is disclosed, the method may be performed with the systems consistent with the disclosed embodiments. In some embodiments, the method may be a method for providing neuromodulation, the method comprising at least the steps of inputting patient data; 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; or calculating the impact of the provided (or simulated) neuromodulation.
In some embodiments the method may further comprise the step of outputting at least one of pre-operative planning data, intra-operative planning data and/or post-operative planning data.
In some embodiments, the method may include 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 are provided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosed embodiments or the scope of the inventions as claimed. The concepts in this application may be employed in other embodiments without departing from the scope of the inventions.

BRIEF DESCRIPTION OF THE FIGURES

Reference will now be made in detail to exemplary embodiments, discussed with regards to the accompanying drawings. In some instances, the same reference numbers will be used throughout the drawings and the following description to refer to the same or like parts. Unless otherwise defined, technical or scientific terms have the meaning commonly understood by one of ordinary skill in the art. The disclosed embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosed embodiments. It is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the disclosed embodiments. Thus, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

FIG. 1 shows a schematic overview of an embodiment of the neuromodulation system according to the disclosed embodiments, with which the method according to the disclosed embodiments may be performed;

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

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 disclosed embodiments as disclosed in FIG. 1;

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

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

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

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments, discussed with regards to the accompanying drawings. In some instances, the same reference numbers will be used throughout the drawings and the following description to refer to the same or like parts. Unless otherwise defined, technical or scientific terms have the meaning commonly understood by one of ordinary skill in the art. The disclosed embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosed embodiments. It is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the disclosed embodiments. Thus, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.
FIG. 1 shows a schematic overview of an embodiment of the neuromodulation system 10 according to the disclosed embodiments, with which the method according to the disclosed embodiments 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 104.
In some aspects, as shown in FIG. 1, the device 102 may be communicatively 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.
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.
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.
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.
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.
As shown in FIG. 1, the system 10 comprises an input module 12. The input module 12 can be 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. In some embodiments, the system 10 may comprise more than one input module 12. The system 10 may further comprise a model calculation and building module 14. The model calculation and building module 14 can be 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. In some embodiments, the system 10 may comprise more than one model calculation and building module 14.
The system 10 may further comprise a computation module 16. The computation module 16 can be 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.
In some embodiments, the system 10 may comprise more than one computation module 16. In various embodiments, the input module 12 may be connected to the model calculation and building module 14. The connection between the input module 12 and the model calculation and building module 14 may be a direct and bidirectional connection. However, in various embodiments, an indirect and/or unidirectional connection may be implemented. In some embodiments, the connection between the input module 12 and the model calculation and building module 14 is a wireless connection. However, in various embodiments, a cable-bound connection may be implemented. In various embodiments, the input module 12 may be connected to computation module 16.
The connection between the input module 12 and the computation module 16 may be a direct and bidirectional connection. However, in various embodiments, an indirect and/or unidirectional connection may be implemented. In some embodiments, the connection between the input module 12 and the computation module 16 may be a wireless connection. However, in various embodiments, a cable-bound connection may be implemented. In some embodiments, the model calculation and building module 14 may be connected to computation module 16.
The connection between the model calculation and building module 14 and the computation module 16 may be a direct and bidirectional connection. However, in various embodiments, an indirect and/or unidirectional connection may be implemented. In some embodiments, the connection between the model calculation and building module 14 and the computation module 16 may be a wireless connection. However, in various embodiments, a cable-bound connection may be implemented. In some embodiments, the input module 12 inputs patient data D on the anatomy and/or physiology and/or pathophysiology of a patient into the system 10.
Accordingly, the input module 12 may read patient data D. In some embodiments, patient data D may be 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.
In some embodiments, patient data D may indicate that the patient may be a patient suffering from SCI. In some embodiments, the patient may be a patient suffering from motor dysfunction. In various embodiments, the patient may be a patient suffering from impaired motor dysfunction and/or impaired autonomic function.
In some embodiments, the model calculation and building module 14 builds a patient model M (e.g., based on the patient data D provided by the input module 12). In some instances, the patient model M can describes the anatomy of the patient and the real reaction of the patient on provided neuromodulation. In various instances, the patient model M can describe the physiology and/or pathophysiology and the simulated reaction of the patient to provided (or simulated) neuromodulation. In some embodiments, the computation module 16 uses the model M and calculates the impact of the provided neuromodulation.
In some embodiments, 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. In some embodiments (not shown in FIG. 1), 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.
In some embodiments (not shown in FIG. 1), the pre-operative planning data may include at least one of surgical incision placement, optimal electrode E placement, eligibility of the patient, assessment of in-silico benefit for decision making (see e.g. FIG. 4). In some embodiments, 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 module, real-time registration, navigation system imaging, EEG, ECG, EMG, mechanical feedback and the like (see e.g. FIGS. 2 and 3). The post-operative planning data may include at least one recommend optimum electrode E configuration, electrode E design, plan, stimulation waveforms, timings schedule for neuromodulation events and the like.
In some embodiments (not shown in FIG. 1), the output device may 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, see FIG. 5.
In some embodiments, the system 10 may be a system for restoring motor and/or autonomic function in a patient. The system may determine optimal stimulation parameters (such as frequency, amplitude, and/or pulse width) for the specific recruitment of Aa nerve fibers of at least one dorsal root.
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 will be described in more detail in reference to FIG. 6 below. 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.
The method may further comprise the step of outputting at least one of pre-operative planning data, intra-operative planning data and/or post-operative planning data. The method may 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.
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 disclosed embodiments as described in reference to 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. The model calculation and building module 14 of the system 10 disclosed in FIG. 1 may build the patient model 250 describing the anatomy of a patient.
In some embodiments, the system 10 further comprises an output device for outputting intra-operative planning data. The output device may be communicatively connected to the input module 12, the model calculation and building module 14 and the computation module 16 of the system 10. The connection may be a wireless connection or a wired (e.g., a cable-bound) connection. The connection can be bidirectional or unidirectional connection. In various embodiments, the output device may be connected to at least one of the input module 12, the model calculation and building module 14, or the computation module 16 of the system 10.
In some embodiments, the model calculation and building module 14 builds a patient model 250 based on patient data D. In some instances, the patient model 250 may be a 3D reconstruction of the patient data D. The patient data D may be intra-operative planning data. The patient data D may be imaging data obtained by a 3T MRI scanner and/or an MRI scanner. In some embodiments, the patient model 250 may be a 3D reconstruction of the MRI scan.
In some embodiments, the output device can provides visual information via a display. The output device can provide the patient model 250 built by the model calculation and building module 14. In various embodiments, the patient model 250 may be or may comprise a 2D reconstruction of the patient data D.
In some embodiments 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. In some embodiments, the patient model 250 is combined with a model of a lead L comprising multiple electrodes for providing neuromodulation.
In some embodiments, the computation module 16 may calculate the impact of the neuromodulation provided by the lead L. The computation module 16 can perform this calculation using the patient model 250. In some embodiments, via a user interface of the output device, a user may edit the patient model 250, e.g. by zooming in and/or zooming out and/or rotating and/or adding and/or changing colors.
FIG. 3 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 disclosed embodiments 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.
In some embodiments, 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. In some embodiments, the output device can be communicatively 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, see FIG. 1.
In some embodiments, the intra-operative planning data may be an MRI image. In some embodiments, the output device can 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). In some embodiments, the output device may provide the patient model 306 built by the model calculation and building module 14. Another example patient model M is shown at FIG. 2.
In some embodiments (not shown in FIG. 3), the system 10 may provide 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.
In some embodiments, a computational pipeline may 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. In some embodiments, the system, via model 306, may describe a patient's anatomy in terms of every tissue in the spinal cord S area. In some embodiments, the system, via model 306, may describe a patient's anatomy in terms of a volume of every tissue in the spinal cord S area. In some embodiments, the system, via model 306, may describe the patient's anatomy in terms of every tissue in the spinal cord S area, including crucial trajectories of the spinal roots R, which may 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.
FIG. 4 shows an example of optimization of electrode E position and stimulation configuration with the system 10 disclosed in FIG. 1. In some embodiments a lead L comprising multiple electrodes E may be superimposed on a patient model M. Epidural electrical stimulation (EES) can be utilized for enabling motor functions by recruiting large-diameter afferent nerve fibers within the dorsal roots P. Electrode E positioning and stimulation configuration may have an effect on the selectivity of this recruitment pattern and is dependent on the anatomy of each subject.
In some embodiments, the system 10 disclosed in FIG. 1 further comprises an output device for outputting pre-operative planning data, see FIGS. 2 and 3. The output device may provide visual information via a display, and visual information may be provided by the output device. The output device may comprise a user interface, enabling the user to change pre-operative planning data. In some embodiments, the pre-operative planning data can include optimal electrode E placement. In other words, the system 10 can support improved placement of a lead L comprising multiple electrodes E.
The system 10 may be used for optimization of electrode E position and stimulation configuration for enabling motor function. Left hip flexors and right ankle extensors may be stimulated with a lead L comprising multiple electrodes E, and L1 and S2 dorsal roots may be stimulated by electrodes E of the lead L. Alternatively, and/or additionally, the system 10 may optimize electrode E position and stimulation configuration for treatment of autonomic dysfunction.
In some embodiments, the cost function may be implemented using a distance function. In some embodiments, the distance function can be:
dist(j)=sqrt[(sum_i(w_i*(x_desired_i(j)−x_achieved_i(j))))**2]
where x is the percentage of a specific type of nerve fiber being activated within one dorsal root P, i being a combination of dorsal roots P and neve fiber types that have been initialized, w being a weight, 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.
FIG. 5 shows an example of neurofunctionalization with the system 10 disclosed in FIG. 1, and more specifically shows the neurofunctionalization of the patient 3D Finite Element Method (FEM) model M. The tissues shown are the spinal cord S (white matter W+Grey matter G) and the roots R. 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 this specific figure, an example of myelinated fiber AX (e.g. Aa-sensory fiber) with nodes of Ranvier N is shown. Components of a compartmental cable model are illustrated by showing the lumped elements used to model the ion-exchange at the nodes of Ranvier.
In some embodiments, the system 10 disclosed in FIG. 1 further comprises output device, see FIG. 2. The output device may provide visual information on a display, and visual information may be provided by the output device. The output device may 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. The output device may provide 3D visualization, and more specifically the output device provides neurofunctionalization of a patient 3D FEM model M. Spinal cord S, grey matter G, white matter W and dorsal roots R comprising myelinated axons AX (nerve fibers) are shown.
In some embodiments, simulations can be performed using an electro-quasi-static solver. Simulations of excitation after provided neuromodulation are performed, and the simulations are coupled with electrophysiology models. The simulations may be coupled with a nerve fiber-based electrophysiology model. FIG. 5 illustrates a myelinated axon AX is shown in detail. A myelinated fiber AX (e.g. Aa-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.
In some embodiments, the output device provides visualization of information on the location of the depolarization of a nerve fiber (e.g., an axon AX) after providing neuromodulation to the spinal cord S. Also illustrated are some components of a compartmental cable model by showing the lumped elements used to model the ion-exchange at the nodes of Ranvier N.
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 various embodiments, the output device may provide visualization of information on the location and/or probability of the depolarization of nerve fibers and/or neurons. The system 10 may automatically determine the optimal stimulation parameters for recruiting a nerve fiber and/or neuron population with the spinal cord of a patient.
Turning to FIG. 6, it shows a 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 to 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.
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.
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 at least one of the real reaction or 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 at least one of the real reaction or the 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.
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.
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.
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)
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.
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.

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 describing anatomy and/or physiology and/or pathophysiology and real and/or simulated reaction of a patient on a provided and/or simulated neuromodulation;

at least one computation module for using the patient model and calculating an impact of the provided and/or simulated neuromodulation.

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 and/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 (MRI), 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 recommend optimum electrode configuration, electrode design, plan, stimulation waveforms, and 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 and/or probability of depolarization of nerve fibers and/or neurons.

7. A method for providing neuromodulation, comprising at least the steps of:

inputting patient data of a patient;

building a patient model, the patient model describing anatomy and/or physiology and/or pathophysiology and/or at least one of a real or simulated reaction of a patient to a provided and/or simulated neuromodulation;

calculating an impact of the provided and/or simulated neuromodulation.

8. The method according to claim 7, further comprising a step of outputting at least one of pre-operative planning data, intra-operative planning data and/or post-operative planning data.

9. The method according to claim 8, wherein the pre-operative planning data includes at least one of surgical incision placement, optimal electrode placement, eligibility of the patient, and assessment in-silico benefit for decision making.

10. The method according to claim 8, 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.

11. The method according to claim 8 wherein the post-operative planning data includes at least one recommend optimum electrode configuration, electrode design, plan, stimulation waveforms, and timings schedule for neuromodulation events.

12. The method according to claim 8, wherein visualization of at least one of electric currents, potentials, information on location and/or probability of depolarization of nerve fibers and/or neurons are provided.

13. The method according to claim 7, further comprising determining a desired placement of a lead comprising a plurality of electrodes according to the patient model.

14. The method according to claim 7, wherein the patient model is a three dimensional reconstruction of one or more of a spinal cord, a vertebral column, an epidural fat, a pia mater, a dura mater, a dorsal root, a ventral root, a cerebro-spinal fluid, a white matter and a grey matter of the patient using the patient model.

15. The method according to claim 7, wherein the patient model is combined with a model of a lead including a plurality of electrodes.

16. The method according to claim 7, further comprising determining, according to the patient model, an optimal electrode configuration and/or one or more optimal stimulation parameters for a nerve fiber and/or neuron population within a spinal cord of the patient.

17. The method according to claim 16, wherein the one or more optimal stimulation parameters include a frequency, amplitude, a pulse width and/or polarity applied to a plurality of electrodes of a lead.

18. The method according to claim 16, wherein the optimal electrode configuration and/or the one or more optimal stimulation parameters are determined according to a distance function that is a function of at least a percentage of a specific type of nerve fiber being activated within a dorsal root, a combination of dorsal roots and neve fiber types that have been initialized, and a current used dorsal roots and nerve fibers.

19. 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.

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