METHOD FOR DETERMINING AN ADAPTATION OF AN IMAGING EXAMINATION

Abstract

A computer-implemented method for determining an adaptation of a parameter of an imaging examination that is to be carried out using a medical imaging apparatus in dependence on an input of information for the imaging examination, including: capturing the information input for the imaging examination; determining an item of information relating to an adaptation of the imaging examination in dependence on the information input; allocating the item of information relating to the adaptation of the imaging examination to a parameter of the imaging examination; determining an adaptation of the parameter of the imaging examination in dependence on the information input; and providing a parameter set of the imaging examination.

Claims

1. A computer-implemented method for determining an adaptation of a parameter of an imaging examination that is to be carried out using a medical imaging apparatus in dependence on an input of information for the imaging examination, comprising: capturing the information input for the imaging examination; determining an item of information relating to an adaptation of the imaging examination in dependence on the information input; allocating the item of information relating to the adaptation of the imaging examination to a parameter of the imaging examination; determining an adaptation of the parameter of the imaging examination in dependence on the information input; and providing a parameter set of the imaging examination, wherein the provided parameter set has a parameter that is changed in accordance with the determined adaptation, and wherein the parameter set is stored in storage that is connected to an imaging apparatus.

2. The method as claimed in claim 1, wherein the parameter of the imaging examination comprises an imaging parameter, an imaging sequence, and/or a parameter of a workflow of the imaging examination.

3. The method as claimed in claim 1, wherein the information input comprises a speech input from a user, the capturing of the information input comprises processing of the speech input from the user, and the adaptation of the parameter is determined in dependence on the speech input from the user.

4. The method as claimed in claim 3, wherein the speech input from the user is processed by means of an artificial neural network or a multilayered neural network.

5. The method as claimed in claim 3, wherein the speech input from the user is processed by means of a statistical model and/or a logical data model.

6. The method as claimed in claim 1, further comprising: outputting a captured image of the imaging examination to the user of the imaging apparatus, wherein the capturing of the information input comprises capturing feedback from the user on the captured image of the imaging examination, and the adaptation of the parameter is determined in dependence on the feedback from the user.

7. The method as claimed in claim 6, wherein the capturing of the information input comprises capturing feedback from the user on an imaging parameter and/or an image property.

8. The method as claimed in claim 1, wherein the information input comprises at least: an input from the user, a speech input from the user, a feedback message from the user on a captured image of the imaging examination, an item of information on a patient, a clinical finding, and/or an item of information on an image property.

9. The method as claimed in claim 1, further comprising: training an intelligent algorithm in respect of determining an adaptation of a parameter, wherein the intelligent algorithm is trained at least in dependence on the information input, the imaging examination to be carried out, and the determined adaptation of the parameter.

10. The method as claimed in claim 9, wherein the intelligent algorithm is an artificial neural network or a multilayered neural network.

11. The method as claimed in claim 9, wherein the training of the intelligent algorithm is activated or deactivated in dependence on an item of user information.

12. The method as claimed in claim 9, wherein the adaptation of the parameter is determined in dependence on the trained intelligent algorithm.

13. The method as claimed in claim 1, further comprising: carrying out the imaging examination for the purpose of capturing a diagnostic image of a patient, wherein the parameter of the imaging examination is changed in accordance with the determined adaptation, and the imaging examination is carried out with the changed parameter.

14. An imaging apparatus, comprising: a computer formed to coordinate a method as claimed in claim 1 and to carry the method out using the imaging apparatus.

15. A non-transitory computer program product, which is directly loadable into a data memory of a computer of an imaging apparatus, having program code to carry out the method as claimed in claim 1 when the computer program product is executed in the computer of the imaging apparatus.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Further advantages and details of the present disclosure will be apparent from the exemplary embodiments described below, and from the drawings, in which:

[0085] FIG. 1 shows a schematic representation of an embodiment of an imaging apparatus according to the disclosure,

[0086] FIG. 2 shows a flow chart of an embodiment of a method according to the disclosure, and

[0087] FIG. 3 shows a flow chart of an embodiment of a method according to the disclosure.

DETAILED DESCRIPTION

[0088] FIG. 1 shows an imaging apparatus according to the disclosure that, in the present example, takes the form of a magnetic resonance apparatus 10. The magnetic resonance apparatus 10 comprises a magnet unit 11, which has for example a permanent magnet, an electromagnet or a superconducting main magnet 12 for generating a strong and in particular homogeneous main magnetic field 13. Moreover, the magnetic resonance apparatus 10 comprises a patient-receiving region 14 for receiving a patient 15. In the present exemplary embodiment, the patient-receiving region 14 is cylindrical in form and is surrounded in a peripheral direction by the magnet unit 11. However, configurations of the patient-receiving region 14 that differ from this example are in principle also conceivable.

[0089] The patient 15 can be positioned in the patient-receiving region 14 by means of a patient-positioning apparatus 16 of the magnetic resonance apparatus 10. For this purpose, the patient-positioning apparatus 16 has a patient table 17 that is configured to be movable within the patient-receiving region 14. Furthermore, the magnet unit 11 has a gradient coil 18 for generating magnetic gradient fields that are used for spatial encoding during imaging. The gradient coil 18 is controlled by means of a gradient control unit 19 of the magnetic resonance apparatus 10. Furthermore, the magnet unit 11 may comprise a radio-frequency antenna that, in the present exemplary embodiment, takes the form of a body coil 20 that is permanently integrated in the magnetic resonance apparatus 10. The body coil 20 is formed to excite nuclear spins in the main magnetic field 13 generated by the main magnet 12. The body coil 20 is controlled by a radio-frequency unit 21 of the magnetic resonance apparatus 10, and irradiates an image capture region, substantially formed by a patient-receiving region 14 of the magnetic resonance apparatus 10, with radio-frequency excitation pulses. The body coil 20 may furthermore also be formed to receive magnetic resonance signals.

[0090] For the purpose of controlling the main magnet 12, the gradient control unit 19 and for controlling the radio-frequency unit 21, the magnetic resonance apparatus 10 has a control unit 22. The control unit 22 is formed to control the performance of a sequence, such as an imaging GRE (gradient echo) sequence, a TSE (turbo spin echo) sequence or a UTE (ultra-short echo time) sequence. Moreover, the control unit 22 comprises a computing unit 28 for evaluating magnetic resonance data captured during a magnetic resonance examination. The computing unit 28 of the magnetic resonance apparatus 10 may be formed to employ reconstruction methods in order to reconstruct magnetic resonance images using the magnetic resonance data.

[0091] The magnetic resonance apparatus 10 may further have a local receiving antenna 26, which is positioned at a diagnostically relevant body region of the patient 15 and detects magnetic resonance signals of the body region of the patient 15 and transmits them to the computing unit 28 of the control unit 22. The local receiving antenna 26 preferably has an electrical terminal line 27 that provides a signaling connection to the radio-frequency unit 21 and the control unit 22. Like the body coil 20, the local receiving antenna 26 may also be formed to excite nuclear spins and to receive magnetic resonance signals. The local receiving antenna 26 may for this purpose be controlled by the radio-frequency unit 21.

[0092] Furthermore, the magnetic resonance apparatus 10 comprises a user interface 23, which provides a signaling connection to the control unit 22. Control information such as imaging parameters, but also reconstructed magnetic resonance images, may be displayed to a user 40 at a display unit 24, for example at least one monitor of the user interface 23. Furthermore, the user interface 23 has an input unit 25 by means of which parameters of a magnetic resonance examination can be input by the user 40. In particular, it is conceivable that the user interface 23 has a speech input unit 31 that is formed to capture a speech input of the user 40. The user interface 23 may have for example a desktop computer or take the form of a mobile device such as a smartphone or a tablet computer.

[0093] The computing unit 28 and/or the speech processing unit 32 may be formed to receive and process a speech message of the user 40 from the speech input unit 31. In this case, the computing unit 28 may comprise the speech processing unit 32 or be connected to the speech processing unit 32 (see FIG. 1). Further, the computing unit 28 may be formed to output a determined adaptation of a parameter to the user 40 of the magnetic resonance apparatus 10 by way of the display unit 24, and/or to store it in a storage unit 29 and/or a storage unit in a cloud 30. It is furthermore conceivable that the computing unit 28 is formed to transmit the determined adaptation of the parameter to a further magnetic resonance apparatus (not shown) over a network connection or similar. In the present example, the information input is a speech message of the user 40 that is captured by means of the speech input unit 31. The speech input unit 31 may have for example a microphone and/or a sound sensor in order to receive the speech message of the user 40. The magnetic resonance apparatus 10 may further have an output unit (not shown) that, by means of an acoustic output, informs the user 40 of the fact that a change to a parameter of an imaging examination has been determined. However, it is likewise conceivable that the determined adaptation of the parameter is provided to the user 40 by means of the user interface 23, such as a screen with a speaker.

[0094] It is furthermore conceivable that the storage unit 29 and/or the storage unit in the cloud 30 have a database with explanations and/or descriptions of parameters of a magnetic resonance examination. The computing unit 28 and/or the speech processing unit 32 may accordingly be formed to access the database in the context of determining the item of information relating to the adaptation of the magnetic resonance examination, in dependence on a speech message of the user 40. For example, the explanations and/or descriptions of the parameters may be used for a semantic search, a keyword search and/or a text mining method. It is likewise conceivable that the item of information relating to adaptation of the magnetic resonance examination is allocated to a parameter of the magnetic resonance examination by means of a processor in the cloud 30 that has access to a search engine and the worldwide web. The processor in the cloud 30 may further have an intelligent algorithm that is trained by the user 40, using the item of information relating to the adaptation of the magnetic resonance examination, and/or is formed to determine the adaptation of the parameter of the magnetic resonance examination in dependence on the information input. Preferably, however, the magnetic resonance apparatus 10 comprises a learning and/or determination unit 34 at which an intelligent algorithm is implemented. The learning and/or determination unit 34 may be directly connected to the computing unit 28 and/or the speech processing unit 32. During training of the intelligent algorithm, the learning and/or determination unit 34 may receive an item of information on the imaging examination and an information input from the computing unit 28. It is possible, after a sufficient number of training cycles, for the learning and/or determination unit 34 to be formed to independently determine an adaptation of a parameter in dependence on the magnetic resonance examination and an information input.

[0095] It goes without saying that the illustrated magnetic resonance apparatus 10 may comprise further components that magnetic resonance apparatuses conventionally have. Further, it is also possible for other imaging apparatus such as computed tomography devices, X-ray devices, mammography devices, or similar to have a computing unit 28 that is formed to perform a method according to the disclosure having the imaging apparatus.

[0096] FIG. 2 shows a possible flow chart of a method according to the disclosure for determining an adaptation of a parameter of a magnetic resonance examination that is to be carried out in dependence on an information input for the magnetic resonance examination.

[0097] In an optional step S1, a captured magnetic resonance image of the magnetic resonance examination is output to the user 40 of the magnetic resonance examination 10, wherein capture of the information input comprises capture of a feedback message from the user 40 on the captured magnetic resonance image of the magnetic resonance examination. The magnetic resonance image may be output by means of a display unit 24, which preferably has a screen for displaying the magnetic resonance image. The magnetic resonance image may be a first magnetic resonance image of a series of magnetic resonance images that are captured during the magnetic resonance examination. However, it is likewise conceivable that the magnetic resonance image is a subsequent magnetic resonance image. When the magnetic resonance image is output, the user 40 of the magnetic resonance apparatus may be requested to deliver feedback on the magnetic resonance image. Feedback from the user 40 may comprise for example a speech message, a gesture and/or an input by means of any desired input device (e.g. mouse, keyboard) of the user interface 23. Preferably, feedback from the user 40 comprises an item of information relating to an adaptation of the imaging examination, such as an assessment an image quality of the magnetic resonance image and/or a request for adaptation of an imaging parameter, an imaging sequence and/or a parameter of the workflow of the magnetic resonance examination.

[0098] In a step S2, an information input for the magnetic resonance examination is captured. Information inputs made by the user 40 may comprise for example an input by means of the input unit 25, a speech input by means of the speech input unit 31, and/or feedback from the user 40 on a magnetic resonance image. Likewise, the feedback from the user 40 may similarly be captured by means of the input unit 25 and/or the speech input unit 31. Moreover, the information input may also be a gesture by the user 40 that is captured by means of a camera in an examination room of the magnetic resonance apparatus 10.

[0099] However, the information input may also be performed independently of the user 40. For example, the information input may be an item of patient information on the patient 15, a clinical finding on the patient 15 and/or an item of information on an image property of the magnetic resonance image. Preferably, the computing unit 28 is formed to derive such information inputs in dependence on data from a radiological information system, a hospital information system, a patient registration, a captured magnetic resonance image or similar. The information on the image property may in this case comprise in particular a quantification of a signal-to-noise ratio of the magnetic resonance image and/or an assessment of the presence of image artifacts. For this, the magnetic resonance apparatus 10 may have a dedicated image processing unit that is formed to determine an image property of the magnetic resonance image.

[0100] Further, capture of the information input may comprise processing of the information input. For this purpose, a signal of the information input such as a noise signal, a speech message, a camera signal, a text-based message or similar is preferably converted into an electrical signal and/or into machine-readable data. Machine-readable data may take the form among other things of binary code, hexadecimal numbers, a high-level language and be in suitable file formats such as RDFa, HTML, CSV, XML and similar. For example, a speech input by the user 40 is captured using the speech input unit 31 and converted into machine-readable data. Then, the speech input may be processed using the computing unit 28 and/or the speech processing unit 32. Here, speech processing may have one or more of the following steps, which are known to those skilled in the art: [0101] speech recognition, [0102] tokenization, [0103] morphological analysis, [0104] syntactical analysis, [0105] semantic analysis, and [0106] dialog analysis.

[0107] In one embodiment, the speech input is processed using an artificial neural network or a multilayered neural network (e.g. MultiNet). However, it is likewise conceivable that an individual one or a plurality of the listed steps of speech processing are performed using an artificial neural network or a multilayered neural network.

[0108] According to a further embodiment, processing of the speech input is performed using a statistical model and/or a logical data model. Preferably, the speech input is processed using a statistical model and/or a logical data model according to individual or all of the above-listed steps of speech processing. It is conceivable that, when corresponding models are used, the user 40 makes use of a predetermined selection of terms or instructions, wherein this selection is matched to the imaging apparatus and/or the speech input unit.

[0109] A method for speech processing carried out using the computing unit 28 and/or the speech processing unit 32. However, it is likewise conceivable that the method for speech processing is implemented on an external processor, in particular a processor in the cloud 30. In this case, the speech input can be transmitted to the processor in the cloud 30, and this processor processes the speech input and sends an item of information relating to the adaptation of the magnetic resonance implementation back to the computing unit 28.

[0110] In a further step S3, an item of information relating to an adaptation of the magnetic resonance examination is determined in dependence on the information input. The item of information relating to adaptation of the magnetic resonance examination may be determined in a complex manner from processed information inputs made by the user 40 and from an item of patient information, a clinical finding and/or an item of information on an image property. Here, for each information input a check may be carried out of the information input in respect of correlation with an imaging parameter, an imaging sequence and/or a workflow of the magnetic resonance examination. Preferably, the item of information relating to the adaptation of the magnetic resonance examination comprises an indication of the direction in which the adaptation of a parameter is to be made. While it is possible for a user 40 to input a desired change to an imaging parameter using a speech message, an indication of the direction in which the adaptation of a parameter is to be made may, within the context of the method according to the disclosure, also be determined automatically in dependence on an image property of a magnetic resonance image. Undesired image properties may be for example a low signal-to-noise ratio and/or or an undesired position of a diagnostic relevant region of the body of the patient 15 in an imaging region.

[0111] In a further step S4, the item of information relating to the adaptation of the magnetic resonance examination is allocated to a parameter of the magnetic resonance examination. Here, allocation is preferably performed by means of a classification. It is conceivable that the classification comprises use of a neural network, a multilayered neural network and/or a text mining method. It is also possible for the classification to comprise formation of tuples, vectors, matrices and/or any desired data structure, wherein these allocate the item of information on the change to the imaging examination to a parameter of the imaging examination. It is furthermore conceivable that allocation of the item of information relating to the adaptation of the imaging examination is performed using a model, such as a statistical model and/or a logical data model. The method for classifying the item of information relating to the adaptation of the magnetic resonance examination may be implemented for example at the computing unit 28 and/or a processor in the cloud 30.

[0112] In a further step S5, an adaptation of a parameter of the magnetic resonance examination is determined in dependence on the information input. Preferably, the adaptation of a parameter is determined using the item of information relating to the adaptation of the magnetic resonance examination. For example, the item of information relating to the adaptation of the magnetic resonance examination may comprise a desire of the user 40 in respect of a lower contrast of fatty tissue in a magnetic resonance image. Here, determination of the adaptation in relation to relevant imaging parameters, such as a time to echo and/or a repetition time, may be performed taking into account a T1 relaxation time and/or a T2 relaxation time of fatty tissue. Corresponding data on the fatty tissue is preferably stored in and retrievable from the storage unit 29 or a storage unit in the cloud 30.

[0113] In a preferred embodiment, the adaptation of the parameter is determined in dependence on the speech input made by the user 40. Preferably, the item of information relating to the adaptation of the magnetic resonance examination is determined by means of one of the above-described methods for processing the speech input. It is furthermore conceivable that the computing unit 28 is formed to prioritize different information inputs, in dependence on the items of information relating to the adaptation of the magnetic resonance examination. In this way, from a plurality of information inputs it is possible to draw on an information input having top priority, such as the safety of the patient 15 in relation to a specific absorption rate (or a radiation dose in X-ray imaging), for the purpose of determining the adaptation of the parameter. However, it is also possible for the computing unit 28 to be formed to determine a compromise between a plurality of information inputs. In this case, a compromise of the parameters may be found using the plurality of conflicting information inputs, such as a high number of magnetic resonance images and the taking into account of the specific absorption rate.

[0114] In an optional step S6, an intelligent algorithm is trained with respect to determining adaptation of the parameter, wherein the intelligent algorithm is trained at least in dependence on the information input, the magnetic resonance examination to be carried out, and the determined adaptation of the parameter. Preferably, training of the intelligent algorithm comprises supervised learning of a multilayered neural network. The multilayered neural network may in this case have for example two layers (hidden layers), three layers, four layers or more than four layers. During the training, for each magnetic resonance examination in which an adaptation of a parameter in dependence on an information input is determined, a training data set may be transmitted to the learning and/or determination unit 34 on which the multilayered neural network is implemented. The training data set preferably comprises at least information relating to the magnetic resonance examination, the information input and the adaptation of the parameter that has been determined in dependence on the information input. The item of information relating to the magnetic resonance examination may for example comprise a parameter set of a standard sequence that prevailed before adaptation of the parameter was determined.

[0115] In one example, the multilayered neural network is trained by a supervised learning method. Here, the parameter set of the magnetic resonance examination having the changed parameter that was determined in dependence on the information input may be a target output of the multilayered neural network. The target output can be compared with an actual output that the multilayered neural network generates in the current state. For this purpose, an input pattern such as the standard sequence and/or the information input may be propagated forward by the multilayered neural network. It is conceivable that an item of information relating to the adaptation of the magnetic resonance examination in dependence on the information input has already been determined by the computing unit 28 and/or the speech processing unit 32 before it is transmitted as an information input to the learning and/or determination unit 34. In the course of the training procedure, the information input (or the item of information relating to the adaptation of the magnetic resonance examination) may be regarded for example as a parameter of the parameter set of the magnetic resonance examination, or implemented as a separate input variable, such as a label of the input pattern. Furthermore, it goes without saying that further possible implementations are conceivable.

[0116] The multilayered neural network may be trained by comparing the actual output and the target output. It is conceivable that a backpropagation method or an SGD (stochastic gradient descent) method is used for this purpose. For example, in the backpropagation method a difference between the actual output and the target output of the multilayered neural network is formed, and this difference is considered an error. The error can then be backpropagated from an output layer to an input layer of the multilayered neural network. Here, a configuration of the multilayered neural network, in particular a weighting of connections between neurons, can be changed depending on its effect on the error. By means of corresponding methods, the error between the target output and the actual output of the multilayered neural network for an input pattern can be minimized. It is also conceivable, referring to FIG. 1, that the learning and/or determination unit 34 is implemented on a processor in the cloud 30. In this way, the learning and/or determination unit 34 may be trained by a multiplicity of data sets of magnetic resonance apparatuses that are connected to the cloud 30.

[0117] In one embodiment, rights of a user 40 in respect of training are linked to a profile of the user 40, such that training of the intelligent algorithm is automatically activated or deactivated when a user 40 logs onto the imaging apparatus, depending on the rights.

[0118] In a step S7, a parameter set of the magnetic resonance examination is provided, wherein the provided parameter set has a parameter that is changed in accordance with the determined adaptation, and wherein the parameter set is stored in a storage unit that is connected to an imaging apparatus. Provision comprises at least storage of the parameter set of the magnetic resonance examination having the changed parameter, in the storage unit 29 of the magnetic resonance apparatus 10 and/or a storage unit in the cloud 30. It is furthermore conceivable that the parameter set having the changed parameter is transmitted to a further magnetic resonance apparatus. In this way, a user 40 of the magnetic resonance apparatus 10 can also make use of adaptations on a further magnetic resonance apparatus that have been made by means of the method according to the disclosure under particular conditions for a particular magnetic resonance examination. Further, provision of the parameter set may also comprise visual output of the parameter set to the user 40, by means of the display unit 24. Preferably, the changed parameter is indicated by a marker and/or highlighted in the visual output so that the user 40 can quickly ascertain which parameters of the parameter set have been changed.

[0119] In an optional step S8, the magnetic resonance examination is carried out for the purpose of capturing a diagnostic image of a patient, wherein the parameter of the magnetic resonance examination is changed in accordance with the determined adaptation, and wherein the magnetic resonance examination is carried out using the changed parameter. It is conceivable that an information input is captured even before capture of the first magnetic resonance image, for example during preparation of the magnetic resonance examination and/or during patient registration. In this case, the magnetic resonance examination may be started up using the changed parameter. However, it is likewise conceivable that the information input comprises feedback from the user 40 on the first magnetic resonance image, the subsequent magnetic resonance image and/or a clinical finding that is derived in dependence on a magnetic resonance image. In such cases, the magnetic resonance examination may be propagated using the parameter that has been changed in dependence on the feedback from the user 40 and/or the clinical finding.

[0120] FIG. 3 shows a flow chart of a further embodiment of the method according to the disclosure. In this embodiment, the steps S1 to S7 as described above may be carried out at a first point in time. The first point in time is in particular characterized in that an intelligent algorithm of a learning and/or determination unit 34 such as a multilayered neural network is trained in dependence on a magnetic resonance examination, an information input and a determined adaptation of a parameter.

[0121] At a second point in time, the intelligent algorithm has completed a sufficient number of training cycles. It is conceivable that the step S5 of determining the adaptation of the parameter of the magnetic resonance examination in dependence on the information input is performed using the intelligent algorithm from the second point in time onward. In one embodiment, the computing unit 28 is formed to retrieve the determination of the adaptation of a parameter from the learning and/or determination unit 34 at the second point in time. For this purpose, the learning and/or determination unit 34 may for example receive a parameter set of the magnetic resonance examination and an item of information relating to adaptation of the magnetic resonance examination, and provide a parameter set having at least one changed parameter. The step S4 of allocating the item of information relating to adaptation of the magnetic resonance examination to a parameter of the magnetic resonance examination may be implicitly performed using the intelligent algorithm, at the second point in time. It is likewise conceivable that the step S6 of training the intelligent algorithm in respect of determining the adaptation of the parameter is dispensed with from the second point in time onward. Preferably, however, the intelligent algorithm may run through further training cycles, for example if, during step S3 of determining an item of information relating to adaptation of the magnetic resonance examination, a request from a user 40 with expert specialist knowledge for a change to a parameter is determined. It is furthermore conceivable that training of the intelligent algorithm and/or use of the intelligent algorithm for determining an adaptation of a parameter may be activated or deactivated manually for a predetermined magnetic resonance examination and/or as a global setting of the magnetic resonance apparatus 10.

[0122] As described above, the computing unit 28 may further be formed to carry out the magnetic resonance examination using the parameter set having the changed parameter. It is also conceivable that at the second point in time the parameter set of the magnetic resonance examination is the current one and/or that there is no information input. In this case, the computing unit 28 may continue with coordination of the magnetic resonance examination without any change to a parameter. In a further embodiment, the intelligent algorithm may likewise be trained at the second point in time to determine (S3) the item of information relating to adaptation of the magnetic resonance examination in dependence on the information input according to one of the above-described methods, in particular using an artificial neural network or a multilayered neural network.

[0123] It goes without saying that the embodiments of the inventive method and the inventive magnetic resonance apparatus that are described here should be understood as exemplary. Individual embodiments can thus be extended by features of other embodiments. In particular, the order in which the method steps of the inventive method are performed should be understood as exemplary. The individual steps may also be carried out in a different order, or may overlap one another in time, partly or completely.