U.S. patent application number 17/106030 was filed with the patent office on 2021-05-27 for neuromodulation system.
The applicant listed for this patent is GTX MEDICAL B.V.. Invention is credited to Jurriaan BAKKER, Edoardo PAOLES, Mathieu SCHELTIENNE, Jeroen TOL.
Application Number | 20210153942 17/106030 |
Document ID | / |
Family ID | 1000005279161 |
Filed Date | 2021-05-27 |
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United States Patent
Application |
20210153942 |
Kind Code |
A1 |
SCHELTIENNE; Mathieu ; et
al. |
May 27, 2021 |
NEUROMODULATION SYSTEM
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.
Inventors: |
SCHELTIENNE; Mathieu;
(Eindhoven, NL) ; PAOLES; Edoardo; (Eindhoven,
NL) ; TOL; Jeroen; (Eindhoven, NL) ; BAKKER;
Jurriaan; (Eindhoven, NL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GTX MEDICAL B.V. |
Eindhoven |
|
NL |
|
|
Family ID: |
1000005279161 |
Appl. No.: |
17/106030 |
Filed: |
November 27, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 30/20 20180101;
A61B 2090/3762 20160201; A61B 34/10 20160201; A61B 2090/373
20160201; G16H 20/40 20180101; A61B 2017/00039 20130101; A61B
2017/00044 20130101; A61N 1/36103 20130101; A61B 2034/104 20160201;
A61B 2090/374 20160201; A61N 1/0551 20130101; A61B 2034/105
20160201 |
International
Class: |
A61B 34/10 20060101
A61B034/10; A61N 1/05 20060101 A61N001/05; G16H 20/40 20060101
G16H020/40; G16H 30/20 20060101 G16H030/20 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 27, 2019 |
EP |
19211698.6 |
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.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] 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
[0002] 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
[0003] 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.
[0004] 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.
[0005] 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.
[0006] 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.
[0007] 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.
[0008] 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.
[0009] 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.
[0010] 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.
[0011] 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.
[0012] 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.
[0013] 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.
[0014] 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).
[0015] 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.
[0016] 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.
[0017] 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,
A.alpha.-, A.beta.-, A.delta.-, C-sensory fibers, interneurons,
.alpha.-motoneurons and efferent nerves, as well as dorsal column
projections.
[0018] 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.
[0019] 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:
[0020] Recalculating the selectivity index for a multitude of
different lead positions;
[0021] Find the minimal distance among all lead positions;
[0022] Take the dist(j) function for that position for all possible
active sites;
[0023] Minimize it through superposition of the active sites to
calculate the multipolar configuration.
[0024] 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).
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] 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.
[0030] 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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.
[0038] 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).
[0039] 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.
[0040] 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.
[0041] 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.
[0042] 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.
[0043] 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.
[0044] 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.
[0045] 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
[0046] 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.
[0047] 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;
[0048] 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;
[0049] 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;
[0050] FIG. 4 shows an example of optimization of electrode
position and stimulation configuration with the system disclosed in
FIG. 1;
[0051] FIG. 5 shows an example of neurofunctionalization with the
system disclosed in FIG. 1; and
[0052] FIG. 6 shows a high level flow chart illustrating an example
method for patient-specific neuromodulation.
DETAILED DESCRIPTION
[0053] 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.
[0054] 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.
[0055] 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.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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.RTM., IEEE 802.11, and the like.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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:
[0073] inputting patient data D;
[0074] 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;
[0075] calculating the impact of the provided and/or simulated
neuromodulation.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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.
[0089] 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.
[0090] 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.
[0091] 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;
[0092] Reiterate the selectivity index for a multitude of different
lead L positions;
[0093] Find the minimal distance among all lead L positions;
[0094] Take the dist(j) function for that position for all possible
active sites;
[0095] Minimize it through superposition of the active sites to
calculate the multipolar configuration.
[0096] 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, A.alpha.-, A.beta.-,
A.delta.-, C-sensory fibers, interneurons, .alpha.-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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] In general, realistic compartmental cable models can
automatically be created within the personalized 3D FEM models,
including but not limited to, A.alpha.-, A.beta.-, A.delta.-,
C-sensory fibers, interneurons, .alpha.-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.
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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.
[0105] 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.
[0106] 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)
[0107] 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.
[0108] 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.
* * * * *