Correlated probabilistic shell-to-brain deep stimulation targeted binding

Abstract

Methods and systems for analyzing and selecting therapy configurations for use in stimulating nervous tissue. A probabilistic shell related to the likelihood that electrical stimulation emanating to a given volume of nervous tissue will cause a therapeutic outcome is incorporated in a system for analyzing anatomical and other data, including lead location relative to the neuroanatomy. Metrics for analyzing therapy configurations may then be calculated and the process of identifying therapy configurations that may be beneficial may be enhanced.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 533,038, filed August 16, 2023, entitled “INTEGRATION OF RELATEDPROBABILITY SHELLS INTO DEEP BRAIN STIMULATION TARGETING”, the disclosure of which is incorporated herein by reference. Background Technology

[0003] Deep brain stimulation (DBS) is a form of neuromodulation in which electrodes are implanted into a patient's brain to deliver stimulation. Therapies have been proposed and / or implemented for a wide range of conditions, including Alzheimer's disease, Parkinson's disease, cognitive and / or memory decline, depression, and other ailments. Each patient has a unique anatomy, and each disease may require different parts of the brain to receive the therapy. Therefore, accurate and precise therapeutic targeting is essential. In many cases, the exact location of the targeted brain structure cannot be directly determined but can be inferred through a combination of imaging studies and population-based brain atlas data. Specific structures can be characterized as having a probability or likelihood of being located in a specific location. Similarly, tissue of a specific volume can be characterized as having a probability of responding to a therapy or stimulus in a specific manner. New and alternative methods and systems relying on probabilistic information are permitted when configuring and / or testing DBS. Summary of the Invention

[0004] The inventors have recognized, among other things, the need for new and / or alternative methods and systems that allow reliance on probabilistic information when configuring and / or testing a DBS. In some examples, a probabilistic shell is used to perform partitioning of brain structures around the implanted lead.

[0005] A first illustrative and non-limiting example takes the form of a configuration system for configuring the delivery of neural modulation to specific tissues of a patient, the system comprising: a receiver module (400) configured to receive at least brain anatomy data of the patient and lead location data of leads forming part of a neural modulation system, the lead location data indicating the location of the leads in the patient's brain; a structure selection module (404) coupled to a user interface (406) providing graphical output to allow the user to identify and select brain structures in the patient's brain as target structures and avoidance structures; and a voxel definition module (402) configured to define portions of the patient's brain as voxel data structures in voxel form; wherein: the receiver module receives, in the brain anatomy data, a plurality of nested probability shells for neural structures, the nested probability shells indicating the probability of a therapeutic outcome resulting from stimulation of a volume defined by the nested probability shells; and the voxel definition module is configured to use the probability shells among the plurality of nested probability shells to determine the voxel value of each voxel in the voxel data structure.

[0006] Additionally or alternatively, each probability shell has an outer boundary and defines a probability increment relative to the volume outside the probability shell, and the voxel definition module is configured to determine a voxel value for each voxel in the voxel data structure using the probability shells of the nested probability shells for the neural structure: a) selecting a first probability shell; b) for the selected probability shell, calculating a fill amount for each voxel therein representing at least a portion of the voxel; c) for each voxel in the selected probability shell, multiplying the fill amount by the probability increment of the selected probability shell to produce a partial voxel value; repeating a), b), and c) for each probability shell of the nested probability shells for the neural structure.

[0007] Additionally or alternatively, the system further includes an optimization block configured to determine optimal steering and amplitude settings for use by the neural modulation system using data passed from the voxel definition module; wherein the voxel definition module is configured to pass a plurality of target shell and avoidance shell structures to the optimization block, including one or more target shell or avoidance shell structures generated by performing steps a), b) and c) on selected probability shells of the nested probability shells of the neural structure.

[0008] Additional or alternative locations, each target shell or avoidance shell structure includes multiple partial voxel scores.

[0009] Additionally or alternatively, the voxel definition block is configured to sum the partial voxel values ​​of each voxel to produce a summed voxel value for each voxel relative to the neural structure. The system also includes an optimization block configured to use data passed from the voxel definition module to determine optimal steering and amplitude settings for use by the neural modulation system; and the voxel definition module is configured to pass the summed voxel values ​​for the neural structure.

[0010] Additionally or alternatively, each probability shell has an outer boundary and defines a probability applicable to volumes located outside any other nested probability shells within it, and the voxel definition module is configured to determine the voxel value of each voxel in the voxel data structure for each corresponding probability shell in which the voxel is at least partially located by using the probability shells of the plurality of nested probability shells in the following manner: a) calculating partial padding, the partial padding representing the percentage of the voxel in the corresponding probability shell; b) calculating partial voxel values ​​by multiplying the partial padding by the probability for the corresponding probability shell; after completing a) and b) for each corresponding probability shell, summing all partial voxel values ​​for each voxel such that each voxel has a single summed voxel value relative to the nested probability shells of the neural structure.

[0011] Additionally or alternatively, the system further includes an optimization block configured to use data passed from the voxel definition module to determine optimal steering and amplitude settings for use by the neural modulation system; wherein the voxel definition module is configured to pass the summed voxel values ​​to the optimization block.

[0012] Additionally or alternatively, each probability shell has an outer boundary and defines an increase in probability relative to the organization outside the probability shell, and the voxel definition module determines the voxel value of each voxel in the voxel data structure using the probability shells of the plurality of nested probability shells in such a way as: selecting a first probability shell; calculating, for the selected probability shell, a fill amount of at least a portion of each voxel therein, the fill amount representing the percentage of the voxel within the selected probability shell; and calculating a shell weight for the selected probability shell by multiplying the increased probability of the selected probability shell by a target or avoidance structure weight received from the user via the structure selection block.

[0013] Additionally or alternatively, the system includes an optimization block configured to use data passed from the voxel definition module to determine optimal steering and amplitude settings for use by the neural modulation system; wherein the voxel definition module is configured to pass a set of voxel fill values ​​and shell weights for each nested probability shell.

[0014] Alternatively or additionally, the target structure corresponds to a beneficial treatment outcome, and the avoidance structure corresponds to an unfavorable treatment outcome.

[0015] Additionally or alternatively, the optimization block includes a metric calculator configured to determine each of the following: a target value, calculated by determining the active volume of the target structure for the selected steering configuration and amplitude; an avoidance structure penalty, calculated by determining the active volume of the avoidance structure for the selected steering configuration and amplitude; a background penalty, calculated using the total active volume for the selected steering configuration and amplitude; and a metric of the target value minus the avoidance structure penalty and the background penalty.

[0016] Alternatively, the optimization block may use user-defined avoidance structure weights to calculate the avoidance structure penalty and use user-defined background ratio weights to calculate the background penalty.

[0017] Additionally or alternatively, the system includes a therapy selection module and a communication module, the therapy selection module being adapted to: present at least one suggested therapy configuration to a user for selection; and, in response to the user selecting a suggested therapy configuration for use, instruct the communication module to send a command to a pulse generator of the neural modulation system to implement the selected suggested therapy configuration. Additionally or alternatively, the therapy selection module is configured to present the user with a graphical representation of a stimulation field model of the suggested therapy configuration.

[0018] Another example takes the form of a neural modulation system, which includes: a pulse generator; leads configured to be coupled to the pulse generator and adapted to be positioned in the patient's brain; and a configuration system as described above, wherein the pulse generator is adapted to receive the instructions from the therapy selection module and configure the selected recommended therapy to be applied to the patient via the leads.

[0019] Another illustrative and non-limiting example takes the form of a method for configuring a neural modulation system to target delivery to specific tissues of a patient, the method comprising: receiving at least brain anatomy data of a patient and lead location data of leads forming part of the neural modulation system, the lead location data indicating the location of the leads in the patient's brain; identifying and selecting brain structures in the patient's brain as target structures and avoidance structures; defining portions of the patient's brain in voxel form as voxel data structures; calculating at least one candidate therapy using the voxel data structures, the at least one candidate therapy including optimized therapy parameters for use by the neural modulation system, the optimized therapy parameters including at least amplitude for stimulation and electrode usage data for stimulation; and selecting at least one candidate therapy and delivering it to the neural modulation system for use by the patient; wherein: the brain anatomy data includes a plurality of nested probability shells for neural structures, the nested probability shells indicating the probability of a therapeutic outcome caused by stimulation of a volume defined by the nested probability shells; and the step of defining portions of the patient's brain in voxel form includes using the probability shells of the plurality of nested probability shells to determine a voxel value for each voxel in the voxel data structure.

[0020] Additionally or alternatively, each probability shell has an outer boundary and defines a probability increment relative to the volume outside the probability shell, and the step of defining the patient's brain portion in voxel form includes determining a voxel value for each voxel in the voxel data structure by using the probability shells of the nested probability shells for the neural structure: a) selecting a first probability shell; b) for the selected probability shell, calculating a fill amount for each voxel therein, the fill amount representing a percentage of the voxel within the selected probability shell; c) for each voxel in the selected probability shell, multiplying the fill amount by the probability increment of the selected probability shell to produce a partial voxel value; repeating a), b), and c) for each probability shell of the nested probability shells for the neural structure.

[0021] Additionally or alternatively, the step of computing at least one candidate therapy using the voxel data structure includes determining an optimal steering and amplitude setting for use by the neural modulation system using multiple target shell and avoidance shell structures, the system including one or more target shell or avoidance shell structures generated by performing steps a), b) and c) on selected probability shells of the nested probability shells of the neural structure.

[0022] Additional or alternative locations, each target shell or avoidance shell structure includes multiple partial voxel scores.

[0023] Additionally or alternatively, the step of defining the patient’s brain portion in voxel form includes summing the partial voxel values ​​of each voxel to produce a summed voxel value for each voxel relative to the neural structure; and the step of calculating at least one candidate therapy using the voxel data structure includes using the summed voxel values.

[0024] Additionally or alternatively, each probability shell has an outer boundary and defines a probability applicable to a volume located outside any other nested probability shell within the probability shell; and the step of defining the patient's brain portion in voxel form includes, for each corresponding probability shell in which the voxel is at least partially located, determining the voxel value of each voxel in the voxel data structure using the probability shells of the plurality of nested probability shells in such a way as: a) calculating partial padding, the partial padding representing a percentage of the voxel in the corresponding probability shell; b) calculating a partial voxel value by multiplying the partial padding by the probability for the corresponding probability shell; after completing a) and b) for each corresponding probability shell, summing all partial voxel values ​​for each voxel such that each voxel has a single summed voxel value relative to the nested probability shells of the neural structure.

[0025] Additionally or alternatively, the step of calculating at least one candidate therapy using the voxel data structure includes using the summed voxel values ​​to determine the optimal steering and amplitude settings for use by the neural modulation system.

[0026] Additionally or alternatively, each probability shell has an outer boundary and defines an increase in probability relative to tissue outside the probability shell; and the step of defining the patient's brain portion in voxel form includes determining the voxel value of each voxel in the voxel data structure by using the probability shells among the plurality of nested probability shells in such a way as: selecting a first probability shell; for the selected probability shell, calculating a fill amount for each voxel therein, the fill amount representing a percentage of the voxel within the selected probability shell; and for the selected probability shell, calculating a shell weight by multiplying the increased probability of the selected probability shell by a target or avoidance structure weight received from the user via the structure selection block.

[0027] Additionally or alternatively, computing at least one candidate therapy using the voxel data structure includes determining the optimal steering and amplitude settings for use by the neural modulation system using the voxel fill values ​​and the shell weights of each of the nested probability shells.

[0028] Alternatively or additionally, the target structure corresponds to a beneficial treatment outcome, and the avoidance structure corresponds to an unfavorable treatment outcome.

[0029] Additionally or alternatively, calculating at least one candidate therapy using the voxel data structure includes: calculating a target value by determining the activation volume of the target structure for a selected steering configuration and amplitude; calculating an avoidance structure penalty by determining the activation volume of the avoidance structure for a selected steering configuration and amplitude; calculating a background penalty using the total activation volume of the selected steering configuration and amplitude; and calculating a metric as the target value minus the avoidance structure penalty and the background penalty.

[0030] Alternatively or additionally, the calculation of the avoidance structure penalty may include applying user-defined avoidance structure weights, and the calculation of the background penalty may include applying user-defined background ratio weights.

[0031] Additionally or alternatively, the method may also include delivering a selected candidate therapy to the patient via the neural modulation system.

[0032] Additionally or alternatively, the method further includes presenting a simulation of a stimulation field model for the candidate therapy on a graphical user interface.

[0033] This summary is intended to provide an overview of the subject matter of this patent application. It is not intended to provide a unique or exhaustive explanation. The detailed description is included to provide further information about this patent application. Attached Figure Description

[0034] In accompanying drawings that are not necessarily drawn to scale, the same numbers may describe similar parts in different views. The same numbers with different letter suffixes may represent different instances of similar parts. The accompanying drawings illustrate various embodiments discussed in this document by way of example and not limitation.

[0035] Figure 1 An illustrative DBS system implanted in a patient is shown;

[0036] Figure 2 Detailed information about the oriented DBS leads is shown;

[0037] Figure 3 The illustrative method is shown in boxes;

[0038] Figure 4 An illustrative set of relevant probability shells is shown;

[0039] Figure 5 It shows Figure 4 The probability shell relative to the partitioned grid;

[0040] Figure 6 The illustrative method is shown in boxes; and

[0041] Figure 7 An illustrative system is shown. Detailed Implementation

[0042] Figure 1 An illustrative DBS system implanted in a patient is shown. The system includes a pulse generator 10, which is implanted in the chest region of patient 20 as shown. The pulse generator 10 is coupled to a lead 12, which extends subcutaneously into the head of patient 20 through a burr hole formed in the patient's skull, and then into the patient's brain. In the example shown, the lead 12 includes multiple electrodes located near the distal end 14 of the lead, as follows... Figure 2 As shown. Lead 12 can be placed at any suitable location in the brain that identifies the target of the therapy. For example, lead 12 can be positioned such that distal end 14 is close to the midbrain and / or various structures therein known in the art for providing stimulation to treat various diseases.

[0043] DBS can be targeted at, but is not limited to, neuronal tissue in the thalamus, globus pallidus, subthalamic nucleus, pontine nucleus, substantia nigra reticularis, cortex, lateral globus pallidus externus, medial anterior tract, periventricular gray matter of the four ducts, periventricular gray matter, habenular nucleus, subnuclear cingulate cortex, ventral intermediate nucleus, anterior nucleus, other thalamic nuclei, ascending zonuclear zone, ventral sac, ventral striatum, nucleus accumbens, and / or white matter tracts connecting these and other structures. Data related to DBS may include the identification of neural tissue regions identified through analysis that are associated with side effects or benefits observed in practice. As used herein, “target” refers to brain structures associated with therapeutic benefits, while “avoidance” or “avoidance” regions refer to brain structures associated with side effects.

[0044] Conditions to be treated may include dementia, Alzheimer's disease, Parkinson's disease, various tremors, depression, anxiety or other mood disorders, sleep-related disorders, etc. Treatment benefits may include, for example, but not limited to, improved cognition, alertness, and / or memory; enhanced mood or sleep; avoidance of pain or tremors; reduction of motor injury; and / or protection of existing functions and / or cellular structures, such as preventing tissue loss and / or cell death. Treatment effectiveness may be monitored using, for example, patient surveys, performance tests, and / or physical monitoring (such as monitoring gait, tremors, etc.). Side effects may include a range of problems, for example, but not limited to, decreased cognition, alertness, and / or memory; decreased sleep quality; depression; anxiety; unexplained weight gain / loss; tinnitus; pain; tremors, etc. These are merely examples, and the discussion of diseases, benefits, and side effects is illustrative and not exhaustive.

[0045] The illustrative system of claim 1 includes various external devices. The clinician programmer (CP) 30 can be used to determine / select a therapeutic procedure, including direction (explained further below) and stimulation parameters. Stimulation parameters may include the amplitude of the stimulation pulse, the frequency or repetition rate of the stimulation pulse, the pulse width of the stimulation pulse, and more complex parameters known in the art, such as burst definition. A biphasic square wave is typically used, but nothing in this invention is limited to a biphasic square wave, and ramp, triangle, sine, monophasic, and other stimulation types may be used as needed. The CP 30 may be, for example, a laptop or tablet computer, and may be used by or under the guidance of a physician to obtain data from and provide instructions to the pulse generator 10 via a suitable communication protocol (such as Bluetooth or MedRadio or other wireless communication protocols) and / or via other modes (such as inductive telemetry).

[0046] The patient can use a patient remote control (RC) 40 to perform various actions related to the pulse generator 10. These can be physician-defined options, such as turning the therapy on and / or off, typing in required information (such as answering questions about the activity, therapy benefits, and side effects), and making (limited) adjustments to the therapy, such as selecting and adjusting, for example, amplitude settings from available therapy programs. The RC 40 can communicate via telemetry technology similar to that of the CP 30 to control the pulse generator 10 and / or obtain data from it. The patient RC 40 can also be programmed by the patient or can communicate with or link to the CP 30.

[0047] If the pulse generator 10 is rechargeable, a charger 50 can be provided to the patient to allow the patient to recharge the pulse generator 10. Some pulse generators 10 are not rechargeable, so the charger 50 can be omitted. The charger 50 can, for example, recharge the pulse generator by generating a changing magnetic field to activate an inductor associated with the pulse generator 10, using known methods to provide power.

[0048] Some systems may include an external test stimulator (ETS) 60. After the lead 12 has been positioned within the patient, the ETS 60 can be used during surgery to test therapeutic procedures to determine if the therapy is suitable for the patient 20 or if the therapy will work for the patient 20. For example, the initial implantation of the lead 12 can be performed using, for instance, a stereotactic guidance system, where the pulse generator 10 is temporarily omitted. The lead 12 may have a proximal end that connects to an intermediate connector (sometimes called an operating room cable) coupled to the ETS 60. After the lead 12 has been implanted and coupled to the ETS 60, the ETS can be programmed using a CP 30 with various therapeutic procedures and stimulation parameters that have been tested to determine therapeutic efficacy. During this process, the lead position can be adjusted as needed. Once therapeutic suitability for the patient has been established, a permanent pulse generator 10 is implanted and the lead 12 is connected to it, and then the ETS is discontinued.

[0049] The pulse generator 10 may include operational circuitry for generating output stimulation programs and / or pulses according to stored instructions. Examples of prior versions of such circuitry, as well as planned future examples, can be found in U.S. Patent 10,716,932, the disclosure of which is incorporated herein by reference. The pulse generator circuitry may include circuitry for various commercially known implantable pulse generators for spinal cord stimulation, vagus nerve stimulation, and deep brain stimulation, which are also well-known. Additional examples of the pulse generator 10, CP 30, RC 40, charger 50, and ETS 60 can be found, for example, but not limited to, U.S. Patent Nos. 6,895,280, 6,181,969, 6,516,227, 6,609,029, 6,609,032, 6,741,892, 7,949,395, 7,244,150, 7,672,734, 7,761,165, 7,974,706, 8,175,710, 8,224,450, and 8,364,278, the entire disclosure of which is incorporated herein by reference.

[0050] Figure 2 Detailed information about the oriented DBS leads is shown. The distal end 14 is shown, along with multiple electrodes. As illustrated, two ring electrodes 16a and 16b (collectively referred to as ring electrodes 16) can be provided, and multiple segmented electrodes (collectively referred to as segmented electrodes 18) are shown at 18a, 18b, 18c, 18d, 18e, and 18f. Each electrode 16, 18 is individually addressable in the system, such as by using a pulse generator with multiple independent current control (MICC) or multiple voltage sources.

[0051] A MICC is a stimulation control system that provides multiple independently generated output currents, each with an independent current magnitude. The use of a MICC allows for the generation of a spatially selective field during therapeutic output. The term "fractionalization" can refer to how the total current emitted by the pulse generator via the electrodes is distributed among the electrodes 16, 18 on the leads. It should be noted that the pulse generator canister can be used as a neutral electrode or a return electrode for therapeutic output; conversely, if desired, one of the electrodes (such as one or more of the ring electrode 16 or segmented electrodes 18) can be used as a return electrode. Thus, for example, during one phase of stimulation pulse delivery, the electrodes on the leads can be used as cathodes, while the pulse generator canister serves as an anode. In another example, during one phase of stimulation pulse delivery, some of the lead electrodes 16, 18 serve as cathodes, while others serve as anodes. Any suitable combination and number of anodes and cathodes can be used for therapeutic purposes, and any lead electrode and / or shell electrode can be used for any of these functions as needed.

[0052] Examples of electrical leads having segmented or oriented lead structures include, but are not limited to, those disclosed in US PG Patent Publications 20100268298, 20110005069, 20110078900, 20110130803, 20110130816, 20110130817, 20110130818, 20110238129, 20110313500, 20120016378, 20120046710, 20120071949, 20120165911, 20120197375, 20120203316, 20120203320, 20120203321, 20130197602, 2 The disclosures shown in U.S. Patent Nos. 0130261684, 20130325091, 20130317587, 20140039587, 20140353001, 20140358207, 20140358209, 20140358210, 20150018915, 20150021817, 20150045864, 20150021817, 20150066120, 20130197424, and 20150151113, as well as U.S. Patent Nos. 8,483,237 and 8,321,025, are incorporated herein by reference.

[0053] MICCs used in conjunction with directional leads can facilitate precise therapeutic targeting. For example, such as... Figure 2 The directional leads shown can be used to generate, for example... Figure 2The stimulation field is shown in 80. For illustrative purposes, the outer boundary of field 80 can be understood as representing an isoelectric or isofield boundary within which the electric field is above an activation threshold, and outside which the electric field is below a threshold. The activation threshold can represent or approximate the voltage / field threshold at which a nerve cell will activate or “fire.” The activation threshold can be determined based on a population, such as by relating it to a voltage / field to determine a 50% activation probability for 50% of the cell population at that voltage / field, but other boundaries / thresholds can also be used. The shape of the field can be adjusted by modifying the fractionalization of the current emitted via the electrodes using MICC, as described in various references incorporated above. For example, by using electrode 18c as the cathode and surrounding electrodes 18a, 18e, and 18d as the anode, an output that generates the activation field boundary as shown in 80 can be generated (roughly). The actual characteristics of fractionalization can be more complex than this simple example.

[0054] Figure 2 The boundary shown in field 80 can be used to illustrate the stimulus field effect and can be generated using stimulation field modeling (SFM) for display purposes. In SFM, for example, a finite element model is used to model the tissue, where the lead body is treated as an insulator surrounded by a thin encapsulating sheath and then by neural tissue. The neural tissue can be modeled as isotropic and homogeneous, but more complex models can be used if desired. A set of model voxels is defined around the lead, decomposing the volume into segments, each of which can be analyzed within the model. As mentioned above, the outer boundary of the SFM can be determined using a population-based activation threshold. The result can be that, given a fractionalization and total stimulation current, the SFM can be generated as a three-dimensional surface surrounding a portion of the lead and containing a certain volume of neural tissue. For example, field 80 can be understood as a two-dimensional representation of a slice of SFM. As mentioned above, SFM can be used as a visual tool to indicate to patients or physicians which tissues are stimulated or not stimulated by a given fractionalization and total current.

[0055] Figure 3 An illustrative method is shown in box form. In this illustrative method, the location of the lead within the patient's body is defined as 100. For illustrative purposes, the location of the lead in the brain will be explained; however, the invention can be used when modeling and optimizing neural stimulation in other parts of the body. After the lead has been implanted, the lead location 100 can be determined using imaging modalities such as X-rays, CT scans, MRI, or others.

[0056] Next, the system maps structures, as shown at 102. Mapping structure 102 may include identifying structures within the brain using preoperative and / or postoperative imaging 104 and data from brain atlas 106. Brain atlas 106 may include data from patient populations indicating the general location and nature of structures in the brain, allowing reference images based on population examples. Exemplary structures may include the thalamus, globus pallidus, subthalamic nucleus, and / or other structures described above. Data input to 102 may also include other data 108, such as, but not limited to, input from a database of therapy settings from previously programmed / treated patient populations, as needed. That is, therapies published by other implantable systems can repeatedly target similar and / or selected structures, thus providing additional understanding of, for example, best practices. Other data 108 may also include brain functional data collected using function-based imaging, or electrophysiological activity recorded using implanted leads, each of which may also be entered into the system as data input at 102.

[0057] This collective data is used to map the location of structures in the brain, as well as “sweet” and “sour” spots—regions associated with treatment benefits and / or harms or side effects. Existing systems have already treated 102 identified brain structures as deterministic, meaning specific locations and boundaries are considered the actual locations of neural structures. The probability shells are further explained below, and this invention modifies this existing method by receiving and analyzing probability shells to replace or supplement other inputs. To this end, cross-referencing patient-specific imaging with brain atlases and other data sources allows for the generation of multiple probability shells, as described below. Figure 4 and Figure 5 Further description.

[0058] Then, the user or physician selects a structure at block 110. Structures can be identified as target structures or avoidance structures. Target structures are those that the physician determines should be stimulated as much as possible, while avoidance structures are those that the physician determines should not be stimulated as much as possible. Typically, target structures are associated with therapeutic benefits, while avoidance structures are associated with side effects.

[0059] Voxel calculations occur as shown in 112. Voxel calculation 112 defines a mesh of volume elements (voxels) in the tissue region surrounding the lead and determines which voxels reside in various structures, as further described below. As used herein, "voxel" refers to any segment of volume used in the analysis, regardless of shape, and may include cubes, polygons, partial cylinders, partial toroidal shapes, etc. For simplicity, these figures illustrate volumes defined as cubes (since voxels can be defined in a Cartesian coordinate system) displayed in two dimensions. Voxel calculations can reference any of the world coordinate system, anatomical coordinate system, or image coordinate system. Other voxel definitions and coordinate systems (such as spherical or polar coordinates) can be used if desired. Some systems can use an anatomical reference to the relevant coordinate system to define lead location and structure location / positioning. Those skilled in the art will understand transformations from one coordinate system to another. Voxel calculation 112 includes using selections made at block 110 to identify which target and avoidance structures contain which voxels. A single voxel may reside in multiple structures.

[0060] Optimization is performed in iteration block 120. Structure selection 110 and voxel calculation 112, and / or device history or other inputs are used to determine the initial steering configuration 122. Then, the steering configuration 122 is used to determine... surface. The table shows the minimum total current required or likely to trigger neural activity in a given voxel, for each voxel using a given turning state and fractionalization. The values ​​in the table are used for creation. Volume histogram 124. A volume histogram is created for each target or avoidance structure. A volume histogram, containing bins for each available amplitude setting range (voltage or current level). Using... The data is presented in a table, where each voxel is characterized as being activated or inactive at multiple amplitudes. Each voxel has a "value" relative to each target or avoidance structure, calculated as discussed further below, where the voxel value partially represents the fraction of voxels within a particular structure. Each interval has a value and an associated amplitude, where the interval value is determined by calculating and summing the product of the voxel volume and the voxel value for each voxel activated at that amplitude within that interval. That is, Each interval in the volume histogram specifies the region used for generation. The table's orientation and other treatment parameters, and the variation in stimulation volume for each structure within each amplitude range.

[0061] These The content of volume histogram 124 and target / avoidance region selection are combined with weights 126 to generate The metric histogram is 128. In a simple approach, there can be two user-adjustable weights and one preset weight: target volume weight. It can be preset to 1 to avoid structural weights. and background weight It can be user-adjustable; other methods can be used to weight the amplitudes for the target, avoidance, and background structure. Therefore, for each of the multiple amplitudes, The metric histogram 128 indicates the result of each amplitude change of the metric based on which voxels are activated and within the target or avoidance region, as weighted according to weight 126.

[0062] The weighted value of each voxel is 126 and The product of volume histogram 124 is called Metric histogram 128. At block 130, the metric values ​​are integrated for each amplitude change to determine the highest metric value and the current amplitude that generates the highest metric value for the analyzed steering configuration. These values, along with those generated by previous optimization iterations, are used to generate the next steering state, indicating the next iteration, as shown in 132. If an exit condition is met, such as by showing that the metric does not increase with the new steering configuration, the iteration in 120 terminates, and at 140, candidate results including various maximum metrics and amplitudes are analyzed.

[0063] exist Figure 3 During the analysis, each voxel can be understood as having its own value, depending on whether the voxel is in the target region, the avoidance region, or the background (neither target nor avoidance). This value can then be used to generate a metric by multiplying it by weights. At a higher level, the total metric can be understood as shown in Equation 1:

[0064] Equation 1

[0065] in It is the volume of the target tissue that is stimulated. It is the volume of the avoidance zone that is being stimulated. It is the total stimulated region under optimized amplitude. In Equation 1, and As mentioned earlier, and each of these can be user-adjustable; target weights can also be included if needed. (Set to 1, omitted in Equation 1), and combine it with... Multiplication. A similar version of Equation 1 can also be used on a voxel-by-voxel basis to fill in the gaps. Measurement histogram 128.

[0066] Figure 3Other examples and possible details used in the previous description of the algorithm can be found in U.S. Patent 11,195,609, the disclosure of which is incorporated herein by reference for details of voxelization, histograms, and optimization procedures. However, the preceding description is one way to implement optimization 120. In other examples, different sequences of operations can be used. For example, in one alternative, for each given steering configuration, a search algorithm can be used to test different amplitude measurements without generating a histogram at all, wherein the search algorithm is used iteratively multiple times until the current amplitude under the given steering configuration is determined to maximize the metric calculated in Equation 1 above.

[0067] While the simplest way to find the highest metric might be to scan all available steering and amplitude configurations (as well as other parameters such as pulse width, shape, frequency, etc.), the computational burden of such a procedure can be too heavy. A more selective approach is preferable. In some examples, given a patient's anatomy, lead location, and medical condition, optimization at 120 can be achieved using similarity analysis to identify similar patient characteristics in a database, and this analysis can be used to generate a starting point for optimization at 120. As mentioned earlier, these are merely examples.

[0068] These combinations of steering configurations and stimulation parameters that produce the highest metrics can be characterized as candidate therapies, as shown in 140. Candidate therapies 140 can also be rated or analyzed using secondary factors such as power consumption. Candidate therapies can be submitted to a physician. The physician can then select the steering configuration and stimulation parameters for subsequent testing of the system. Testing can be performed using an ETS or an implantable pulse generator, if needed.

[0069] As mentioned above, Figure 3 The preceding discussion has been simplified and does not cover the use of probabilistic shells generated as part of the mapping structure process 102. A set of probabilistic shells can be generated, rather than from the deterministic output of the mapping structure process 102, thus providing richer detail and confirming the actual location of structures to be targeted and avoided that cannot be known with exact precision from the combination of imaging and brain mapping data. Furthermore, not only the overall structure can be defined, but also the region within a given structure to be targeted, such as targeting the anterior or posterior part of the given structure.

[0070] Figure 4A set of relevant probability shells is shown, illustrated as a cross-section of a three-dimensional shell. In the example, each shell represents the probability that a particular structure exists within a given boundary, or alternatively, the likelihood that a stimulus delivered to a location within the given boundary will elicit a neural response, whether desired or undesirable. For example, the outer probability shell 200 might indicate a 10% probability that a stimulus delivered within shell 200 (and outside the next shell 202) will produce a therapeutic outcome. The next probability shell 202 might indicate a similar 20% probability, and shell 204 might indicate a 50% probability. Shell sets 200, 202, and 204 can be described as nested sets of probability shells, all indicating the probability that a stimulus delivered to the interior of a shell will produce a therapeutic outcome. If shells 200, 202, and 204 are all associated with a target region, a beneficial therapeutic outcome is expected.

[0071] Figure 5 It shows Figure 4 The comparison between the probability shell and the voxel mesh is again shown as a cross-section of the 3D shell. The mesh defines multiple individual voxels 210. During the voxelization process, each voxel is analyzed by examining the boundaries and implications of each probability shell, thus deriving the voxel values. Several methods can be used.

[0072] In the first example, a separate voxelization is generated for each probability shell, each of which corresponds to a target or avoidance region. Therefore, first, second, and third voxels corresponding to shells 200, 202, and 204 are generated and stored, where the percentage value stored for each voxel is equal to the increase in probability represented by each subsequent voxel. In the following discussion, this can be referred to as “unweighted multiple targets.” Thus, the probability of the first voxelization is 10% (10% minus zero), the probability of the second voxelization increases by 10% (20% minus 10%), and the probability of the third voxelization increases by 30% (50% minus 20%). Assume that nested probability shells 200, 202, and 204 are used for target structures identified by the physician:

[0073] Voxel 210 is outside each probability shell 200, 202, 204, and the target value is 0 for each of the first, second and third voxels.

[0074] Voxel 212 is 60% inside shell 200 and completely outside shells 202 and 204, and has a target value of 0.6 for the first voxelization and a target value of 0 for each of the second and third voxelizations.

[0075] Voxel 214 is 30% within 204, completely within shells 200 and 202, and has a value of 1 for each of the first and second voxelizations and a value of 0.3 for the third voxelization.

[0076] Voxel 216 is completely located within each of shells 200, 202, and 204, and has a value of 1 for each voxelization.

[0077] In the diagram, whenever a voxel with multiple values ​​is identified, each such value can be considered a "partial voxel value." In some examples, the partial voxel values ​​are summed before being passed to the optimization module or step, while in other examples, each partial voxel value is passed instead of being summed. As mentioned earlier, each of the three voxelizations in the above example carries a step probability value, representing an increase in the probability represented by the shell. Thus, an "unweighted multiple target" is defined. Each shell can then be passed as a separate target in subsequent metric calculations. Because each shell represents a probability, the weight associated with each shell can be reduced. For example, if each shell is part of a structure with a given weight of 2, the first voxelization would be considered as having a weight of 2. The first target structure has a weight of 0.2 (weight multiplied by probability); the second shell will also have a weight of 0.2, and the third shell will have a weight of 0.6. The drawback is that the target-specific weights need to be recalculated during voxelization and then sent to... Figure 3 In the iterative analysis at point 120.

[0078] The next example can be described as using "weighted multiple targets". In this example, each voxelized voxel data can carry the probability of the corresponding shell, thus weighting the targets according to the probability. Here, for example:

[0079] Voxel 210 is outside each of the probability shells 200, 202, and 204, and the target value is 0 for each of the first, second, and third voxels.

[0080] Voxel 212 is 60% inside shell 200 and completely outside shells 202 and 204, and has a target value of 0.06 (60% multiplied by 10%) for the first voxelization and a target value of 0 for each of the second and third voxelizations.

[0081] Voxel 214 is 30% within 204, entirely within shells 200 and 202, and the target value is 0.1 (1 x 10%) for each of the first and second voxelizations and 0.09 (30% x 30%) for the third voxelization.

[0082] Voxel 216 is completely located within each of shells 200, 202 and 204, and the target value is 0.1 for each of the first and second voxels and 0.3 for the third voxel.

[0083] However, each voxelization will be passed as the first, second, and third target structures because the voxel values ​​calculated above take into account the probability of each shell, so there is no need to reduce the weight of each voxelization as in unweighted multiple targets. Here, the process uses weighted multiple targets, and the user's weighting in the optimizer remains unchanged. Instead, the user's weighting in the optimizer is used... Figure 3 Any weights assigned in the structure selection step 110 will have the same weights in the optimizer for each voxelization and generated target structure.

[0084] In the first two examples, voxel calculation 112 can forward multiple targets, with each of the first, second, and third voxelizations described above being passed to block 120. For example, during the optimization process, a given voxel can be processed multiple times as each target structure is analyzed individually. Doing so will increase the amount of analysis performed in each iteration of block 120, particularly at block 128.

[0085] In another approach, the values ​​calculated for specific voxels in the first, second, and third voxels can be summed together to form a single voxel data structure, and this single target structure can be forwarded to block 120. When doing this, using the numerical example above again, the resulting single voxel structure will have these values:

[0086] The final target value for voxel 210 will be 0+0+0=0

[0087] The final target value for voxel-212 will be 0.06 + 0 + 0 = 0.06

[0088] The final target value for voxel 214 will be 0.1 + 0.1 + 0.09 = 0.29.

[0089] The final target value for voxel 216 will be 0.1 + 0.1 + 0.3 = 0.5.

[0090] Since the maximum theoretical probability of the probability shell is 1.0, using this method, the maximum target value for any voxel will also be 1.0. Because both probability and fractional filling information are included in the final target structure voxel value, the user-defined parameters can be applied during step 128. Figure 3 The structural weights at position 110 are assigned without any modification. For the purposes of this paper, this process may be referred to as “single target summation” because the summation occurs at the end of the procedure.

[0091] Back to Figure 4 Another illustrative example performs the voxelization step by directly merging probabilities at the beginning. Using region 200 to represent the previous example with a 10% probability shell, region 202 to represent a 20% probability shell, and region 204 to represent a 50% probability shell, probabilities can be directly merged into the voxelization. The following analysis can be performed:

[0092] Voxel 210 is outside each of probability shells 200, 202, and 204, and the target value is 0.

[0093] Voxel 212 is 60% inside shell 200 and completely outside shells 202 and 204, with a target value of 0.06 (60% multiplied by 10%).

[0094] Voxel 214 is 30% within 204, entirely within shells 200 and 202, and has a target value that is the sum of 70% multiplied by 20% (for the portion outside shell 204 and inside shells 200 and 202) plus 30% multiplied by 50% or 0.14+0.15=0.29.

[0095] Voxel 216 is completely located within each of shells 200, 202, and 204, and has a target value of 0.5, which is the product of the highest applicable probability shell multiplied by 1.

[0096] This can be called "combining a single target" because the combination occurs at the beginning of the voxel definition. The resulting target structure data is the same as that of a single target summation, meaning the structure weights do not need to be adjusted, but the order and number of steps are different. The combined single-target procedure allows the computation of one voxelization instead of multiple voxels, and may require less memory than a single target summation, but uses more complex operations.

[0097] Figure 6 The illustrative method is shown in boxes. Two alternative methods are shown in the figure, but each method begins with the same basic steps because the nested shell is received at 300, and feature 302 is the target (T), avoidance (A), or optionally, unrelated (T). Here, the physician can display multiple structures near the implanted lead via a user interface, identifying these structures as targets or avoidance structures. If the physician does not identify a particular structure as a target or avoidance, it may fall into an irrelevant category and be treated as background. At block 304, voxel values ​​are generated for each target (T) and avoidance (A) shell. In block 304, a single voxel structure (such as summing or combining a single target analysis) is generated and passed at 320, or multiple voxels are passed (such as by weighted or unweighted multiple targets), as shown in 310.

[0098] If multiple voxels are passed at 310, there are two ways to do so. One is to use weighted multiple targets, passing multiple targets forward, each with its own weight, as described above. The other is to use unweighted multiple targets, where multiple targets are passed to the optimizer, which uses the same weight for each target during the optimization process. As shown at 310, multiple voxels can be passed as separate target structures for further analysis; these target structures may or may not overlap. This means that the voxels generated at 312... The volume histogram will include each voxelized Volume histogram. If individual target structures overlap, a voxel can be processed multiple times during block 312; this is possible in any case because a single voxel can be part of multiple structures. In calculating each... Following the volume histogram, as shown in 314, the analysis results for each target are summed as part of the metric calculation. For weighted multiple targets, block 314 references the different weights of each individual target associated with a given structure; for unweighted multiple targets, block 314 can refer to the same weights for each target associated with that structure. With each of the multiple target methods... The number of calculations during the optimization process and the amount of memory required for additional histograms may increase the required processing power.

[0099] On the other hand, individual voxel structures can be computed as shown in block 320. This can include the summation of individual targets or the combination of individual target analyses described above. These configurations within the optimizer may require fewer and simpler floating-point operations, as generated at block 322... Fewer voxel structures are used when plotting volume histograms. As shown in 324, the metric for this single target can be calculated, as well as... The remainder of the volume histogram. Single-target methods may require additional analysis before passing the target to the optimizer. Figure 3 Block 120), but it is easier to process in one go during the optimization process.

[0100] In each of the above examples of reference shells 200, 202, and 204, the same analysis can be applied if the physician identifies the avoidance structure corresponding to shells 200, 202, and 204 instead of the target structure.

[0101] In an alternative approach, avoidance structures can be distinguished from target structures by completely ignoring probabilities and treating all shells representing probabilities higher than a predetermined threshold as having a probability of 1. For example, any shell with a probability equal to or greater than 10% can be considered as having a probability of 1; if so, again assuming shell 200 represents a 10% probability, and shells 202 and 204 represent even greater probabilities, then all shells 200, 202, and 204 can be considered as having a probability of 1. If desired, the fill ratio of a single voxel can still be used, such that if the avoidance voxel is completely within any of shells 200, 202, and 204, the value can be 1, and if it is outside all shells, the value can be 0, and the value can be equal to the fill ratio. Alternatively, the fill ratio can be considered as 1 for any voxel that has any part within the avoidance shell. Various combinations of these analyses are conceivable.

[0102] Figure 7 An illustrative system is shown. This system may include a data receiver block 400. For example, communication circuitry 450 may be coupled to a software or hardware module configured to receive and store information relating to patient anatomy, lead location, and / or other information, including, for example, population-based structural data. As needed, the data input to 400 may also include input from a database of therapeutic settings from previously programmed / treated patient populations. Furthermore, brain functional data collected using function-based imaging, or electrophysiological activity recorded using implanted leads, may also be used as data input at 400 as part of a holistic mapping of “sweet” and “sour” points—regions associated with therapeutic benefits and / or harms or side effects. Then, at 402, the data in block 400 is used for voxel definition, which may be implemented as another software module and / or by a separate or dedicated circuit (application-specific integrated circuit, microcontroller, etc.). Voxel definition block 402 provides voxelization to structure selection block 404.

[0103] The structure selection block 404 is configured to receive user / physician input from a user interface 406, which may include one or more of a keyboard, mouse, trackball, touchscreen, monitor / output screen, voice, or other audio input / output devices. The structure selection block 404 may also receive structural data from a data receiver 400, such as data that helps identify structural boundaries and structures. The user interface 406 allows the user / physician to identify and select targets and avoid areas within the patient's anatomy for use by the structure selection block 404. If necessary, considering the clinician's expected treatment and / or system labeling limitations, the block 404 may also restrict which structures can be selected as targets.

[0104] The user interface is also used to allow users / doctors to select or modify the structural weights for background, targets, and / or avoidance as described above. Structural selection 404 can be applied to voxel definitions to prioritize each structure based on a structure-by-structure identification stimulus (e.g., using a separate weight for each target structure) and / or avoidance (e.g., using a separate weight for each avoidance structure). The effect of structural selection 404 is then to use voxelization to determine the value of each voxel for each target and avoidance structure. As used herein, the voxel values ​​are as described above to indicate the number of voxels filled by a structure or structural portion (such as a probability shell), the probability of a neural response occurring if stimulated, and / or the presence of a structure (whether target or avoidance) within the voxel. Target and avoidance structure data, along with their corresponding voxel values, are passed to optimizer 420.

[0105] In some examples, the data entering block 420 can be any of the voxelizations described above, including, for example, single or multiple target / avoidance structure data corresponding to nested probability shells, such as single target summation, combined single target, weighted multiple targets, and unweighted multiple targets. Optimizer 420 begins a series of iterative analyses at 422 by selecting a steering configuration. The table is generated at 424, and the metric / amplitude data is generated at 426. That is, for a given steering configuration, the metric / amplitude data provides an indication of the resulting metric and amplitude pairings. Optimizer 420 selects one or more optimal combinations and stores them in memory 430. If no exit condition occurs, the next iteration is triggered at 428, and a new steering configuration is set at 422, and the process continues iterating. The exit condition and steering reconfiguration selection can be as described, for example, in U.S. Patent 11,195,609, the disclosure of which is incorporated herein by reference.

[0106] When the exit conditions are met, the dataset in memory 430 will provide one or more “best” or highest-scoring steering settings and amplitude or parameter selections. These are then presented to the user / doctor by the therapy selection module 440 via user interface 406. The user / doctor can then select or approve one or more suggested therapies. The therapy selection block 440 can generate an SFM for display via user interface 406 to aid the therapy selection process. Once the user has selected a therapy for the implementation, communication block 450 transmits the therapy to the pulse generator or ETS. The pulse generator or ETS then delivers the selected therapy to the patient.

[0107] The turn selection at 422 can utilize artificial intelligence methods and / or search functions with predetermined stopping conditions. In the example, block 420 can be configured to receive structural selection data that can inform the process of selecting a turn configuration. For example, the turn selection for an iterative process can use (but is not limited to) a database of turn selection configurations for other similar patients. By generally comparing the database of turn configurations with the received structural selections, optimizer 420 and turn selection block 422 are able to quickly eliminate most of the possible turn configurations to build a shortened list. Some illustrative methods include iterative optimization methods such as gradient descent search, genetic algorithms, simulated annealing, stochastic coordinate descent, particle swarm optimization, fuzzy logic, and other machine learning search algorithms. Various details of the search process are also explained in U.S. Patent 11,195,609, the disclosure of which is incorporated herein by reference.

[0108] While the above discussion largely focuses on the use of DBS, other tissue areas can also be treated. For example, anatomical mapping can be used to identify nerves and / or other structures to be targeted or avoided during other therapies, such as spinal cord stimulation (SCS), peripheral nerve stimulation, occipital nerve stimulation, muscles and muscle nerve fibers, therapies targeting the digestive tract or other areas, and vagus nerve stimulation. For instance, in the SCS, such as in the cervical spine, the spinal cord carries neural signals from various parts of the body. With advancements in science related to spinal cord structure, knowledge becomes available about which parts of the spinal cord at a given vertebral level carry signals to which parts of the body or from which parts of the body. Using this knowledge, therapeutic and avoidance zones in the spinal cord can be defined / identified. If multiple leads or paddle leads are present at a given vertebral level, spinal cord atlases and lead implantation / location data (such as from X-rays or other imaging systems) can be used to determine steering and SFM models for that given vertebral level. A similar process to that described above can be used to define therapies that target the portions of the spinal cord that carry the neural signals to be interfered with (such as pain signals), while other portions (such as those carrying motor signals) are to be avoided. The aforementioned metric calculations can then be used to steer the optimizer to limit side effects and achieve the desired therapy.

[0109] Back Figure 7The data receiver 400 may include communication circuitry (transceiver, antenna, etc., such as Bluetooth, WiFi, medical radio, etc.) and / or input / output circuitry for use, for example, with a local area network cable. On the other hand, in some examples, the data receiver 400 is a software module that communicates with other applications in the CP; communication via external hardware may be optional for the data receiver 400. A microcontroller or microprocessor, containing specially configured software or other instructions, may be stored, for example, in non-transitory memory, such as memory 430 known in the art, which may include flash memory, RAM, ROM, etc. Overall, Figure 7 The implementation can be done on a desktop or laptop computer, such as the clinician programmer or CP mentioned above.

[0110] Each of these unrestricted examples can exist independently, or can be arranged or combined with one or more other examples in various ways.

[0111] The above detailed description includes reference to the accompanying drawings, which form part of the detailed description. The drawings illustrate specific embodiments by way of illustration. These embodiments are also referred to herein as “examples.” These examples may include elements other than those shown or described. However, the inventors also contemplate examples that provide only those elements shown or described. Furthermore, the inventors contemplate examples of any combination or arrangement of those elements (or one or more aspects thereof) shown or described, whether relating to a particular example (or one or more aspects thereof) or other examples (or one or more aspects thereof) shown or described herein.

[0112] In the event of any inconsistency between the usage in this document and any other document incorporated by reference, the usage in this document shall prevail.

[0113] In this document, the terms “a” or “an” are used as commonly found in patent documents to include one or more, independent of any other instances or uses of “at least one” or “one or more.” Furthermore, in the following claims, the terms “first,” “second,” and “third,” etc., are used merely as labels and are not intended to impose numerical requirements on their objects.

[0114] The methods described herein can be implemented, at least in part, by a machine or computer. Some examples may include computer-readable media or machine-readable media encoded with instructions operable to configure an electronic device to perform the methods described in the examples above. Implementations of these methods may include code, such as microcode, assembly language code, high-level language code, etc. Such code may include computer-readable instructions for performing various methods. The code may form part of a computer program product. Furthermore, in the examples, such as during execution or at other times, the code may be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media. Examples of such tangible computer-readable media may include, but are not limited to, hard disks, removable disks or optical discs, magnetic tape cassettes, memory cards or memory sticks, random access memory (RAM), read-only memory (ROM), etc.

[0115] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments may be used, such as those that can be used by those skilled in the art after reading the above description.

[0116] Providing a summary is to comply with 37 CFR The provisions of 1.72(b) are intended to enable the reader to quickly determine the nature of the technical disclosure. This document is submitted on the condition that it is not used to interpret or limit the scope or meaning of the claims.

[0117] Furthermore, in the above detailed description, various features may be combined to simplify this disclosure. This should not be construed as meaning that any unclaimed disclosed feature is essential to any claim. Rather, the subject matter of innovation may lie not in all the features of a particular disclosed embodiment, but in some of them. Therefore, the following claims are incorporated into the detailed specification as examples or embodiments, each claim existing independently as a separate embodiment, and it is conceivable that these embodiments may be combined with each other in various combinations or arrangements. The scope of the invention should be determined by reference to the appended claims and the full scope of their equivalents.

Claims

1. A configuration system for configuring the delivery of neural modulation to a specific tissue of a patient, the system comprising: A receiver module (400) is configured to receive at least brain anatomy data of the patient and lead position data of a lead forming part of a neural modulation system, the lead position data indicating the location of the lead in the patient's brain; A structure selection module (404), coupled to a user interface (406), provides graphical output, thereby allowing the user to identify and select brain structures in the patient’s brain as target structures and avoidance structures; Voxel definition module (402), configured to define portions of the patient's brain in voxel form as a voxel data structure; wherein: The receiver module receives multiple nested probability shells for neural structures in the brain anatomy data, the nested probability shells indicating the probability of a therapeutic outcome caused by stimulation of a volume defined by the nested probability shells; as well as The voxel definition module uses the probability shells among the multiple nested probability shells to determine the voxel value for each voxel in the voxel data structure.

2. The configuration system of claim 1, wherein each probability shell has an outer boundary and defines a probability increase relative to a volume outside the probability shell, and the voxel definition module is configured to determine the voxel value for each voxel in the voxel data structure using the probability shells of the nested probability shells for the neural structure by: a) Select the first probability shell; b) For the selected probability shell, calculate the filling amount of at least a portion of each voxel therein, the filling amount representing the percentage of the voxel within the selected probability shell; c) For each voxel in the selected probability shell, multiply the filling amount by the probability increase of the selected probability shell to produce a partial voxel value. a), b), and c) are repeated for each of the nested probability shells in the neural structure.

3. The configuration system of claim 2 further includes an optimization block configured to use data passed from the voxel definition module to determine optimal steering and amplitude settings for use by the neural modulation system; wherein, The voxel definition module is configured to pass multiple target shell and avoidance shell structures to the optimization block, including one or more target shell or avoidance shell structures generated by performing steps a), b) and c) on the selected probability shell of the nested probability shell of the neural structure.

4. The configuration system of claim 3, wherein each target shell or avoidance shell structure comprises a plurality of partial voxel scores.

5. The configuration system according to claim 2, wherein, The voxel definition block is configured to sum the partial voxel values ​​of each voxel to produce a summed voxel value for each voxel relative to the neural structure. The system also includes an optimization block configured to use data passed from the voxel definition module to determine optimal steering and amplitude settings for use by the neural modulation system. The voxel definition module is configured to pass the summed voxel values ​​for the neural structure.

6. The configuration system of claim 1, wherein each probability shell has an outer boundary and defines a probability applicable to volumes located outside any other nested probability shells within the probability shell, and the voxel definition module is configured to determine the voxel value of each voxel in the voxel data structure using the probability shells among the plurality of nested probability shells for each corresponding probability shell in which the voxel is at least partially located, in such a way as: a) Calculate partial fill, whereby the partial fill represents the percentage of the voxel in the corresponding probability shell; b) Calculate the partial voxel value by multiplying the partial fill by the probability for the corresponding probability shell; After completing a) and b) for each corresponding probability shell, sum all partial voxel values ​​for each voxel such that each voxel has a single summed voxel value relative to the nested probability shell of the neural structure.

7. The configuration system of claim 6, further comprising an optimization block configured to use data from the voxel definition module to determine optimal steering and amplitude settings for use by the neural modulation system; wherein, The voxel definition module is configured to pass the summed voxel values ​​to the optimization block.

8. The configuration system of claim 1, wherein each probability shell has an outer boundary, the outer boundary defining an increase in probability relative to an organization outside the probability shell, and the voxel definition module determines the voxel value of each voxel in the voxel data structure using the probability shells among the plurality of nested probability shells in such a way as: Choose the first probability shell; For a selected probability shell, calculate the filling amount of at least a portion of each voxel therein, the filling amount representing the percentage of the voxels within the selected probability shell; and For the selected probability shell, the shell weight is calculated by multiplying the increased probability of the selected probability shell by the target weight or avoidance structure weight received from the user via the structure selection block.

9. The configuration system of claim 8, further comprising an optimization block configured to use data passed from the voxel definition module to determine optimal steering and amplitude settings for use by the neural modulation system; wherein, The voxel definition module is configured to pass a set of voxel fill values ​​and a shell weight for each nested probability shell.

10. The configuration system according to any of the preceding claims, wherein the target structure corresponds to a beneficial treatment outcome and the avoidance structure corresponds to an adverse treatment outcome.

11. The configuration system according to any one of claims 3, 5, 7, and 9, wherein the optimization block includes a metric calculator configured to determine each of the following: The target value is calculated by determining the activation volume of the target structure for the selected steering configuration and amplitude; The avoidance structure penalty is calculated by determining the activation volume of the avoidance structure for the selected steering configuration and amplitude; Background penalty, calculated using the total active volume for the selected steering configuration and amplitude; and The target value is measured by subtracting the avoidance structure penalty and the background penalty.

12. The configuration system of claim 11, wherein the optimization block uses user-defined avoidance structure weights to calculate the avoidance structure penalty and uses user-defined background ratio weights to calculate the background penalty.

13. The configuration system according to any of the preceding claims further includes a therapy selection module and a communication module, the therapy selection module being adapted to: Present the user with at least one suggested treatment configuration for the user to choose from; and In response to the user selecting a suggested therapy configuration for use, the communication module is instructed to send a command to the pulse generator of the neural modulation system to implement the selected suggested therapy configuration.

14. The configuration system of claim 13, wherein the therapy selection module is configured to present the user with a graphical representation of a stimulation field model for the suggested therapy configuration.

15. A neural modulation system comprising a pulse generator, leads configured to be coupled to the pulse generator and adapted to be positioned in the brain of a patient; and a configuration system as claimed in any one of claims 13 or 14, wherein the pulse generator is adapted to receive instructions from the therapy selection module and configure a selected recommended therapy to be applied to the patient via the leads.

Images