Neuroanatomical-based search for optimizing trajectory selection during DBS targeting

Abstract

Methods and systems for planning the trajectory and location of implanting an electrical stimulation lead in the brain of a patient are described. An apparatus for planning a position of a stimulation lead for neurostimulation of one or more target structures of a patient's brain, where the stimulation lead comprises a tip, a longitudinal axis, and a plurality of electrode contacts, the apparatus comprising: a processor configured to: receive indications of a plurality of candidate positions for the stimulation lead; determining a set of optimized stimulation parameters for each of the candidate locations; predicting an activated tissue volume (VTA) for a set of optimized stimulation parameters for each of the candidate locations; an overlap of each of the predicted VTAs with the target structure is determined, and the plurality of candidate locations are ranked based at least in part on the overlap. For each of the candidate locations, the processor is configured to predict a therapeutic effect using the optimized stimulation parameters for the candidate location, and rank the plurality of candidate locations based at least in part on the predicted therapeutic effect.

Description

Technical Field

[0001] This application relates to implantable stimulator devices (ISDs), and more particularly, to algorithms and systems for selecting trajectory selections in ISDs such as deep brain stimulation (DBS) devices. Background Art

[0002] Implantable nerve stimulator devices are devices that generate and deliver electrical stimulation to body nerves and tissues to treat various biological diseases, such as pacemakers for treating arrhythmias, defibrillators for treating cardiac fibrillation, cochlear stimulators for treating deafness, retinal stimulators for treating blindness, muscle stimulators for generating coordinated limb movement, spinal cord stimulators for treating chronic pain, cortical and deep brain stimulators for treating movement and psychological diseases, and other nerve stimulators for treating urinary incontinence, sleep apnea, shoulder subluxation, etc. The following description will generally focus on the use of the present invention in a deep brain stimulation (DBS) system, such as the system disclosed in U.S. Patent Application Publication 2020 / 0001091, which is incorporated herein by reference. However, the present invention can be applied to any implantable nerve stimulator device system, including spinal cord stimulation (SCS) systems, vagus nerve stimulation (VNS) systems, sacral nerve stimulation (SNS) systems, peripheral nerve stimulation (PNS) systems, etc.

[0003] The DBS system generally includes an implantable pulse generator (IPG) 10 shown in FIG. 1A. The IPG 10 includes a biocompatible device housing 12 that houses circuitry and a battery 14 for powering the operation of the IPG, although the IPG 10 may also lack a battery and may be wirelessly powered by an external source. The IPG 10 is coupled to a tissue stimulation electrode 16 via one or more electrode leads 18 or 19, which are shown in more detail in FIGS. 1B and 1C.

[0004] FIG. 1B shows a lead 18 having eight annular electrodes 16 that are located at different longitudinal positions along a central axis 15. The lead 18 is referred to herein as a "non-directional lead" because the annular electrodes span 360 degrees around the axis 15 and thus cannot direct stimulation to different rotational positions around the axis 15.

[0005] FIG. 1C shows lead 19 which also has eight electrodes, but not all of the electrodes are annular. Electrodes E8 at the distal end of lead 19 and electrode E1 at the proximal end of the lead are annular. In contrast, electrodes E2, E3, and E4 include split-ring electrodes, and each of the split-ring electrodes is located at the same longitudinal position along axis 15, but each split-ring spans less than 360 degrees about the axis. For example, each of electrodes E2, E3, and E4 can span 90 degrees about axis 15, with each electrode spaced from the others by a 30-degree gap. Electrodes E5, E6, and E7 also include split-ring electrodes, but are located at different longitudinal positions. Lead 19 is referred to herein as a "directional lead" because at least some of the electrodes (such as E2, E3, E4) at a given longitudinal position span less than 360 degrees, meaning that these electrodes can direct stimulation about axis 15 to different rotational positions (and thus to different brain tissues). In other designs of directional lead 19, all of the electrodes can be split-rings, or there can be a different number of split-ring electrodes (i.e., more or fewer than three) at each longitudinal position.

[0006] The wires 20 within the lead are coupled to the electrodes 16 and to the proximal contacts 21 of a lead connector 22 that can be inserted into a head 23 fixed to the IPG 10. The head can include, for example, epoxy resin. Alternatively, the proximal contacts 21 can be connected to a lead extension (not shown) which is in turn inserted into the lead connector 22. Once inserted, the proximal contacts 21 connect to head contacts 24 within the lead connector 22, which in turn are coupled by feedthrough pins 25 through a housing feedthrough 26 to a stimulation circuitry 28 within the housing 12, which will be described below.

[0007] In the IPG 10 shown in FIG. 1A, there are 32 electrodes (E1 - E32), distributed among four percutaneous leads 18 or 19 (18 is shown), so the head 23 can include a 2x2 array of eight electrode lead connectors 22. However, the type and number of leads in the IPG and the number of electrodes vary depending on the application and can thus be different. In another example (not shown), a given lead can have 16 electrodes, so the lead will have two sets of proximal contacts 21 to mate with two of the eight electrode lead connectors 22, as disclosed in, for example, U.S. Patent Application Publication 2019 / 0076645. The conductive housing 12 can also include an electrode (Ec).

[0008] In DBS applications (such as for treating tremors in Parkinson's disease), the IPG 10 is typically implanted under the patient's collarbone (clavicle). The lead 18 or 19 (which may extend through a lead extension, not shown) passes through the neck and scalp and forms a tunnel thereunder, where the electrode 16 is implanted through a hole drilled in the skull and positioned in the brain. The IPG 10 can also be implanted under the scalp closer to the location of the electrode implantation, as disclosed in, for example, USP 10,576,292. In other solutions, one or more IPG leads 18 or 19 can be integrated with the IPG 10 and permanently connected to the IPG 10.

[0009] The IPG 10 can include an antenna 27a, allowing it to communicate bidirectionally with a plurality of external devices and systems discussed subsequently. As shown, the antenna 27a includes a conductive coil within the housing 12, although the coil antenna 27a can also be present in the head 23. When the antenna 27a is configured as a coil, near-field magnetic induction is preferably used to communicate with the external system. The IPG 10 can also include a radio-frequency (RF) antenna 27b. In FIG. 1A, the RF antenna 27b is shown within the head 23, but it can also be within the housing 12. The RF antenna 27b can include a patch, slot, or wire, and can operate as a monopole or dipole. The RF antenna 27b preferably uses far-field electromagnetic waves for communication and can operate according to any number of known RF communication standards (such as Bluetooth, Zigbee, WiFi, MICS, etc.). If the IPG 10 lacks a battery 14, additional coils can be present to receive wireless power from an external source.

[0010] Stimulation in the IPG 10 is typically provided by pulses, each of which can include a plurality of phases (such as 30a and 30b), as shown in the examples of FIGS. 2A and 2B. In the example shown, such stimulation is unipolar, meaning that current is provided between at least one selected lead-based electrode (e.g., E1) and the case electrode Ec 12. The stimulation can be bipolar, in which current is provided between at least two lead-based electrodes, as shown, or can be unipolar. Stimulation parameters typically include amplitude (current I, but voltage amplitude V can also be used); frequency (F); pulse width (PW) of the pulse or its individual phases (such as 30a and 30b); the electrodes 16 selected to provide the stimulation; and the polarities of these selected electrodes, i.e., whether they act as anodes providing current to the tissue or cathodes drawing current from the tissue. These, along with other possible stimulation parameters employed, collectively comprise a stimulation program that the stimulation circuitry 28 in the IPG 10 can execute to provide therapeutic stimulation to the patient.

[0011] In the example of FIG. 2A, electrode E1 has been selected (during its first phase 30a) as the cathode, thus providing a pulse that draws a negative current of magnitude -I from the tissue. The case electrode Ec has been selected (also during the first phase 30a) as the anode, thus providing a corresponding positive current of magnitude +I to the tissue. Note that at any time, the current drawn from the tissue (e.g., -I at E1 during phase 30a) is equal to the current provided to the tissue (e.g., +I at Ec during phase 30a). The current polarities at these electrodes can be changed: for example, during the first phase 30a, Ec can be selected as the cathode and E1 can be selected as the anode, and so on. Monophasic stimulation can also be used.

[0012] The IPG 10 as described above includes a stimulation circuitry 28 to form a prescribed stimulation at the patient's tissue. FIG. 3 shows an example of the stimulation circuitry 28, which includes one or more current sources 40 i and one or more current sinks 42 i . The source 40 i and the sink 42 i can include digital-to-analog converters (DACs), and can be referred to as PDAC 40 i and NDAC 42 i according to the positive (source, anode) and negative (sink, cathode) currents they respectively emit. In the example shown, the NDAC / PDAC 40 i / 42 i pair is dedicated (hardwired) to a specific electrode node ei 39. For reasons described below, each electrode node ei 39 is connected to the electrode Ei 16 via a DC blocking capacitor Ci 38. The PDAC 40 i and the NDAC 42 i can also include voltage sources.

[0013] Proper control of the PDAC 40 i and the NDAC 42 i allows any one of the electrodes 16 and the case electrode Ec 12 to act as an anode or a cathode to generate a current through the patient's tissue Z (such as the pulses described previously), desirably with a good therapeutic effect. In the example shown, consistent with the first pulse phase 30a of FIG. 2A, electrode E1 has been selected as the cathode electrode to draw current from the tissue Z, and the case electrode Ec has been selected as the anode electrode to provide current to the tissue Z. Thus, the PDAC 40 C and the NDAC 42 1is activated and digitally programmed to generate the desired current I at the correct timing (e.g., according to a specified frequency F and pulse width PW). The power for the stimulation circuitry 28 is provided by the compliance voltage VH, as described in further detail in U.S. Patent Application Publication 2013 / 0289665. Other stimulation circuitry 28 may also be used for the IPG 10. In an example not shown, a switch matrix may intervene between one or more PDACs 40 i and the electrode nodes ei 39, and between one or more NDACs 42 i and the electrode nodes. The switch matrix allows one or more of the PDACs or one or more of the NDACs to be connected to one or more electrode nodes at a given time. Various examples of stimulation circuitry can be found in USP 6,181,969, 8,606,362, 8,620,436, U.S. Patent Application Publications 2018 / 0071520 and 2019 / 0083796.

[0014] As described in U.S. Patent Application Publications 2012 / 0095529, 2012 / 0092031, and 2012 / 0095519, most of the stimulation circuitry 28 of FIG. 3 (including the PDAC 40 i and the NDAC 42 i , the switch matrix (if present), and the electrode nodes ei 39) can be integrated on one or more application specific integrated circuits (ASICs). As explained in these references, one or more ASICs may also contain other circuitry useful in the IPG 10, such as telemetry circuitry (for off-chip interfacing with the telemetry antennas 27a and / or 27b), circuitry for generating the compliance voltage VH, various measurement circuits, etc.

[0015] Also shown in FIG. 3 are DC blocking capacitors Ci 38 placed in series in the electrode current path between each of the electrode nodes ei 39 and the electrodes Ei 16 (including the case electrode Ec 12). The DC blocking capacitors 38 serve as a safety measure to prevent DC current injection into the patient, which could occur, for example, if there is a circuit fault in the stimulation circuitry 28. The DC blocking capacitors 38 are typically provided off-chip (outside one or more ASICs), but may be disposed in or on the circuit board used to integrate the various components of the IPG 10, as described in U.S. Patent Application Publication 2015 / 0157861.

[0016] Referring again to FIG. 2A, the stimulation pulses shown are biphasic, where each pulse includes a first phase 30a, followed by a second phase 30b of opposite polarity. It is well known that biphasic pulses can be used to actively recover any charge that may be stored on capacitive elements in the electrode current path, such as the charge on the DC blocking capacitor 38. FIG. 3 also shows that the stimulation circuitry 28 can include a passive recovery switch 41 i , which is further described in U.S. Patent Application Publications 2018 / 0071527 and 2018 / 0140831. The passive recovery switch 41 i can be closed to passively recover any remaining charge on the DC blocking capacitor Ci 38 after the second pulse phase 30b is issued, i.e., recover the charge without using the DAC circuitry to actively drive current, as shown by duration 30c. Alternatively, passive charge recovery can be used during the second pulse phase 30b after the actively driven first pulse phase 30a, although this is not shown in FIG. 2A. Similarly, passive charge recovery is well known and will not be described further.

[0017] FIG. 4 shows various external systems 60, 70, and 80 that can communicate wirelessly with the IPG 10. Such systems can be used to wirelessly send a stimulation program to the IPG 10, i.e., program its stimulation circuitry 28 to generate stimulations having the desired amplitude and timing as described above. Such systems can also be used to adjust one or more stimulation parameters of the stimulation program currently being executed by the IPG 10, and / or wirelessly receive information from the IPG 10, such as various status information and measurements.

[0018] For example, the external controller 60 can be as described in U.S. Patent Application Publication 2015 / 0080982 and can include a portable handheld controller dedicated to working with the IPG 10. The external controller 60 can also include a general-purpose mobile electronic device (such as a mobile phone) programmed with a medical device application (MDA), allowing it to function as a wireless controller for the IPG 10, as described in U.S. Patent Application Publication 2015 / 0231402. The external controller 60 includes a display 61 and means for inputting commands (such as buttons 62 or optional graphical icons provided on the display 61). The user interface of the external controller 60 enables a patient to adjust stimulation parameters, although its functionality may be limited compared to the systems 70 and 80 described later. The external controller 60 can have one or more antennas capable of communicating with a compatible antenna in the IPG 10, such as a near-field magnetic induction coil antenna 64a and / or a far-field RF antenna 64b.

[0019] The clinician programmer 70 is further described in U.S. Patent Application Publication 2015 / 0360038 and may include a computing device such as a desktop computer, laptop or notebook computer, tablet computer, mobile smart phone, personal data assistant (PDA) type mobile computing device, etc. In FIG. 4, the computing device is shown as a laptop computer, which includes typical computer user interface devices such as a display 71, buttons 72, and other user interface devices such as a mouse, keyboard, speaker, stylus, printer, etc. For convenience, not all of these devices are shown. Also shown in FIG. 4 are accessory devices of the clinician programmer 70, which are typically specific to its operation as a stimulation controller. A communication “stick” 76 that can be coupled to a suitable port on the computing device may include an IPG-compatible antenna such as a coil antenna 74a or an RF antenna 74b. The computing device itself may also include one or more RF antennas 74b. The clinician programmer 70 can also communicate wirelessly or via a wired link provided at an Ethernet or network port with other devices and networks such as the Internet.

[0020] The external system 80 includes another device that communicates with and controls the IPG 10 via a network 85 that may include the Internet. The network 85 may include a server 86 programmed with IPG communication and control functions and may include other communication networks or links such as WiFi, cellular or landline links, etc. The network 85 ultimately connects to an intermediate device 82 having an antenna suitable for communicating with the IPG's antenna such as a near-field magnetic induction coil antenna 84a and / or a far-field RF antenna 84b. The intermediate device 82 may be located generally near the IPG 10. The network 85 may be accessed by any user terminal 87, which typically includes a computer device associated with a display 88. The external system 80 allows a remote user at the terminal 87 to communicate with and control the IPG 10 via the intermediate device 82.

[0021] FIG. 4 also shows the circuitry 90 involved in any of the external systems 60, 70, or 80. Such circuitry may include control circuitry 92, which may include any number of devices capable of executing programs in a computing device, such as one or more microprocessors, microcomputers, FPGAs, DSPs, other digital logic structures, etc. Such control circuitry 92 may contain or be coupled to a memory 94, which may store external system software 96 for controlling and communicating with the IPG 10, and for presenting a graphical user interface (GUI) 99 on a display (61, 71, 88) associated with the external system. In the external system 80, the external system software 96 may reside in the server 86, while the control circuitry 92 may be present in either or both of the server 86 or the terminal 87.

[0022] Figure 5A An example of the GUI 99 that may be presented on the display of an external system, such as the clinician programmer 70 mentioned previously, is shown. The GUI 99 is particularly useful in a DBS environment because it provides the clinician with a visual indication of how the stimulation selected for the patient will interact with the brain tissue of the implanted electrodes. The GUI 99 may be used during the surgical implantation of the leads 18 or 19 and their IPG 10, but may also be used after implantation to assist in selecting a therapeutically useful stimulation program for the patient. The GUI 99 may be controlled by a cursor 101. For example, the user may use a mouse connected to the clinician programmer 70 to move the cursor 101.

[0023] The GUI 99 may include a waveform interface 104 in which various aspects of the stimulation may be selected or adjusted. For example, the waveform interface 104 allows the user to select the amplitude (e.g., current I), frequency (F), and pulse width (PW) of the stimulation pulses. The waveform interface 104 may be significantly more complex, especially if the IPG 10 supports providing more complex stimulation than a repetitive pulse train. The waveform interface 104 may also include inputs to allow the user to select whether to use biphasic (FIG. 2A) or monophasic pulses, or to provide the stimulation in the form of a burst, and to select whether passive charge recovery will be used, although these details are not shown for simplicity.

[0024] The GUI 99 may also include an electrode configuration interface 105 that allows a user to select a specific electrode configuration that specifies which electrodes should be active to provide stimulation, as well as which polarities and relative amplitudes to use. In this example, the electrode configuration interface 105 allows the user to select whether an electrode should include an anode (A) or a cathode (C) or be off, and allows the amount of the total anode or cathode current +I or -I (specified in the waveform interface 104) that each selected electrode will receive to be specified as a percentage X. For example, in Figure 5A the case of Figure 5A , the housing electrode 12Ec is specified to receive X = 100% of the current I as the anode for the anode current +I (e.g., during the first pulse phase 30a if biphasic pulses are used; see FIG. 2A). The corresponding cathode current -I (also during the first pulse phase 30a) is distributed among the cathode electrodes E2 (18% or 0.18 * -I), E4 (52% or 0.52 * -I), E5 (8% or 0.08 * -I), and E7 (22% or 0.22 * -I). The waveform generated on the electrodes by this electrode configuration is as shown in Figure 5B . Note that two or more electrodes can be selected to act as an anode or a cathode at a given time, thus shaping the electric field in the tissue, as explained further below. Once the waveform parameters (104) and the electrode configuration parameters (105) are determined, they can be sent from the clinician programmer 70 to the IPG 10 such that the stimulation circuitry 28 (FIG. 3) of the IPG can be programmed (various NDACs and PDACs) to generate the desired current at the selected electrodes at the appropriate time. For example, the PDAC 40 C will be programmed to generate +100% * +I, and the NDAC 42 4 will be programmed to generate 52% * -I, etc. The various waveform parameters and electrode configuration parameters include stimulation parameters that together constitute a stimulation program.

[0025] Using these electrodes to provide cathodic stimulation sets a specific position for the cathode 120 in three-dimensional space. The position of this cathode 120 can be quantified along a specific longitudinal position L of the lead (e.g., relative to a point on the lead, such as the longitudinal position of electrode E1) and a specific rotation angle θ (e.g., relative to a specific angle on the lead, such as relative to the center of electrode E2). (Note that the rotation angle θ is only relevant when using a directional lead, such as 19 (Figure 1C)). This position is shown in the lead interface 102 of the GUI 99. Note that the position of the pole 120 (L, θ) can be virtual; that is, this position may not necessarily occur at the physical location of any of the electrodes 16 in the electrode array, as further explained later. The lead interface 102 preferably also includes an image 103 of the lead for the patient. Although not shown, the lead interface 102 can include options for accessing a library 103 of relevant representations of the types of leads (e.g., 18 or 19) that can be implanted in different patients, and these relevant representation libraries can be stored together with relevant software (e.g., 96, Figure 4). The cursor 101 can be used to select the electrodes 16 shown (e.g., E1 - E8 or the case electrode Ec), or a pole such as the cathode 120. The pole 120 can also be an anode, or if multipolar stimulation is used, there can be more than one pole, but not shown.

[0026] An electrode configuration algorithm (not shown) operating as part of the external device software 96 can determine the position of the cathode 120 in three-dimensional space based on a given electrode configuration, and can also, conversely, determine the electrode configuration based on a given position of the pole 120. For example, the user can use the cursor 101 to place the position of the pole 120. Then, the electrode configuration algorithm can be used to calculate the electrode configuration that best places the pole 120 at that position. Note that the cathode 120 is positioned closest to electrode E4, but is generally also close to electrodes E2, E7, and E6. Thus, the electrode configuration algorithm can calculate that electrode E4 should receive the largest share of the cathodic current (52% * -I), while E2, E7, and E6, which are farther from the pole 120, receive smaller percentages, as shown in the stimulation parameter interface 104. By involving more than one electrode, the cathode 120 is formed as a virtual pole rather than the position of any physical electrode. Similarly, the electrode configuration algorithm can also operate in reverse: based on a given electrode configuration, the position of the pole 120 can be determined. The electrode configuration algorithm is further described in U.S. Patent Application Publication 2019 / 0175915, which is incorporated herein by reference.

[0027] The GUI 99 may also include a visualization interface 106 that allows a user to view a stimulation field image 112 formed on the leads given the selected stimulation parameters and electrode configuration. The stimulation field image 112 is formed by field modeling in the clinician programmer 70, as further discussed in the '091 disclosure. The visualization interface 106 preferably but not necessarily also includes tissue imaging information 114. The tissue imaging information 114 is represented in Figure 5A as Figure 6 three different tissue structures 114a, 114b, and 114c of the patient in question, for example, these tissue structures may include different regions of the brain. Such tissue imaging information may be from the patient's magnetic resonance image (MRI) or computed tomography (CT) image, any structural or functional imaging modality, it may be from a general image library, and may include user-defined regions. The GUI 99 may overlay the lead image 111 and the stimulation field image 112 with the tissue imaging information 114 in the visualization interface 106 such that the position of the stimulation field 112 relative to the various tissue structures 114i can be visualized. The various images shown in the visualization interface 106 (i.e., the lead image 111, the stimulation field image 112, and the tissue structures 114i) may be three-dimensional in nature and thus may be presented, such as by shading or coloring the image, to allow the user to better appreciate this three-dimensionality. The view adjustment interface 107 may allow the user to move or rotate the image using the cursor 101, for example, as described in the '091 disclosure. In Figure 5A a cross-sectional interface 108 allows the various images to be seen in a particular two-dimensional cross-section, in this example, the cross-section 109 is shown perpendicular to the lead image 111 and passing through the open-loop electrodes E2, E3, and E4. The interfaces 106 and 108 may also show the cathode 120 in the appropriate position, but it is not shown.

[0028] Figure 5AThe GUI 99 is particularly useful because it allows the electric field reflected in the stimulation field image 112 (or pole 120) to be seen relative to the surrounding tissue structure 114i. This allows the user to adjust the stimulation parameters to mobilize or avoid mobilizing a particular tissue structure 114i. For example, assume that a given patient wishes to stimulate tissue structure 114a but not tissue structures 114b or 114c. This may be because tissue structure 114a is causing an undesirable patient symptom (such as tremors) that the stimulation can relieve, while stimulation of tissue structures 114b and 114c will cause undesirable side effects. The clinician can then use the GUI 99 to adjust the stimulation (e.g., adjust the stimulation parameters or electrode configuration) to move the stimulation field 112 (e.g., the cathode 120) to the appropriate position (L, θ). In the example shown, as shown in the cross-sectional interface 108, higher cathode currents are provided at the open-loop electrodes E4 (0.52*-I) and E2 (0.18*-I) because these electrodes generally face the tissue structure 114a that should be stimulated. In contrast, the open-loop electrode E3 does not carry a cathode current because it generally faces the tissue structure 114b that ideally should be avoided being stimulated. The result is that the stimulation field 112 is more dominant in the tissue structure 114a and less dominant in the tissue structure 114b, as shown in the visualization interface 106.

[0029] Particularly in DBS applications, it is important to determine the correct stimulation parameters for a given patient. Inappropriate stimulation parameters may not effectively relieve the patient's symptoms or may cause unknown or unnecessary side effects. To determine the appropriate stimulation, clinicians typically use the GUI 99 to try different combinations of stimulation parameters. This may occur at least in part during the surgery of DBS patients when implanting the leads. Such intraoperative determination of stimulation parameters can be used to determine the overall efficacy of DBS treatment and confirm lead placement. However, the final stimulation parameters suitable for a given DBS patient typically occur after the patient has had the opportunity to recover after surgery and the position of the lead in the patient's body has stabilized. At this time, the patient typically goes to the clinician's office to determine (or further refine) the optimal stimulation parameters during a programming session.

[0030] DBS surgery typically involves first obtaining preoperative images of the patient's brain, such as by using a computed tomography (CT) scanner device, a magnetic resonance imaging (MRI) device, or any other imaging modality. This sometimes involves first attaching spherical or other fiducial markers visible on the images produced by the imaging modality to the patient's skull. The fiducial markers help register the preoperative images with the patient's actual body position in the operating room during subsequent surgical procedures.

[0031] After acquiring preoperative images by an imaging modality, they are loaded onto an image-guided surgical (IGS) workstation. Using the preoperative images displayed on the IGS workstation, a neurosurgeon can select a target region within the brain, an entry point on the patient's skull, and a desired trajectory between the entry point and the target region. The entry point and the trajectory are typically carefully selected to avoid crossing or otherwise damaging certain nearby critical brain structures or vasculature.

[0032] In the operating room, the patient is fixed, and the patient's actual body position is recorded, such as by using a remotely detectable IGS wand, into the preoperative images displayed on the IGS workstation. In one example, the doctor marks the entry point on the patient's skull, drills a burr hole at that location, and secures a trajectory guiding device around the burr hole. The trajectory guiding device includes a bore that can be aimed using the IGS wand to obtain the desired trajectory to reach the target region. After aiming, the trajectory guide is locked to maintain the aimed trajectory towards the target region. After locking the aimed trajectory using the trajectory guide, a surgical instrument is inserted along the trajectory towards the target region in the brain using a microdrive introducer. During electrode implantation, the trajectory can be refined (usually dynamically).

[0033] There is a need in the art for a method and system for assisting a clinician in determining a lead trajectory that is most likely to achieve a treatment goal or meet other criteria that the clinician deems important during the planning phase. Summary of the Invention

[0034] A method for planning the position of a stimulation lead for neuromodulation of one or more target structures in a patient's brain, where the stimulation lead includes a tip, a longitudinal axis, and a plurality of electrode contacts, the method includes: determining a plurality of candidate positions for the stimulation lead; determining a set of optimized stimulation parameters for each of the candidate positions; predicting the volume of tissue activated (VTA) for each of the set of optimized stimulation parameters at the candidate positions; determining the overlap of each of the predicted VTAs with the target structure and ranking the plurality of candidate positions at least in part based on the overlap. According to some embodiments, the method further includes implanting the stimulation lead into the patient's brain according to the highest ranked candidate position. According to some embodiments, each candidate position is defined by a tip position, a rotation angle, and a longitudinal axis angle. According to some embodiments, the indication of the plurality of candidate positions includes an indication of a reference position and an indication of one or more values of a tip position, a rotation angle, and / or a longitudinal axis angle. According to some embodiments, determining a set of optimized stimulation parameters includes using an inverse programming algorithm. According to some embodiments, the inverse programming algorithm includes optimizing the current decomposition between electrode contacts based on a stimulation field model (SFM) modeled for each current decomposition. According to some embodiments, the inverse programming algorithm includes optimizing one or more parameters selected from the group consisting of pulse width, frequency, and amplitude. According to some embodiments, the inverse programming algorithm includes a cost function that includes (i) the overlap of the SFM with the target structure for each current decomposition, and (ii) a cost associated with increasing the size of the SFM. According to some embodiments, the cost function is further (iii) a function of the overlap of the SFM with an avoidance structure for each current decomposition. According to some embodiments, ranking the plurality of candidate positions is further based on one or more boundary parameters or additional scoring functions. According to some embodiments, the boundary parameter includes maximum power usage. According to some embodiments, the boundary parameter specifies one or more of a stimulation amplitude value, a total charge value, a pulse width, or a frequency. According to some embodiments, the method further includes receiving a prior ranking for each of the candidate positions, where the ranking of the plurality of candidate positions is further based on the prior ranking.

[0035] The present disclosure also discloses an apparatus for planning the position of a stimulation lead for neurally stimulating one or more target structures in a patient's brain, wherein the stimulation lead includes a tip, a longitudinal axis, and a plurality of electrode contacts. The apparatus includes: a processor configured to: receive an indication of a plurality of candidate positions for the stimulation lead; determine a set of optimized stimulation parameters for each of the candidate positions; predict the volume of tissue activated (VTA) of the set of optimized stimulation parameters for each of the candidate positions; determine the overlap of each of the predicted VTAs with the target structure, and rank the plurality of candidate positions at least in part based on the overlap. According to some embodiments, each candidate position is defined by a tip position, a rotation angle, and a longitudinal axis angle. According to some embodiments, the indication of the plurality of candidate positions includes an indication of a reference position and an indication of the value of one or more of the tip position, rotation angle, and / or longitudinal axis angle. According to some embodiments, determining a set of optimized stimulation parameters includes using an inverse programming algorithm. According to some embodiments, the inverse programming algorithm includes optimizing the current decomposition between electrode contacts based on a stimulation field model (SFM) modeled for each current decomposition. According to some embodiments, the inverse programming algorithm includes optimizing one or more parameters selected from the group consisting of pulse width, frequency, and amplitude. According to some embodiments, the inverse programming algorithm includes a cost function that includes (i) the overlap of the SFM with the target structure for each current decomposition, and (ii) a cost associated with increasing the size of the SFM. According to some embodiments, the cost function is further a function of (iii) the overlap of the SFM with an avoidance structure for each current decomposition. According to some embodiments, ranking the plurality of candidate positions is further based on one or more boundary parameters. According to some embodiments, the boundary parameter includes maximum power usage. According to some embodiments, the boundary parameter specifies one or more of a stimulation amplitude value, a total charge value, a pulse width, or a frequency. According to some embodiments, the processor is further configured to receive a prior ranking for each of the candidate positions, wherein the ranking of the plurality of candidate positions is further based on the prior ranking.

[0036] The present disclosure also discloses a method for planning the position of a stimulation lead for neurally stimulating one or more target structures in a patient's brain, wherein the stimulation lead includes a tip, a longitudinal axis, and a plurality of electrode contacts. The method includes: receiving an indication of a plurality of candidate positions for the stimulation lead; determining a set of optimized stimulation parameters for each of the candidate positions; predicting a treatment effect for each of the candidate positions using the optimized stimulation parameters for the candidate position, and ranking the plurality of candidate positions at least in part based on the predicted treatment effect. According to some embodiments, the treatment effect includes the extent to which the optimized stimulation parameters of the candidate position will stimulate one or more target structures. According to some embodiments, the treatment effect includes the extent to which the optimized stimulation parameters of the candidate position will avoid stimulating one or more non-target structures.

[0037] The present disclosure also discloses an apparatus for planning the position of a stimulation lead for neurally stimulating one or more target structures in a patient's brain, where the stimulation lead includes a tip, a longitudinal axis, and a plurality of electrode contacts, and the apparatus includes: control circuitry configured to: receive indications of a plurality of candidate positions of the stimulation lead; determine, for each of the candidate positions, a set of optimized stimulation parameters; for each of the candidate positions, predict a treatment effect using the optimized stimulation parameters of the candidate position, and rank the plurality of candidate positions at least in part based on the predicted treatment effect. According to some embodiments, the treatment effect includes the extent to which the optimized stimulation parameters of the candidate position will stimulate one or more target structures. According to some embodiments, the treatment effect includes the extent to which the optimized stimulation parameters of the candidate position will avoid stimulating one or more non-target structures.

[0038] The invention may also reside in one or more non-transitory computer-readable memories including instructions that, when executed by a processor, configure the processor to perform any of the above methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] FIG. 1A shows an implantable pulse generator (IPG) according to the prior art. FIG. 1B shows a percutaneous lead having a ring electrode, and FIG. 1C shows a percutaneous lead having an open-ring electrode according to the prior art.

[0040] FIGS. 2A and 2B show examples of stimulation pulses (waveforms) that can be generated by an IPG according to the prior art.

[0041] FIG. 3 shows an example of a stimulation circuitry that can be used for an IPG according to the prior art.

[0042] FIG. 4 shows various external systems that can communicate with an IPG and program the stimulation in the IPG according to the prior art.

[0043] Figure 5A Shows a graphical user interface (GUI) that can be operated on an external system (such as a clinician programmer) that is capable of programming a stimulation program for the IPG. Figure 5B Shows by using Figure 5A the GUI-generated waveform at the electrode.

[0044] Figure 6 Shows an embodiment of how the trajectory of an electrode lead can be defined.

[0045] Figure 7 Shows an embodiment of a workflow for selecting a trajectory for a stimulation lead.

[0046] Figure 8A and 8B shows the trajectory of a stimulation lead in a contour anatomy.

[0047] Figure 9 shows a graphical user interface (GUI) for evaluating lead trajectories.

[0048] Figure 10 shows the calculated volume of tissue activation (VTA).

[0049] Figure 11 shows the ranking and weighting of candidate lead trajectories.

[0050] Figure 12 shows a machine configured to select a lead trajectory. DETAILED DESCRIPTION

[0051] As described above, before implanting a DBS lead into a patient's brain, a surgeon will first plan the trajectory of the lead. As used herein, the term "trajectory" refers to the position and orientation of one or more DBS leads, typically including the entry point on the brain surface to certain internal targets. Figure 6 shows information that can define the trajectory of lead 600. The trajectory of the lead can be defined by: (1) the position of the lead tip (e.g., x, y, z coordinates), (2) a longitudinal vector describing the axis of lead 600 extending from the tip towards the distal end of the lead, and (3) a rotation vector orthogonal to the longitudinal vector and describing the rotation of marker 602. Typically, a surgeon may attempt to implant the lead such that the marker faces a consistent direction (such as forward), but the lead may be deliberately rotated or rotated during the implantation process.

[0052] A surgeon can use preoperative imaging to determine the trajectory that the surgeon believes is most likely to result in successful therapeutic stimulation, while taking into account factors such as avoiding the vascular system, critical brain structures, etc. The challenge during the planning phase is that the only planning information available to the surgeon is anatomical information, such as imaging data. They do not know what type of stimulation program they can activate with any particular trajectory, the volume of tissue activation they may achieve, the stimulation fields they may obtain, etc. For example, a trajectory can be designed to position an electrode lead within or near a desired neural structure, but the doctor may still be unsure whether they can determine a stimulation program that will provide a stimulation field that adequately or optimally activates the desired neural target.

[0053] The present disclosure relates to methods and systems for assisting a surgeon / doctor in evaluating potential DBS lead trajectories during the planning phase of an implantation procedure and in selecting the trajectory most suitable for their needs. Figure 7 An overview of one embodiment of a workflow 700 as described herein is shown. Each of the components of the workflow will be discussed in more detail below. The workflow 700 can be executed with the assistance of one or more computer programs running on a machine, as described in more detail below. One or more machines can be configured to provide a GUI to assist in selecting and executing the steps of the workflow. These machines can be one or more, where processing and visualization may be separated, including portions on a handheld phone, tablet, laptop, desktop, and server, especially a cloud server, and can be accessed via a dedicated application (app, software) or via a web browser.

[0054] At step 702, a clinician can determine a plurality of candidate trajectories. According to some embodiments, the clinician can use a computer-implemented planning algorithm to plan the plurality of candidate trajectories. The planning algorithm can be configured to use preoperative imaging to derive patient-specific atlas data of the patient. Preoperative imaging can include computed x-ray tomography (CT), magnetic resonance tomography (MR), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound tomography (ultrasonography), etc. The planning algorithm can be configured to contour specific anatomical structures, such as the subthalamic nucleus (STN), the internal globus pallidus (GPI), and the ventral intermediate (VIM) nucleus of the thalamus. Examples of planning algorithms are described, for example, in U.S. Patent Nos. 10,249,041 and 11,020,004, the contents of which are incorporated herein by reference. An example of a commercial product that includes an algorithm for planning electrode lead trajectories is BRAINLAB ELEMENTS (Munich, Germany), which is provided with systems of the VERCISE deep brain stimulation family from Boston Scientific (Marborough, MA).

[0055] Additionally or alternatively, the system may determine multiple candidate trajectories. It may enhance or automate the process of determining trajectories by using priors (e.g., a set of trajectories previously used by a surgeon, or a set curated for that purpose). Alternatively, the system may use an optimization scheme to create, evaluate, and select a subset of trajectories from a family of trajectories using one or more starting trajectories. Trajectories meeting certain criteria (such as passing within a distance of a target structure and avoiding avoidance structures) may be selected or used to create additional options.

[0056] Figure 8 shows a contour representation of three anatomical structures (802a, 802b, and 802c) which, as described above, may be displayed on a graphical user interface (GUI) of a system configured for trajectory planning. In Figure 8A the user has selected three candidate trajectories (804a, 804b, and 804c) for the electrode lead 600. Figure 8B An embodiment is shown where the user may select multiple trajectories by selecting a reference trajectory 806 and then specifying the variance of the tip position as well as the longitudinal and rotational vectors (see Figure 6 ). For example, the user may specify that the tip position, rotational vector, and longitudinal vector each vary by ±10%. The system may be configured to select a given number of trajectories within the limits of the specified variance.

[0057] Referring again to Figure 7 , once multiple trajectories are hypothesized, the clinician may rank or weight each of the trajectories (step 703). For example, the weighting may be based on the clinician's experience and clinical judgment. Some embodiments of the workflow may not include the step of the clinician ranking / weighting the trajectories. The system may also label, mark, or highlight certain trajectories with useful information, or may sort the trajectories for the clinician to weight, or may rank or weight the trajectories and allow the clinician to confirm. The presence of prior surgeries, prior implant materials, and especially prior implanted DBS leads may affect the operation of the software and the weighting of the trajectories.

[0058] At step 704, the clinician may specify which one or more neural structures are to be activated during treatment and, if possible, which neural structures should not be activated. For example, referring to Figure 8A and / or 8B, the clinician may wish to provide stimulation that activates the anatomical structure 802b but avoids activating the structures 802a and 802c (referred to herein as "avoidance structures"). Figure 9An embodiment of a GUI 900 is shown having dropdown elements 902a and 902b by which a clinician can select, respectively, anatomical structures to be activated and avoided. These structures can be created from patient imaging by processes such as automatic segmentation, or from finely segmented anatomical structures, or created manually by the clinician, or can be target volumes imported from some other workflow. In some cases, the target has an anatomical basis and in other cases a physiological basis. The GUI can also include sliders 903a and 903b (or other GUI elements) for weighting the importance of the selected target / non-target structures. The GUI can also include a display 904 configured to display a representation of the relevant anatomical structures (e.g., 802a, 802b, and 802c; Figure 9 ). In the illustration, the display 904 also shows one of the candidate trajectories of the stimulation lead. The GUI 900 can also be configured to display a GUI element 906 by which the user can select boundary parameters or other desired parameters related to the stimulation, as will be discussed in more detail below.

[0059] Once the candidate trajectories (and their weights, if applicable) and the structures to be stimulated and / or avoided have been indicated, at step 706, the algorithm can determine the optimized stimulation parameters for each of the candidate trajectories. Also at step 706, the volume of tissue activated (VTA) is determined for one or more of the optimized stimulation parameters for each of the candidate trajectories. According to some embodiments, a reverse programming algorithm can be used to automatically select the optimized stimulation parameters. Given an indication of which target structures are to be stimulated and which target structures are to be avoided (i.e., avoidance structures), the reverse programming algorithm can use a stimulation field model (SFM) to optimize the stimulation parameters to provide a VTA that best overlaps with the desired structures and avoids the undesired structures. For the purposes of the present disclosure, the terms SFM and VTA are considered equivalent. Specifically, the algorithm determines how the current should be decomposed between the electrodes to provide the best VTA to preferentially stimulate the targets. Additional stimulation parameters can be considered, such as (but not limited to) amplitude, pulse width, pulse rate, pulse polarity, pulse type or mode. In some embodiments, multiple sets of stimulation parameters are searched and explored, and a subset is used to select the trajectories, and information about the selected settings is optionally presented to the clinician user, can be used to be exported into reports and other human and machine-readable formats, and can be used by the system when programming the stimulation device, including intraoperative test use and chronic treatment use. When there are multiple target structures, an additional weighting function can be employed. The system can also create a representation of the expected responses that can be used to verify the preferred trajectories during implantation, such as predicting the responses of partially or fully implanted leads, which include clinical responses to stimulation, including induced therapeutic and side effect responses to stimulation, and responses to recordable intracranial or other biopotential, such as LFP or evoked potentials that can be recorded when the leads are partially or fully implanted according to the preferred trajectories. The system can consider secondary treatment and side effect estimates, such as the secondary treatment effects available to address side effects caused by the primary treatment stimulation.

[0060] Algorithms for optimizing stimulation programs using SFM / VTA and patient-specific maps and imaging are described in U.S. Patent Nos. 11,344,732, 11,195,609, 9,411,935, 9,072,905, and 8,958,615, the contents of which are incorporated herein by reference. An example of a commercial algorithm for optimizing stimulation programs using VTA and patient-specific maps and imaging is Boston Scientific's Illumina 3-D algorithm (Boston Scientific, Valencia, CA, USA). It should be noted that the Illumina 3-D algorithm and the algorithms described in the attached references are generally used during and after surgery, i.e., after the lead has been implanted into the patient's brain. Once the lead has been implanted, postoperative imaging and these algorithms can be used to optimize the stimulation parameters based on the position of the implanted lead. In contrast, the present application uses a reverse programming algorithm to determine the VTA of potential candidate trajectories.

[0061] As described above, when the lead is in a candidate trajectory, one or more reverse programming algorithms can operate on each of the candidate trajectories to determine an optimized "breakdown" of the current of the electrodes. The breakdown can be expressed as a percentage of the total current supplied to each active electrode (see, for example, Figure 5A ). To generate the VTA associated with each breakdown, for example, a program such as COMSOL Multiphysics software (COMSOL, Inc., Burlington, MA, USA) is used to construct the electric field generated by the stimulation settings as a finite element model (FEM). As is known in the art, the lead body and neural tissue can be modeled. A multi-resolution mesh can be created to enclose the lead body and the surrounding tissue, with the highest resolution at the electrode-tissue interface and a higher resolution in the region of interest (ROI) around the electrode array than in the remaining volume. The scalar electric potential at the mesh nodes is calculated, and the model is solved once for each electrode at a unit current (1 mA).

[0062] The electric field results generated by the RoI can be interpolated onto a regular grid of model axons around the DBS lead. The response to each stimulus can be calculated by time scaling the potential along the axonal compartment using waveforms modeled based on stimulator recordings to estimate the threshold current ('Ith' in mA) for each axon in the grid to fire an action potential from a resting state. For example, a machine learning algorithm (Bootstrap aggregated random forest) can be trained on over 100 million axonal stimulations, which takes as input the features of the axonal voltage distribution and estimates the axonal response. The base file and the trained predictor can be integrated with an anatomical model of the patient. The output current amplitude threshold of the axonal model is an isosurface at the selected stimulation current amplitude. If desired, the resulting surface can be displayed as a VTA and overlaid with a representation of the patient's anatomical structure. Figure 10 An example of a VTA 1002 as described above is shown, corresponding to a particular set of stimulation parameters overlaid on a display of an anatomical structure.

[0063] Embodiments of the reverse programming algorithm may use a metric optimization algorithm, such as Bound Optimization by Quadratic Approximation (BOBQYA). The goal of the algorithm is to maximize stimulation of the target volume while maintaining clinician-specified constraints. The algorithm takes into account the cost of increasing VTA size, the cost of overlapping with avoidance volumes, including possible side effect areas, and stimulation safety limits.

[0064] For each decomposition, the optimizer's cost function or metric is the weighted sum of the stimulated volumes of each structure (a target and one or more avoidance regions) and the VTA (background volume). The target structure has a positive weight, and the avoidance structure and background have negative weights. The target and avoidance structures can take the form of a probability map so that certain parts of the structure can have a higher or lower calculated overlap score when the weights remain unchanged. In addition, in some embodiments, the weights can be unevenly distributed throughout the volume of each structure so that overlap with certain parts results in a higher or lower calculated overlap score than other parts. For each decomposition, the highest possible metric value is calculated and the corresponding amplitude is determined. The clinician can specify a target area, zero or more avoidance areas, a priority for not stimulating avoidance areas (controlled by a slider to set the "avoidance ratio"), and a priority for reducing the VTA volume (controlled by a slider to set the "background ratio"). Therefore, the equation for calculating the optimization metric is:

[0065] m=∑(v target ―(v avoidance *avoidance ratio (avoidance ratio) SFM*background ratio

[0066] Wherein:

[0067] m = measurement value,

[0068] v target = the stimulated target volume, in mm 3 ,

[0069] v avoidance = the stimulated avoidance volume, in mm 3 , and

[0070] v SFM = the total VTA volume, in mm3,

[0071] In short, this measurement is the stimulated target volume (in mm 3 ) minus the weighted sum of the total volume of the stimulated avoidance area (unit: mm 3 ) and the avoidance ratio, minus the weighted sum of the total volume of the background stimulation (unit: mm 3 ). Among them, the avoidance ratio is the ratio of the cost of stimulating the avoidance area (reduction of the measurement value) to the benefit of stimulating the target area (increase of the measurement value), and the background ratio is the ratio of the cost of stimulating the background volume to the benefit of stimulating the target area. The stimulated background volume is the same as the volume of the VTA.

[0072] The optimization algorithm can be run once for each of two types of virtual electrodes (see below) (one equivalent to a ring electrode on the lead wire, and the other equivalent to a segmented electrode on the lead wire, but can be placed and rotated arbitrarily). First, run the optimizer using the ring virtual electrode and determine the best solution. If the lead wire is directional, run the optimizer using the directional virtual electrode. When the optimization algorithm tests the position of each virtual electrode, this position is converted into a decomposition on the real electrode of the lead wire. For each decomposition, the best measurement among the possible amplitudes is compared with the measurement of the current best solution. If the new measurement is better than the previous best measurement, the new measurement, virtual electrode type, position, and derived amplitude are stored as the new best solution. When the optimization algorithm meets the stop condition, the best solution is returned and displayed to the clinician.

[0073] According to some embodiments, the virtual electrode is an annular (e.g., 1.5 mm high, 360 degrees around the lead) or a directional (e.g., 1.5 mm high, 90 degrees around the lead) electrode, which is modeled as the only electrode on an infinitely long lead for calculating the voltage field of the electrode, with its nominal lead diameter and material being the same as those of the real lead. The voltage field of the virtual electrode rotates around the axis of the lead and translates along the axis of the lead to model the placement of the virtual electrode at an arbitrary position along and around the effective length of the lead. Least squares fitting is used to determine the decomposition on the real electrode that will produce the best fit between the voltage field generated by the real electrode on the real lead and the voltage field of the virtual electrode placed at the selected position.

[0074] According to other embodiments, the algorithm can involve a "brute force" search for the best stimulation parameters for each of the candidate trajectories, rather than using a reverse programming algorithm. In other words, for each of the candidate trajectories, the clinician can try a series of decompositions to determine which ones best overlap with the desired anatomical target.

[0075] Referring again to workflow 700( Figure 7 ), the above discussion explains how to determine the optimized parameter stimulation parameters and the overlap of the VTA with the target anatomy for each of the candidate trajectories that can be determined. Step 708 involves ranking and presenting the candidate trajectories based on the VTA (unoptimized stimulation parameters) of the candidate trajectories and their prior clinician weighting (if available). According to some embodiments, the algorithm includes a search engine configured to rank and weight the candidate trajectories based on these criteria.

[0076] Figure 11 An example of ranking / weighting is shown. Assume that the clinician has envisioned three candidate trajectories - 804a, 804b, and 804c, as shown in list 1102. It is also assumed that the clinician has weighted each of the trajectories, as Figure 11 shown. This weighting indicates that the clinician believes that trajectory 804a will be the best trajectory. The clinician's belief can be based on experience, historical data, published data, etc. For example, it is known that a given trajectory can best avoid the vasculature, ventricles, etc. As mentioned above, some embodiments may not include the step of receiving the clinician's weighting. In some embodiments, the clinician can weight or prioritize the considerations that the algorithm will use when finding, sorting, ranking, and scoring the trajectories. For example, the clinician may highly weight the "avoidance of vascular effects" for a given patient and lowly weight its "avoidance of induced cognitive decline".

[0077] As mentioned above, the algorithm determines the best stimulation parameters for each of the candidate trajectories( Figure 7Step 706) in. The optimal stimulation parameters are informed by which anatomical features should be stimulated and which anatomical features should be avoided as specified by the clinician. The algorithm also determines each achievable VTA in the candidate trajectories based on the optimized stimulation parameters. Once the VTA is determined for each trajectory, the algorithm can apply a search engine and / or a ranking routine to rank the candidate trajectories based on which trajectories provide the best VTA while considering the clinician's prior ranking (if available). In the example shown, as shown in List 1104, the search engine determines that trajectory 804b should be ranked highest. Note that this determination is different from the determination selected by the clinician as the first option.

[0078] According to some embodiments, the ranking algorithm can consider other criteria, constraints, boundary parameters, etc. (collectively referred to herein as "boundary parameters"). Examples of boundary parameters can relate to power usage, stimulation amplitude, total charge, pulse width, frequency, or the impact on the patient (such as the risk of inducing side effects, etc.). For example, in some cases, the clinician may wish to use the least amount of energy to obtain the greatest benefit. In such a case, the clinician can select boundary parameters based on the energy usage. For example, the algorithm can be configured to relatively heavily weight the energy usage. In other cases, the clinician may not care about the energy usage and may just want to use the trajectory that provides the best VTA overlap regardless of the energy usage. In another case, the clinician may wish to set boundary parameters such that the stimulation does not exceed a predefined amplitude. Other examples of boundary parameters will be apparent to those skilled in the art.

[0079] As described above, the GUI 900 ( Figure 9 ) can include GUI elements, such as element 906 for selecting various boundary parameters. According to some embodiments, the GUI element can include a slider 908 (or some other element) to rank the importance (i.e., weighting) of the selected boundary parameter. For example, in Figure 9 the embodiment shown, the user has selected "total charge" as a boundary parameter and does not want the charge to exceed 300 coulombs. The user can use the slider 908 to set how the algorithm weights the total charge boundary parameter. When a boundary parameter is selected, one or more parameter optimization algorithms can be constrained to only consider stimulation parameters (and corresponding VTAs) within the bounded domain. Alternatively (or additionally), the search engine can weight the candidate trajectories based on the selected boundary parameters (i.e., give a favorable weighting to the trajectories that provide an available VTA while conforming to the boundary parameters).

[0080] Figure 12A block diagram of an example machine 1200 is shown generally, on which any one or more of the techniques (e.g., algorithms, methods, etc.) discussed herein can be executed. In alternative embodiments, machine 1200 can operate as a stand-alone device or can be connected (e.g., networked) to other machines. In a networked deployment, machine 1200 can operate in a server-client network environment as a server machine, a client machine, or both. In an example, machine 1200 can act as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. Machine 1700 can be a personal computer (PC), a tablet computer, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a network device, a network router, a switch or bridge, or any machine capable of executing instructions (sequential or otherwise) specifying actions to be taken by that machine. Further, while only a single machine is shown, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

[0081] As described herein, an example can include or be operated on by logic or a number of components or mechanisms. A circuit set is a collection of circuits implemented in a tangible entity, including hardware (e.g., simple circuits, gates, logic, etc.). Circuit set members may become flexible over time and underlying hardware changes. A circuit set includes components that can individually or in combination perform particular operations when operating. In an example, the hardware of a circuit set can be immutably designed to perform a particular operation (e.g., hardwired), in an example, the hardware of a circuit set can include physically coupled components (e.g., execution units, transistors, simple circuits, etc.), including physically modified computer-readable media (e.g., magnetic, electrical, movable placement of invariant aggregating particles, etc.) that encode instructions for a particular operation. When physically coupling the components, the underlying electrical characteristics of the hardware components change, e.g., from an insulator to a conductor and vice versa. Instructions cause the embedded hardware (e.g., execution units or load mechanisms) to create components of a circuit set in the hardware via variable connections to perform a portion of a particular operation when operating. Thus, when the device is operating, the computer-readable media is communicatively coupled to other components of the circuit set components. In an example, any physical component can be used in more than one component of more than one circuit set. For example, under operation, an execution unit can be used in a first circuit of a first circuit set at one point in time and reused by a second circuit in the first circuit set or a third circuit in a second circuit set at a different time.

[0082] A machine (e.g., a computer system) 1200 may include a hardware processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 1204, and a static memory 1206, some or all of which may communicate with each other via an interconnect (e.g., a bus) 1208. The machine 1200 may also include a display unit 1210 (e.g., a raster display, a vector display, a holographic display, etc.), an alphanumeric input device 1212 (e.g., a keyboard), and a user interface (UI) navigation device 1214 (e.g., a mouse). In an example, the display unit 1210, the input device 1212, and the UI navigation device 1214 may be a touch screen display. The machine 1200 may also include a storage device (e.g., a drive unit) 1216, a signal generation device 1218 (e.g., a speaker), a network interface device 1220, and one or more sensors 1221, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. The machine 1200 may include an output controller 1228, such as a serial (e.g., a universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection, to communicate with or control one or more peripheral devices (e.g., a printer, a card reader, etc.).

[0083] The storage device 1216 may include a machine-readable medium 1222 having stored thereon a set or sets of data structures or instructions 1224 (e.g., software) embodying or used by any one or more of the techniques or functions described herein. During execution of the instructions 1224 by the machine 1700, the instructions 1224 may also reside, completely or at least partially, within the main memory 1204, within the static memory 1206, or within the hardware processor 1202. In an example, one or any combination of the hardware processor 1202, the main memory 1204, the static memory 1206, or the storage device 1216 may constitute a machine-readable medium.

[0084] Although the machine-readable medium 1222 is shown as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) configured to store one or more instructions 1224. The term "machine-readable medium" can include any medium that is capable of storing, encoding, or carrying instructions executable by the machine 1200 and causing the machine 1200 to perform any one or more of the techniques of the present disclosure, or any medium that is capable of storing, encrypting, or carrying data structures used by or associated with these instructions. Non-limiting examples of machine-readable media can include solid-state memory as well as optical and magnetic media. In an example, an aggregated machine-readable medium includes a machine-readable medium having a plurality of particles with invariant (e.g., stationary) mass. Thus, an aggregated computer-readable medium is not a transient propagated signal. Specific examples of aggregated machine-readable media can include: non-volatile memory such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0085] The instructions 1224 can also be sent or received over the communication network 1726 via the network interface device 1220 using a variety of transmission protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks can include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile telephone networks (e.g., cellular networks), plain old telephone (POTS) networks, and wireless data networks (e.g., the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard family known as and the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard family known as the IEEE 802.16 standard family), the IEEE 802.15.4 standard family, peer-to-peer (P2P) networks, etc. In an example, the network interface device 1220 may include one or more physical jacks (e.g., Ethernet, coaxial cable, or telephone jacks) or one or more antennas to connect to the communication network 1226. In an example, the network interface device 1220 may include multiple antennas to perform wireless communication using at least one of single-input multiple-output (SINR), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term "transmission medium" shall be regarded as including any non-tangible medium capable of storing, encoding, or carrying instructions executed by the machine 1200, and including digital or analog communication signals or other non-tangible media to facilitate the communication of such software.

[0086] Although specific embodiments of the present invention have been shown and described, the above discussion is not intended to limit the present invention to these embodiments. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the present invention. Accordingly, the present invention is intended to cover alternatives, modifications, and equivalents that may fall within the spirit and scope of the present invention as defined by the claims.

Claims

1. An apparatus for planning the position of a stimulation lead for neurally stimulating one or more target structures in a patient's brain, wherein the stimulation lead includes a tip, a longitudinal axis, and a plurality of electrode contacts, the apparatus comprises: a processor configured to: receive an indication of a plurality of candidate positions for the stimulation lead; determine a set of optimized stimulation parameters for each of the candidate positions; predict an activated tissue volume (VTA) for the set of optimized stimulation parameters for each of the candidate positions; determine the overlap of each of the predicted VTAs with the target structure, and rank the plurality of candidate positions at least in part based on the overlap.

2. The apparatus according to claim 1, wherein each candidate position is defined by a tip position, a rotation angle, and a longitudinal axis angle.

3. The apparatus according to claim 2, wherein, the indication of the plurality of candidate positions includes an indication of a reference position and an indication of the value of one or more of the tip position, the rotation angle, and / or the longitudinal axis angle.

4. The apparatus according to any one of claims 1-3, wherein determining a set of optimized stimulation parameters includes using an inverse programming algorithm.

5. The apparatus according to claim 4, wherein the inverse programming algorithm includes optimizing the current decomposition between the electrode contacts based on a stimulation field model (SFM) modeled for each current decomposition.

6. The apparatus according to claim 5, wherein the inverse programming algorithm includes a cost function that includes (i) the overlap of the SFM with the target structure for each current decomposition, and (ii) a cost associated with increasing the SFM size.

7. The apparatus according to claim 6, wherein the cost function is further a function of (iii) the overlap of the SFM with an avoidance structure for each current decomposition.

8. The apparatus according to any one of claims 1-7, wherein, ranking the plurality of candidate positions is further based on one or more boundary parameters.

9. The apparatus according to claim 8, wherein the boundary parameter includes maximum power usage.

10. The apparatus according to claim 8, wherein the boundary parameter specifies one or more of a stimulation amplitude value, a total charge value, a pulse width, or a frequency.

11. The apparatus according to any one of claims 1-10, wherein, the processor is further configured to receive a prior ranking for each of the candidate positions, wherein ranking the plurality of candidate positions is further based on the prior ranking.

12. An apparatus for planning the position of a stimulation lead for neurally stimulating one or more target structures in a patient's brain, wherein the stimulation lead includes a tip, a longitudinal axis, and a plurality of electrode contacts, the apparatus comprises: a control circuit configured to: receive an indication of a plurality of candidate positions for the stimulation lead; determine a set of optimized stimulation parameters for each of the candidate positions; for each of the candidate positions, predict a treatment effect using the optimized stimulation parameters for that candidate position, and Rank the plurality of candidate locations based at least in part on the predicted treatment effect.

13. The apparatus of claim 12, wherein the treatment effect includes the degree to which the optimized stimulation parameters at the candidate location will stimulate the one or more target structures.

14. The apparatus of claim 12, wherein the treatment effect includes the degree to which the optimized stimulation parameters at the candidate location will avoid stimulating one or more non-target structures.

15. A non-transitory computer-readable medium comprising instructions that, when executed by a processor of a computing device, configure the computing device to: Receive an indication of a plurality of candidate locations for the stimulation lead; Determine a set of optimized stimulation parameters for each of the candidate locations; Predict an activation volume of tissue (VTA) for a set of optimized stimulation parameters for each of the candidate locations; Determine the overlap of each of the predicted VTAs with the target structure and rank the plurality of candidate locations based at least in part on the overlap.

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