Patent Application
20150246231
The invention relates to a medical system, in particular medical device, and method for planning and/or tuning a neuromodulation therapy, in particular deep brain stimulation.
Deep brain stimulation is an established therapy for treatment of movement disorders such as essential tremor, Parkinson's disease, and dystonia, and other neurological disorders. A typical system for applying deep brain stimulation comprises an implantable neurostimulation probe having at least one electrode which is adapted to generate an electrical stimulation field. The stimulation field is applied to selected brain regions in order to directly change brain activity in a controlled manner. The location and distribution of the stimulation field influences the therapeutic effect.
During implantation of the probe, the physician or surgeon recognizes by means of immediate patient feedback to the stimulation, such as reduction or increase of local tremor, whether the location of the probe results in a desired effect or undesired side-effect. As such, know methods for planning the implantation of the neuromodulation probe or adapting electrical stimulation parameters are primarily based upon a trial and error approach which, however, disregards delayed feedback or clinical outcome. Particularly, common methods of planning a neuromodulation therapy do not include considering long-term effects or side-effects like impaired speech (dysarthria) or introduction of hypomania which may develop over the time. Thus, programming of a deep brain stimulation system is performed without any direct feedback which allows for an assessment as to whether the adapted stimulation field produces long-term beneficial effects and/or avoids long-term side-effects. Same applies for tuning a neuromodulation therapy, i.e. for developing an optimal stimulation field distribution for a specific patient.
It is an object of the present invention to provide for a medical system and a method for planning and/or tuning a neuromodulation therapy which aids to adapt a location and/or distribution of a neuromodulation probe's stimulation field such that short-term as well as long-term side effects are reduced or avoided.
This object is solved according the present invention by a system as claimed in claim 1 and a method as claimed in claim 9.
In particular, this object is solved by a medical system, in particular medical device, for planning and/or tuning a neuromodulation therapy, the system comprising:
The invention is based upon the idea to use a database means for storing statistical data to determine stimulation regions which are expected to show a certain clinical outcome (positive or negative). The database means may contain statistical data of patients which have been treated with a neuromodulation probe, in particular a neuromodulation probe of a deep brain stimulation system. This data may be used to generate dynamic functional brain atlas (dFBA). E.g. the dFBA uses a certain amount of data sets, each data set representing a specific patient of a group of patients which have been treated with the neuromodulation probe. Each data set may comprise meta data of a specific patient and/or patient group and spatial data about the distribution of a stimulation field which has been generated during therapy of said patient and/or patient group. The meta data may also include information about the clinical outcome of each patient.
Each data set may also include spatial data related to the stimulation field applied to the specific patient which provided the meta data of said data set. The distribution of the stimulation field may be determined by setting up a coordinate system, preferably a Cartesian coordinate system, aligned to the probe and defining a probe grid. Each point of the probe grid which includes spatial data related to the stimulation field defines the volume of the stimulation field. Spatial data may be assigned only to probe grid points which are covered by the stimulation field. Hence, only spatial data which has been assigned to probe grid points, i.e. which represent probe grid points covered by the stimulation field, may be combined with meta data in a data set.
By filtering the data sets, a filtered subset of the data sets is generated which includes data sets of patients matching the entered filter criterion or criteria. Thus, the invention allows for a user, e.g. a physician, in particular a neurologist, to identify specific criteria for including or excluding data sets that may or may not be of interest.
The merging means may allow for computing average scores related to clinical outcome on the basis of a filtered subset of data sets. The filtered subset of data sets may include data sets of different patients matching the selected filter criterion or criteria. For example, a filter criterion may be applied to the data sets, in particular the meta data of the data sets, which generate a filtered subset of the data sets wherein the filter criterion relates to the age of a patient and the clinical outcome of that patient. Each data set also contains spatial data of the stimulation field applied to the respective patient. The meta data of a specific data set is assigned to all spatial data values. In other words each point of the probe grid or a main grid, as explained thereinafter, may be assigned the same meta data. By merging a specific data value, e.g. a data value relating to the clinical outcome, of the data sets included in the filtered subset, the system can provide an assessment based on the statistical data of the dFBA as to whether stimulation of a specific region has beneficial effects or undesired side-effects.
The present invention advantageously allows for guidance during surgical planning of a neuromodulation therapy, in particular of probe implantation, intra-operative test stimulation, and postoperative stimulation tuning.
Surgical planning may be aided by at least one three dimensional model/object representing a functional map related to best clinical outcome together with at least one functional map displaying regions that are likely to produce stimulation-induced side-effects. These functional maps may be used to identify anatomical target areas where stimulation will reduce the symptoms of the disease at the same time as side-effects are avoided. Thus, the access point for implanting the neuromodulation probe, the angle of the neuromodulation probe in relation to a body axis or body plane, in particular to the AC-PC (anterior commissure-posterior commissure) plane, and/or the stimulation parameters, e.g. stimulation current, stimulation amplitude and/or stimulation frequency, may be determined by means of the visualization of the target area, preferably the functional map (three dimensional model). The stimulation parameters may be controlled per each electrode of an electrode array attached to the probe, thereby adjusting the distribution of the stimulation field.
In addition, the three dimensional model/object which is computed on the basis of the functional data and which represents a selected functional map may visualize data of local field recordings or other recorded data. The functional map may be displayed together with at least one image of patient-specific anatomy. Displaying local field recordings or other recorded data may also be useful for identifying optimal stimulation areas or, respectively, stimulation targets.
Functional maps will further aid a user, e.g. a physician, in particular a neurologist, during intra-operative test stimulation by providing information regarding the location of the neuromodulation probe. The location of the probe may be derived from immediate feedback of stimulation-induced effects and side-effects together with the functional maps relating to stimulation-induced effects and side-effects observed in a filtered subset of data sets, i.e. relating to stimulation-induced effects and side-effects observed in a selected comparison group of patients.
Moreover, the location of the neuromodulation probe may possibly be derived from recorded local field potentials that are compared with at least one three dimensional model/object, i.e. at least one functional map, displaying previously recorded local field potentials.
The postoperative stimulation tuning may also be aided by functional maps in order to tune the stimulation field towards areas that are likely to produce beneficial effects without side-effects. The dFBA contains information regarding delayed effects and side-effects that may not be immediately tested during tuning. Thus, functional maps may therefore be used to avoid not only immediate, but also delayed, undesired clinical effects, or to alleviate, preferably to avoid, symptoms on which neurostimulation has a delayed effect.
The functional maps or, respectively, the functional data set which forms the basis of the three dimensional model/object representing the functional map, may be generated dynamically. In other words, based on the entered input data, a specific subset of data sets from the database, herein referred to as dFBA, may be filtered, merged and visualized as an image of the three dimensional model/object. Because different filter criteria may be entered, the inventive system provides for a dynamic generation and visualization of the functional data, i.e. functional maps, which represent the functional data merged from the filtered subset of data sets. Dynamically created functional maps of e.g. stimulation fields related to “Best clinical outcome” together with maps of e.g. local field potentials may be an important tool not only for tuning but also for understanding the underlying mechanisms of neuromodulation therapy, in particular deep brain stimulation, which is essential for a more widespread use of said kind of therapy in different neurological disorders.
It is preferred that the system comprises registration means for mapping the three dimensional model of the functional data set on at least one patient-specific medical image, in particular a magnetic resonance image, whereby the visualization means is adapted to visualize an image of the three dimensional model together with the patient-specific medical image. In other words, the functional data set generated from the filtered subset of data can be displayed in relation to a patient-specific medical image, thereby, aiding to determine a location and/or distribution of the stimulation field which has been developed as having a beneficial therapeutic effect and avoiding undesired side-effects. The three dimensional model of the functional data substantially represents a specific functional map wherein the geometry of the functional map depends on the filter criterion applied to the plurality of data sets, i.e. the dFBA. The physician may enter the filter criterion and, thus, decide upon the geometry of the three dimensional model, i.e. the functional map, to be visualized or, respectively, displayed. The three dimensional model may be displayed in 2D or 3D together with the patient-specific medical image.
In a preferred embodiment, the spatial data comprises a plurality of data values, each data value being assigned to a specific location in a main grid, preferably a blank Cartesian main grid. Moreover, the meta data may be mapped to the main grid. Additionally, the volume of the stimulation field may be transformed and/or interpolated from the probe grid points to the main grid points.
The main grid may represent a graphical visualization of the database, herein referred to as dFBA, containing the plurality of data sets. Each point of the meain grid is represented in the database by a spatial data value. Preferably, the main grid is directly mapped onto patient-specific medical images, e.g. magnetic resonance images (MRI) or computer tomographic images, instead of an anatomical atlas. Thus, the user is allowed to identify the anatomical structures.
Further, the merging means may be adapted to calculate an average score of each data value of the filtered subset which is assigned to the same location in the main grid. The data value may comprise information about the clinical outcome of a neuromodulation therapy.
Thereby, a three dimensional model (functional map) can be generated and visualized which shows stimulation regions which have been developed as having beneficial effects for a certain group of patients relating to the filtered subset of data sets.
The physician may select a certain group of patient data contained in the database which match the entered filter criterion. The average of the data values of the filtered subset of data sets related to clinical outcome at a specific stimulation location, in particular a specific point of the main grid, may then be calculated by the merging means. The points of the main grid which have an averaged data value above a certain threshold may be visualized to display the three dimensional model. Thus, for example a three dimensional model or, respectively, functional map may be visualized together with a patient-specific medical image which corresponds to averaged data values representing the best clinical outcome.
The data values of the filtered subset of data sets which are mapped to a specific location in the main grid and of which an averaged data value may be calculated, may comprise a score which is assigned to the spatial data after clinical assessment. The score may consider effects of a neuromodulation therapy according to keywords such as “best”, “good”, “moderate”, “bad” and “worst”. Side-effects may be scored by keywords like “intolerable”, “severe”, “some” and “minor”. The data value relating to clinical outcome may be represented by a number ranging from “0” to “4”, each number being assigned to a first keyword relating to effects and a second keyword relating to side-effects.
Additionally, the spatial data may be represented by a matrix, in particular a three dimensional matrix. In order to allow for a high resolution, it is preferred that for each point in the main grid the distance to its immediate neighbour points is not exceeding 1 mm, particularly 0.75 mm, particularly 0.5 mm, particularly 0.25 mm, particularly 0.1 mm.
The filter means may be adapted to receive at least one Boolean expression related to the meta data. Additionally, the filtered subset of data sets may be generated at least partially based on the Boolean expression. Thus, the functional map represented by the three dimensional model is easily selectable by the physician who enters the respective filter criterion.
In a further preferred embodiment, the system comprises a simulation means for simulating a stimulation field of the neuromodulation probe, wherein the visualization means is adapted to visualize the stimulation field in relation to the three dimensional model of the functional data set. The visualization of the stimulation field aids the physician to determine the stimulation parameters which control the distribution of the stimulation field. The simulation parameters may be controlled such that the three dimensional model/object, e.g. representing a functional map for best clinical outcome of a specific group of patients, is best possible covered by the stimulation field.
A further aspect of the present invention concerns a method for planning and/or tuning a neuromodulation therapy, the method comprising:
The system, in particular device, according to the present invention may comprise a workstation on which at least one of the database means, the input means, the filter means, the merging means, the visualization means, the registration means and the simulation means are installed. The system/device may further comprise a screen, in particular a flat screen and/or touch screen, for displaying the image of the three dimensional model, preferably together with the patient-specific medical image.
The system/device may be embodied as a computer program product.
The system and/or method according to the present invention are advantageously adapted for programming a deep brain stimulation system, in particular a deep brain stimulation system as described in detail hereinafter.
Further details and advantages of the present invention shall be described hereinafter with respect to the drawings:
Deep brain stimulation (DBS) is an established therapy for treatment of movement disorders such as essential tremor, Parkinson's disease (PD), and dystonia, and is being used on an experimental level for a range of other neurological disorders. However, on a detailed level there is no consensus regarding optimal stimulation targets for any of these disorders. It is not clear if one optimal target exists for each of these neurological disorders or if there are multiple targets areas that regulate various symptoms of each disease. Thus, planning and programming of DBS is complicated and to some degree experimental also for treatment of diseases that are approved by the Food and Drug Administration. In order to improve and facilitate the planning, intra-operative stimulation/recordings, and postoperative programming of DBS, a medical system/device for planning and/or running a neuromodulation therapy, e.g. image-guided support systems 10, may serve as a valuable tool.
Such an image guided support system 10 could be included into a neuromodulation/neurostimulation system 100. As an alternative a respective system could be provided as a separated unit.
One embodiment of a neurostimulation system 100 for deep brain stimulation (DBS) is shown in
In the embodiment of
The data set k could be modelled as a vector with the elements {vD, D}. Accordingly, k′ could be a vector with the element {vD′, D′} and k″ with the elements {vD″, D″}.
Thus, each data set k, k′, k″ comprises specific meta data, e.g. the first meta data vector vD, the second meta data vector vD′ and the third meta data vector vD″, and spatial data, e.g. the first data matrix D, the second data matrix D′ and the third data matrix D″. The meta data vectors vD, vD′, vD″ might contain information related to the patients anatomy, tissue fibre structures, patient demographics, local field potential recordings (LFP), microelectrode recordings (MER), (side-)effects, treated neurological disorders, symptoms, treatment parameters and/or clinical evaluations.
The exemplary first meta data vector vD might contain the values {male, 60, positive, PD} which might indicate that a male patient aged 60 having the Parkinson's disease has been treated with deep brain simulation whereby the overall clinical outcome is considered positive. Similarly, the second meta data vector vD′ might contain the values {female, 55, positive, PD} to indicate that a female patient aged 55 having Parkinson's disease has been treated with a positive clinical outcome. The third meta data vector vD″ might have the values {female, 50, negative, PD} to indicate that a female patient aged 50 having Parkinson's disease has been treated with a negative clinical outcome.
The spatial data might be modelled in spatial data matrix D having a plurality of data values dx,y,z. It is obvious that respective data values dx,y,z, can be mapped on a Cartesian coordinate system.
In one embodiment, the spatial data matrix D will hold data values dx,y,z=0 or dx,y,z=1, wherein the value 1 indicates that within a particular deep brain stimulation session, a particular region has been stimulated and wherein 0 indicates that the respective region within the main grid 30 has not been stimulated. Thus, the spatial data matrix D might encode a three dimensional model/object of a respective stimulation field. Similarly, the other spatial data matrices D′, D″ might encode a three dimensional model of a different stimulation field used with e.g. different patients.
In one embodiment, each data set k, k′, k″ might represent one treatment of a particular patient, wherein the stimulated region of the brain and numerous information about the patient, the outcome of the treatment and treatment itself are recorded. It is an objective of the present invention to visualize respective results such that they can be used for planning and/or tuning a neuromodulation therapy. In other words, the data sets k, k′, k″ provide data such that the dFBA can generate different functional maps which can be used for planning and/or tuning a neuromodulation therapy.
Preferably, the functional maps of the dFBA are shown together with images representing the anatomy of a patient to be treated. For high quality results, the main grid 30 should be registered (e.g. scaled and aligned) with the patient's specific medical images, as shown in
Concerning this, several approaches are available. A first approach uses a template medical image data set. The main grid 30 is first (before any functional data is mapped into the main grid) registered with the template images by aligning the main grid with the AC-PC plane, and placing the origin 35 at the midcommissural point (MCP) i.e. at a point in the middle between the AC and PC. Thus, the AC and PC must be identified in the template images, together with the mid-sagittal plane. After this one time procedure, the main grid 30 is registered to patient-specific anatomy simply by registering the patient-specific images with the template image data set. The derived transformation matrix is then applied to the main grid 30. In order to get an accurate and precise registration, elastic registration can be used where the algorithms are adjusted in order to optimize the registration of the target structures where the DBS electrodes are located.
A second registration approach may immediately align the main grid 30 with the anatomy of the patient. In this approach the main grid 30 should be aligned with the patient's AC-PC plane. Thus, the AC and PC must be identified in the patient-specific images, together with the mid-sagittal plane.
Anatomical landmarks might be used to improve either one of the approaches. One landmark for defining the subthalamic nuclues (STN) is the posterior point of the STN at the level of the centre of the red nucleus. Another landmark that could be used as origin is the centre of the STN at the level of the centre of the red nucleus. Finding the landmarks and marking them may be performed manually by the user. After the landmarks have been selected they can be mapped to the corresponding landmarks registered in the main grid 35.
Once the main grid 30 has been registered, it is possible to visualize the information contained in the data sets k, k′, k″ as shown in the
The filter criterion or criteria can be expressed by Boolean operators. For example, only those data sets k, k′, k″ might be relevant where the Parkinson's disease was treated and the clinical outcome was considered positive. The respective Boolean expression could read as follows: “disease=‘PD’ AND clinical outcome=‘positive’”.
With the meta data vectors vD={male, 60, positive, PD}, vD′={female, 55, positive, PD} and vD″ {female, 50, negative, PD}, only the first data set k and the second data set k′ match the respective filter, i.e. the abovementioned Boolean expression. Thus, only these data sets k, k′ will be used to generate a functional map. E.g. the corresponding first and second spatial data matrix D, D′ will be added whereby only those points within the main grid 30 will be considered worth treating where the resulting value equals 2. With this result it is very simple to generate a three dimensional object/model of a preferred treatment volume and to visualize the respective three dimension model as shown in
Preferably, the image guided support system 10 also provides a simulation of a probe 21 for DBS (
For doing so, a simulated stimulation field 70 of the probe 21 might be generated within a probe grid 20, e.g.
In another embodiment the functional map could include e.g. patients older than 60 years old, who had DBS surgery performed more than 3 years ago, suffering from ‘intolerable’, ‘severe’ or ‘minor’ dysarthria. The image guided support system 10 would then display the preferred target stimulation field 50 for this patient group.
Another example would be to show a functional stimulation map of all patient with best overall clinical outcome (e.g. UPDRS: Unified Parkinson Disease Rating Scale) stimulated with an amplitude below e.g. 0.3 mA. A third example would be to visualize a functional map of recorded frequencies during local field potential (LFP) recordings, together with maps that display the best overall clinical outcome. The combination of such functional data into one common map may provide information on how to use LFP for finding the optimal location for stimulation in a specific neurological disorder. In addition, functional maps displaying delayed effects of DBS may be visualized in order to avoid stimulation of certain areas in the brain that are likely to produce such effects, or the inverse, to stimulate areas that are likely to induce delayed beneficial effects.
If a user of the image guided support system 10 is interested in looking at a functional map regarding stimulation-induced diplopia (double vision), then the image guided support system 10 will calculate the scores for diplopia at each grid point of the main grid 30, and then visualize an isosurface that include the grid points that have a diplopia score. The threshold for the isosurface i.e. the severity of diploplia may be adjusted by the user in order to display a map of various severities e.g. only the ‘Severe’ and ‘Intolerable’ cases. If a user is only interested in looking at a subgroup of these patients such as only patients with an age above 75, who suffers from hypertension, patients will be excluded who does not meet the Boolean expression during the calculation of the data at the grid points of the main grid 30.
Generally, the steps performed by the image guided support system might be summarized as follows:
1) Registering the main grid 30 onto patient specific anatomy;
2) Receiving Boolean expressions from the user interface to select/filter the data that is included in the main grid 30 in various ways by means of the functional spatial maps, i.e. maps that visualize the data together with patient-specific anatomy;
3) Executing mathematical and/or statistical calculations at each point of the main grid 30, e.g. summing the severity of a certain side-effect and then calculating the severity average of this side effect;
4) Visualizing node points within the main grid 30, wherein the node points have a side-effect average severity above a certain threshold;
5) Providing a 3D isosurface over the selected node points.
In the above described embodiment, the database containing the plurality of data sets k, k′, k″ is stored in the memory 13 of the image guided support system 10. However, in an alternative embodiment, the database may be hosted on any external device. Thus, the database storing the plurality of data sets k, k′, k″ does not necessarily have to be part of the device. The database may be in any case part of the whole system, irrespective whether it is embodied in a single device or an arrangement of two or more devices. For example, the system may comprise a computing device, which includes the database means, the input means, the filter means and/or the merging means and which creates the functional maps, i.e. the three dimensional model, and a separated visualization device including the visualization means and being adapted to receive and display the functional maps created in the computing device.
| Number | Date | Country | Kind |
|---|---|---|---|
| 12154506.5 | Feb 2012 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2013/052457 | 2/7/2013 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 61596299 | Feb 2012 | US |
