Peripheral nerve stimulation detection device

Abstract

The present invention relates to an apparatus for detecting peripheral nerve stimulation (PNS). The apparatus includes an input unit, at least one sensor, a processing unit, and an output unit. The input unit is configured to provide the processing unit with information regarding individual scans of a scan sequence of a magnetic resonance imaging (MRI) unit used to perform medical imaging of a patient. The at least one sensor is configured to acquire sensor data of the patient undergoing medical imaging. The processing unit is configured to determine the presence of peripheral nerve stimulation (PNS), the determination of the presence of PNS comprising information regarding the individual scans of the scan sequence and the patient's sensor data. The output unit is configured to output an indication that the presence of PNS has been determined.

Description

Technical Field

[0001] The present invention relates to an apparatus for detecting peripheral nerve stimulation, an imaging system, a method for detecting peripheral nerve stimulation, and a computer program element and a computer-readable medium.

Background Art

[0002] It is known that the strong currents applied to the gradient coils of a magnetic resonance imaging (MRI) unit during an MRI procedure have undesirable side effects, which excite the patient's sensory and motor nerves. The patient will typically feel this as a tingling sensation or spontaneous muscle contractions, typically in the back or abdomen. This effect is known as peripheral nerve stimulation (PNS). It is unpleasant and can even be painful for the patient and thus should be avoided during the scan. PNS can also cause motion artifacts, and the patient may move too much due to PNS, which can compromise the image being taken. Also, the patient may feel pain and, in certain situations, may panic and make dangerous movements.

[0003] Standard clinical environment staff can help minimize the likelihood of PNS occurring through proper patient positioning, cable routing, and supervision during the scan. Specialist staff can interpret the patient's responses as indicating that PNS is occurring. As a result, the staff can also adapt the scan when PNS is being caused to mitigate the PNS that occurs.

[0004] However, sensitive patients can be induced to abort the scan even for mild PNS, which is not dangerous. When supervised, this can usually be handled by the staff calming the patient. However, such staff intervention is not possible in an autonomous setting, and sedated, impaired, or unknowledgeable patients may not feel or report PNS that can cause pain or motion artifacts. [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] These problems need to be addressed.

[0006] Having improved means of detecting peripheral nerve stimulation in patients undergoing MRI imaging scans would be advantageous. [Means for solving the problem]

[0007] The object of the present invention is solved by the subject matter of the independent claims, and further embodiments are incorporated into the dependent claims. It should be noted that the embodiments and examples described below of the present invention also apply to apparatus, imaging systems, methods for detecting peripheral nerve stimulation, as well as computer program elements and computer-readable media.

[0008] In the first aspect, an apparatus for detecting peripheral nerve stimulation as described in claim 1 is provided.

[0009] In a second embodiment, an imaging system as defined in claim 10 is provided.

[0010] In a third aspect, a method for detecting peripheral nerve stimulation as described in claim 12 is provided.

[0011] In the first embodiment, an apparatus for detecting peripheral nerve stimulation is provided, and the apparatus is Input unit and At least one sensor, Processing unit and Output unit and It is equipped with.

[0012] The input unit is configured to provide the processing unit with information about individual scans in a scan sequence of a magnetic resonance imaging (MRI) unit used to perform medical imaging of a patient. At least one sensor is configured to acquire sensor data from the patient undergoing medical imaging. The processing unit is configured to determine the presence of peripheral nerve stimulation (PNS). The determination of the presence of PNS involves utilizing information about individual scans in the scan sequence and the patient's sensor data. The output unit is configured to output an indication that the presence of PNS has been determined.

[0013] The device developed above is based on the inventor's insight working in this art that peripheral nerve stimulation (PNS) occurs at a frequency defined by the repetition rate of individual scans in the scan sequence. This is because the inclination changes of the coils used in each scan are frequently repeated across the imaging block from one scan to the next, resulting in a very strong inclination of the coils, and a steep rise and fall gradient of the coil globe that causes PNS. Therefore, by using patient sensor data and information about individual scans in the scan sequence together, it is possible to detect patient motion or artifacts indicating the presence of PNS. This allows, for example, staff to be alerted, the MR sequence to be adapted to avoid PNS, and in fact, the scan itself to be stopped in severe cases.

[0014] In one example, information about each scan in a scan sequence includes the duration of each scan, and the determination of the presence of a PNS involves utilizing the duration of each scan.

[0015] In one example, the processing unit is configured to select sections of sensor data over the acquisition period. The determination of the presence of PNS may include the use of sections of sensor data over the acquisition period that are converted to the frequency domain.

[0016] One example involves using the Fourier transform to convert to the frequency domain.

[0017] In one example, the processing unit is configured to select sensor data in the frequency domain at a center frequency equal to the reciprocal of the time period of each scan. The determination of the presence of a PNS may include the use of selected sensor data in the frequency domain at the center frequency.

[0018] In one example, the processing unit is configured to determine the signal strength of sensor data in the frequency domain at the center frequency, and the determination of the presence of PNS involves utilizing the signal strength.

[0019] In this way, the signal component of the sensor data caused by PNS is selected, and signals at all other frequencies are discarded, allowing PNS to be detected with a high signal-to-noise ratio.

[0020] In one example, the processing unit is configured to determine the signal strength of sensor data in the frequency domain over a frequency range. Determining the presence of a PNS may involve utilizing the signal strength of sensor data in the frequency domain over a frequency range.

[0021] In other words, the signal strength associated with PNS can be compared to signals such as the average signal over a frequency range, and the signal over a frequency range can be a good approximation of the noise floor.

[0022] In one example, the frequency range is centered at frequencies greater than the center frequency equal to the reciprocal of the time period of each scan. In this way, for example, only the high-frequency range of the spectrum may be selected for noise determination.

[0023] In one example, information about individual scans in a scan sequence includes gradient waveform information for at least one tilt coil of the MRI unit. The determination of the presence of PNS may include the use of gradient waveform information for the tilt coil of the MRI unit.

[0024] In one example, the tilt waveform information of the tilt coil includes at least one time of at least one tilt within the period of an individual scan. The processing unit is configured to determine a correlation between the time of the tilt within the period and the relevant sensor data of the sensor data within the period. The determination of the presence of PNS can include the use of the correlation between the tilt and the relevant sensor data.

[0025] In one example, the determination of the correlation between the tilt and the relevant sensor data includes the determination of a fixed phase relationship between the time of the tilt in successive periods of an individual scan and the time of the relevant sensor data in successive periods of an individual scan.

[0026] Thus, in the time domain, patient movement is induced by PNS, but is slightly delayed from the rapid tilt that causes PNS. By correlating the tilt in successive scans with the sensor data, a specific sensor data that occurs at a set phase, in other words, a delay in time after the time of the tilt indicates that the specific sensor data indicates PNS and a determination can be made that PNS is occurring.

[0027] In a second embodiment, an imaging system is provided, the imaging system comprising a magnetic resonance imaging MRI unit, at least one sensor, and a processing unit and is provided with.

[0028] The MRI unit is configured to provide information regarding individual scans of the scan sequence of the MRI unit used to perform medical imaging of a patient to the processing unit. The at least one sensor is configured to acquire sensor data of a patient undergoing medical imaging. The processing unit is configured to determine the presence of peripheral nerve stimulation PNS. The determination of the presence of PNS includes the use of information regarding individual scans of the scan sequence and the sensor data of the patient.

[0029] In one example, if the presence of a PNS is detected, the processing unit is configured to control the MRI unit to adjust the gradient power of the MRI unit's coils.

[0030] In a third embodiment, a method for detecting peripheral nerve stimulation is provided, which is: a) Providing a processing unit with information about individual scans of a scan sequence of a magnetic resonance imaging (MRI) unit used to perform medical imaging of a patient, b) A step of acquiring at least one sensor data of a patient undergoing medical imaging by a sensor, c) A step of determining the presence of peripheral nerve stimulation PNS by a processing unit, wherein the step of determining the presence of PNS includes utilizing information on individual scans of a scan sequence and patient sensor data, d) A step of optionally outputting an instruction that the existence of PNS has been determined. It has.

[0031] In another embodiment, a computer program element is provided that controls one or more of the aforementioned devices or systems, which is adapted to perform one or more of the aforementioned methods when the computer program element is executed by a processing unit.

[0032] In another embodiment, a computer-readable medium having the aforementioned stored computer elements is provided.

[0033] The computer program element may be, for example, a software program, but may also be an FPGA, PLD, or any other suitable digital means.

[0034] Conveniently, any advantage provided by any of the above embodiments applies equally to all of the other embodiments, and vice versa.

[0035] The above embodiments and examples will become apparent from the embodiments described below and will be explained with reference thereto.

[0036] Exemplary embodiments are described below with reference to the following drawings. [Brief explanation of the drawing]

[0037] [Figure 1] A schematic configuration of an example of a device for detecting peripheral nerve stimulation is shown. [Figure 2] A schematic configuration of an example imaging system is shown. [Figure 3] This document describes a method for detecting peripheral nerve stimulation. [Figure 4] An exemplary sequence diagram of an MRI diffusion scan is shown. [Modes for carrying out the invention]

[0038] Figure 1 shows a schematic example of a device 10 for detecting peripheral nerve stimulation. The device 10 comprises an input unit 20, at least one sensor 30, a processing unit 40, and an output unit 50. The input unit is configured to provide the processing unit with information about individual scans of a scan sequence of a magnetic resonance imaging (MRI) unit used to perform medical imaging of a patient. At least one sensor is configured to acquire sensor data from a patient undergoing medical imaging. The processing unit is configured to determine the presence of peripheral nerve stimulation (PNS). The processing unit's determination of the presence of PNS involves the use of information about individual scans of the scan sequence and the patient's sensor data. The output unit is configured to output an indication that the presence of PNS has been determined. The output unit may be part of the processing unit or it may not be a separate unit.

[0039] For example, information about each scan in a scan sequence includes the duration of each scan. Then, the processing unit's determination of the presence of the PNS may include the use of the duration of each scan.

[0040] For example, the processing unit is configured to select sections of sensor data over an acquisition period. The processing unit's determination of the presence of PNS may include utilizing sections of sensor data over an acquisition period that are converted to the frequency domain.

[0041] For example, the conversion to the frequency domain involves the use of the Fourier transform by the processing unit.

[0042] For example, the processing unit is configured to select sensor data in the frequency domain at a center frequency equal to the reciprocal of the time period of each scan. The processing unit's determination of the presence of PNS may include utilizing the selected sensor data in the frequency domain at the center frequency.

[0043] For example, the processing unit is configured to determine the signal strength of sensor data in the frequency domain at the center frequency. The processing unit's determination of the presence of PNS may then include the use of the signal strength.

[0044] For example, the processing unit is configured to determine the signal strength of sensor data in the frequency domain over a frequency range. The processing unit's determination of the presence of a PNS may include utilizing the signal strength of sensor data in the frequency domain over a frequency range.

[0045] For example, the frequency range is centered on frequencies greater than the center frequency equal to the reciprocal of the time period of each scan.

[0046] In one example, the frequency range is centered around a frequency equal to the reciprocal of the time period of each scan.

[0047] In one example, the signal strength of sensor data in the frequency domain across a frequency range is determined as the average signal strength of sensor data in the frequency domain across the entire frequency range. Therefore, for example, if the signal strength at the center frequency is greater than the average signal across all frequencies by a user-defined coefficient greater than 1, the presence of PNS can be flagged. The user-defined coefficient can be any value greater than 1, and the actual value can be determined based on the noise floor. Also, as the PNS coefficient increases beyond 1, the degree of PNS increases, so different values ​​can be used to trigger different events. Therefore, lower levels somewhat above 1 may be used to instruct staff members to reassure the patient, while larger PNS values ​​further above 1 may be used to automatically adjust the scan so that a new scan sequence is less likely to induce PNS in the patient, or to instruct staff members to adjust the scan. Furthermore, the PNS value can be increased further as needed to automatically stop the scan.

[0048] For example, information about individual scans in a scan sequence includes gradient waveform information for at least one tilt coil of the MRI unit. The processing unit's determination of the presence of PNS may include the use of gradient waveform information for the tilt coil of the MRI unit.

[0049] For example, the gradient waveform information for a gradient coil includes at least one time for at least one gradient within the duration of each scan. The processing unit is configured to determine the correlation between the time of the gradient within the duration and the associated sensor data within the duration. The processing unit's determination of the presence of PNS may then include utilizing the correlation between the gradient and the associated sensor data.

[0050] For example, determining the correlation between tilt and associated sensor data by the processing unit involves determining a fixed phase relationship between the time of tilt during a consecutive period of individual scans and the time of the associated sensor data during a consecutive period of individual scans.

[0051] In one example, at least one sensor comprises one or more of the following: one or more cameras, one or more position sensors, one or more accelerometers, one or more RF sensors, or the MRI unit itself.

[0052] Figure 2 shows a schematic example of the imaging system 100. The imaging system 100 comprises a magnetic resonance imaging (MRI) unit 110, at least one sensor 120, and a processing unit 130. The MRI unit is configured to provide the processing unit with information about individual scans of the scan sequence of the MRI unit used to perform medical imaging of a patient. At least one sensor is configured to acquire sensor data of the patient undergoing medical imaging. The processing unit is configured to determine the presence of peripheral nerve stimulation (PNS). Determining the presence of PNS involves utilizing information about individual scans of the scan sequence and the patient's sensor data.

[0053] For example, if the presence of PNS is detected, the processing unit is configured to control the MRI unit to adjust the gradient power of the MRI unit's coils.

[0054] For example, information about each scan in a scan sequence includes the duration of each scan. Then, the processing unit's determination of the presence of the PNS may include the use of the duration of each scan.

[0055] In one example, the processing unit is configured to select a section of sensor data over the acquisition period. The processing unit's determination of the presence of PNS may include utilizing a section of sensor data over the acquisition period that is converted to the frequency domain.

[0056] In one example, the conversion to the frequency domain involves the use of the Fourier transform by the processing unit.

[0057] In one example, the processing unit is configured to select sensor data in the frequency domain at a center frequency equal to the reciprocal of the time period of each scan. The processing unit's determination of the presence of PNS may include utilizing the selected sensor data in the frequency domain at the center frequency.

[0058] In one example, the processing unit is configured to determine the signal strength of sensor data in the frequency domain at the center frequency. The processing unit's determination of the presence of a PNS may then include utilizing the signal strength.

[0059] In one example, the processing unit is configured to determine the signal strength of sensor data in the frequency domain over a frequency range. The processing unit's determination of the presence of a PNS may then include utilizing the signal strength for the sensor data in the frequency domain over a frequency range.

[0060] For example, the frequency range is centered at a frequency greater than the center frequency equal to the reciprocal of the time period of each scan.

[0061] In one example, the signal strength of sensor data in the frequency domain across the frequency range is determined as the average signal strength of sensor data in the frequency domain across the entire frequency range.

[0062] In one example, information about individual scans in a scan sequence includes gradient waveform information for at least one tilt coil of the MRI unit. The processing unit's determination of the presence of PNS may include the use of gradient waveform information for the tilt coil of the MRI unit.

[0063] In one example, the tilt waveform information of the tilt coil includes at least one time for at least one tilt within the duration of each scan. The processing unit is configured to determine the correlation between the tilt time within the duration and the associated sensor data within the duration. The processing unit's determination of the presence of PNS may then include utilizing the correlation between the tilt and the associated sensor data.

[0064] In one example, the determination of the correlation between the tilt and the associated sensor data by the processing unit includes determining a fixed phase relationship between the time of the tilt during a consecutive period of individual scans and the time of the associated sensor data during a consecutive period of individual scans.

[0065] In one example, at least one sensor comprises one or more of the following: one or more cameras, one or more position sensors, one or more accelerometers, and one or more RF sensors.

[0066] In one example, the MRI unit is configured to use MRI data as sensor data that can be used to determine the PNS.

[0067] In one example, the system is configured to output an instruction indicating that the presence of a PNS has been determined.

[0068] Figure 3 shows an example of a method 20) for detecting peripheral nerve stimulation in its basic steps. The method includes, in providing step 210, also called step a), providing a processing unit with information about individual scans of a scan sequence of a magnetic resonance imaging (MRI) unit used to perform medical imaging of a patient; in acquisition step 220, also called step b), acquiring sensor data of the patient being medically imaged by at least one sensor; and in determination step 230, also called step c), determining the presence of peripheral nerve stimulation (PNS) by the processing unit, the step of determining the presence of PNS includes utilizing information about individual scans of the scan sequence and the patient's sensor data.

[0069] In one example, the method includes output step 240, also called step d), which outputs an indicator that the presence of PNS has been determined.

[0070] In one example, if the presence of a PNS is determined, the method includes the step of controlling the MRI unit with a processing unit to adjust the gradient power of the inclined coil of the MRI unit.

[0071] In one example, the information about each scan in the scan sequence includes the duration of each scan, and step c) includes a step that utilizes the duration of each scan.

[0072] In one example, the method includes a step in which a processing unit selects a section of sensor data over an acquisition period, and step c) includes a step in which the section of sensor data over an acquisition period is converted to the frequency domain.

[0073] One example involves a step where the transformation to the frequency domain utilizes the Fourier transform.

[0074] In one example, the method includes a step in which a processing unit selects sensor data in the frequency domain at a center frequency equal to the reciprocal of the time period of each scan, and step c) includes a step in which the selected sensor data in the frequency domain at the center frequency is utilized.

[0075] In one example, the method includes a step in which a processing unit determines the signal strength of sensor data in the frequency domain at the center frequency, and step c) includes a step in which the signal strength is utilized.

[0076] In one example, the method includes a step in which a processing unit determines the signal strength of sensor data in the frequency domain over a frequency range, and step c) includes a step in which the signal strength of sensor data in the frequency domain over a frequency range is utilized.

[0077] For example, the frequency range is centered at a frequency greater than the center frequency equal to the reciprocal of the time period of each scan.

[0078] In one example, the signal strength of sensor data in the frequency domain across the frequency range is determined as the average signal strength of sensor data in the frequency domain across the entire frequency range.

[0079] In one example, information about each scan in a scan sequence includes gradient waveform information for at least one tilt coil of the MRI unit, and step c) includes a step of utilizing the gradient waveform information for the tilt coil of the MRI unit.

[0080] In one example, the tilt waveform information of the tilt coil includes at least one time of at least one tilt within the period of each scan, and the method includes a step by a processing unit to determine the correlation between the time of the tilt within the period and the relevant sensor data within the period, and step c) includes a step of utilizing the correlation between the tilt and the relevant sensor data.

[0081] In one example, the step of determining the correlation between the tilt and the associated sensor data includes determining a fixed phase relationship between the time of the tilt in a consecutive period of individual scans and the time of the associated sensor data in a consecutive period of individual scans.

[0082] In one example, at least one sensor comprises one or more of the following: one or more cameras, one or more position sensors, one or more accelerometers, one or more RF sensors, or the MRI unit itself.

[0083] Thus, new technologies for automatically detecting PNS are based on the insight that PNS occurs at frequencies defined by the repetitive gradient waveform of an MRI scan. This is because the root cause of PNS is the induction of voltages within the body caused by rapid changes in the magnetic field generated by the gradient coil. This insight is utilized to realize highly sensitive PNS sensing devices / systems by spectrally selectively detecting PNS from various types of sensors such as cameras, position sensors, or accelerometers, or even by using image data from the MRI unit itself.

[0084] A device for detecting peripheral nerve stimulation, an imaging system, and a method for detecting peripheral nerve stimulation will be described in more detail with reference to a specific embodiment with reference to Figure 4.

[0085] Figure 4 shows a typical MRI sequence that commonly causes PNS. Figure 4 is a sequence diagram representation of an MRI diffusion scan. The lines GSS, GPE, and GFE represent slope waveforms. The diffusion slope lobes, shown as dashed lines between the vertical bars, have very steep rise and fall slopes, and their magnitude is actually much higher than depicted here. Such slopes can cause PNS due to the steep rise and fall slopes. Individual scans in a scan block are repeated at the repetition time of TR.

[0086] Continuing with Figure 4, this diffusion sequence thus includes two very strong slope lobes, with steep rise and fall slopes at four points in the repetition time of each scan in the scan block or scan sequence. The steep rise and fall slopes cause PNS and are marked with vertical bars. As described above, the basic components of the sequence continue with one repetition time TR, which can be, for example, 800 ms. This block is repeated many times during the execution of the sequence command, such that the grade is repeated at each frequency of 1.25 Hz. The inventors have found that PNS is caused in sync with this repetition, i.e., at the same fundamental frequency, and even due to a phase relationship fixed to the waveform, for example, due to the patient's movement that is slightly delayed in time to the steep slope. It should be noted that other examples of scans that cause PNS can have shorter TRs and higher repetition frequencies, and can also have longer TRs.

[0087] When a patient undergoes an MRI scan, the patient's sensor data is acquired and analyzed along with information from individual scans, as shown in Figure 4. Then, highly sensitive quantitative measurements of the PNS are generated by spectrally selective evaluation of the output of any sensor. This can be achieved, for example, by Fourier-based analysis in the frequency domain, as described below, but in principle, it can also be done by a gating mechanism in the time domain, or in the time domain by correlating sensor data with the gradient within and between consecutive scans to determine a constant phase relationship between the time of specific sensor data and the time of the gradient.

[0088] The following are specific examples of how this can be achieved.

[0089] frequency domain The output of the sensor's time signal is sampled. A section having the length of period Tacq is then Fourier transformed to measure the average signal intensity P in the frequency band Δf = 1 / TR + / 1 / Tacq, while the signal at all other frequencies is discarded. This effectively selects the signal components caused by PNS. A tolerance bandwidth of 2 / Tacq is chosen because this defines the primary limit on the frequency resolution of any time signal sampled for time Tacq. This is governed by the Nyquist limit.

[0090] Fourier analysis can be performed in sliding window mode, meaning that after the Fourier transform of the first fully sampled data vector of duration Tacq is obtained, it is not necessary to wait for Tacq to completely fill the next data vector with new data, but rather the next Fourier transform can be performed as early as, for example, after replacing only a quarter of the data. This can, of course, occur at, for example, half-data points or 10th percentile points.

[0091] The presence of a PNS can be determined as follows: The signal intensity P in the frequency band Δf = 1 / TR + / 1 / Tacq is compared to the average signal M across all frequencies, which is still a good approximation of the noise floor. Here, instead of using all frequencies for the noise floor, a portion of all frequencies can be used, for example, a high-frequency band exceeding the frequency band Δf. If P > f*M with a user-defined coefficient f > 1, the presence of a PNS is flagged. This flag can automatically trigger the following actions. Alarm staff. A sequence change to a mode that uses less gradient power (mT / m) and / or velocity (mT / m / m). Stopped scanning.

[0092] Different coefficients fi can be defined for different actions. For example, a low coefficient may be used to warn staff, while a high coefficient may be used to immediately stop the scan due to a high level of PNS detected.

[0093] time domain Essentially, the gate mechanism is applied to read out any sensor only during the phase in which a PNS occurs. It is possible to know what time delay PNS is in the gradient, or to perform calibration, and the sensor data can be gated over a short period delayed from the time of the gradient. The magnitude of the sensor data in this gate window can then be used to determine the PNS, and to determine whether a PNS has occurred, it can be compared to the average signal over the entire period TR (or any part of TR) in a similar manner to that described above for the frequency domain, using an indicator that the signal over the gate time window is of some user-defined magnitude greater than the magnitude over the entire period TR (or other part of TR), again, to determine whether a PNS has occurred.

[0094] However, if specific sensor data shows a fixed phase relationship with the slope gradient over a continuous period TR, an algorithm can be used to correlate the number of sensor data and specific sensors within each period TR with the magnitude and number of established slope gradients. In other words, the algorithm establishes whether an elevated sensor signal appears at a certain time after the slope gradient, which can be used to indicate that the sensor data is associated with a PNS, automatically indicating that a PNS has been detected.

[0095] Camera as a motion sensor Cameras can be used to monitor patients. They can be used to monitor areas of the patient where PNS is known to occur. Since PNS motion is often subtle, the patient's clothing and coils can be designed with patterns that provide fine detail rather than large, uniform colored surfaces. High frame rates can be used with the camera, which can be beneficial for scans with short TR. By setting the camera frame rate to a multiple of 1 / TR after the Fourier transform, the advantage is that all signals attributable to PNS fall precisely into one bin of the output vector. However, this frame rate is not essential.

[0096] Position / Accelerometer Sensors as Motion Sensors: Known position or accelerometer sensors can be used as motion sensors. The accelerometer signal can be integrated once or twice over time to actually use patient velocity or position information. Accelerometer sensors are based on micro-electromechanical systems and semiconductors, and have the advantages of being MR compatible and very compact. Position sensors based on the optical fiber principle can be used because they are MR safe.

[0097] Therefore, one or more locations or acceleration sensors can be fixed to the patient, to clothing worn by the patient, or to the MR coil. In this case as well, they can be located in areas of the patient that are likely to receive PNS, such as the upper and lower back and the stomach.

[0098] MRI signals as sensors Many MRI sequences acquire image data in a way that often allows for the reconstruction of low-resolution MR images with a reduced number of dimensions. One example is a sequence that frequently acquires a one-dimensional projection of the field of view. This dynamic image data can then be used as a position sensor.

[0099] Non-contact sensor Non-contact sensors based on the patient's RF measurements can also be used as position sensors.

[0100] Adaptation of scan sequence If PNS is detected, a specific predetermined sequence adaptation can be performed "on the fly" during the scan to automatically reduce the gradient power to avoid the PNS. Thus, the steepest gradient and highest intensity of the gradient lobe may be reduced in several small steps until the PNS signal disappears. This mechanism reduces the gradient power only to the extent necessary to ensure that the PNS is avoided, the scan is maintained as quickly as possible, and individual patient PNS thresholds are taken into account. This can be very beneficial, as patient PNS thresholds are known to vary widely.

[0101] In another exemplary embodiment, a computer program or computer program element is provided, characterized in that it is configured to perform a method step of a method according to one of the embodiments described above on a suitable apparatus or system.

[0102] Accordingly, the computer program elements may be stored in a computer unit, which may be part of the embodiment. This computing unit may be configured to perform or trigger the execution of the steps of the method described above. Furthermore, it may be configured to operate the components of the apparatus and / or system described above. The computing unit may be configured to operate automatically and / or to execute user sequences. The computer program may be loaded into the working memory of the data processor. Accordingly, the data processor may be equipped to perform the method according to one of the embodiments described above.

[0103] This exemplary embodiment of the present invention encompasses both computer programs that use the present invention from the outset and computer programs that, through updates, transform existing programs into programs that use the present invention.

[0104] Furthermore, computer program elements can provide all the steps necessary to satisfy the procedure of the exemplary embodiment of the procedure described above.

[0105] According to a further exemplary embodiment of the present invention, a computer-readable medium such as a CD-ROM or USB stick is presented, having computer program elements stored thereon, which are described in the previous section.

[0106] Computer programs may be stored and / or distributed on suitable media such as optical storage media or solid-state media supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless communication systems.

[0107] However, computer programs may also be presented over a network such as the World Wide Web and downloaded from such a network into the working memory of a data processor. According to a further exemplary embodiment of the present invention, a medium is provided for making a computer program element available for download, and this computer program element is configured to perform a method according to one of the aforementioned embodiments of the present invention.

[0108] It should be noted that embodiments of the present invention are described with reference to different subject matter. In particular, some embodiments are described with reference to method-type claims, and other embodiments are described with reference to apparatus-type claims. However, unless otherwise notified, those skilled in the art will find that any combination of features belonging to one type of subject matter, as well as any combination of features relating to different subject matter, are gathered from the above and below descriptions and are deemed to be disclosed in this application. However, all features can be combined to provide a synergistic effect greater than the simple sum of the features.

[0109] The present invention is illustrated and described in detail in the drawings and the foregoing description, but such illustrations and descriptions should be considered illustrative or exemplary and not limiting. The present invention is not limited to the disclosed embodiments. Other variations of the disclosed embodiments can be understood and practiced by those skilled in the art in carrying out the claimed invention from a study of the drawings, disclosure and dependent claims.

[0110] In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite articles “a” or “an” do not exclude plurals. A single processor or other unit may fulfill the functions of several items enumerated in the claims. The mere fact that certain means are referenced in different dependent claims does not imply that combinations of these means cannot be used advantageously. No reference numeral in the claims should be construed as limiting in scope.

Claims

1. A device for detecting peripheral nerve stimulation, wherein the device is Input unit and At least one sensor, Processing unit and Output unit and It has, The input unit is configured to constitute a scan sequence of a magnetic resonance imaging (MRI) unit used to perform medical imaging of a patient, and to provide the processing unit with information about individual scans repeated at TR repetition times, wherein the information about the individual scans of the scan sequence has the duration of the individual scans. The at least one sensor is configured to acquire sensor data of the patient undergoing medical imaging. The processing unit is configured to select a portion of the sensor data over the acquisition period. The processing unit is configured to determine the presence of peripheral nerve stimulation PNS, and the determination of the presence of PNS involves utilizing information about the individual scans of the scan sequence and the sensor data of the patient, utilizing the duration of the individual scans and utilizing a portion of the sensor data over the acquisition period which is converted to the frequency domain. The processing unit is configured to select sensor data within the frequency domain at a center frequency equal to the reciprocal of the duration of each individual scan, and to determine the signal intensity of the sensor data within the frequency domain at the center frequency, and the determination of the presence of the PNS is based on the signal intensity. The output unit is configured to output an instruction that the presence of the PNS has been determined. Device.

2. The apparatus according to claim 1, wherein the conversion to the frequency domain involves the use of a Fourier transform.

3. The apparatus according to any one of claims 1 to 2, wherein the information relating to the individual scans of the scan sequence includes gradient waveform information for at least one gradient coil of the MRI unit, and the determination of the presence of the PNS includes the use of the gradient waveform information for the gradient coil of the MRI unit.

4. The apparatus according to claim 3, wherein the tilt waveform information for the tilt coil has at least one time of at least one tilt within the period of the individual scan, the processing unit is configured to determine a correlation between the time of the tilt within the period and the relevant sensor data of the sensor data within the period, and the determination of the presence of the PNS involves utilizing the correlation between the tilt and the relevant sensor data.

5. The apparatus according to claim 4, wherein the determination of the correlation between the tilt and the associated sensor data comprises determining a fixed phase relationship between the time of the tilt in a continuous period of individual scans and the time of the associated sensor data in a continuous period of individual scans.

6. An imaging system, Magnetic resonance imaging (MRI) unit, At least one sensor, Processing unit and It has, The MRI unit is configured to constitute a scan sequence of the MRI unit used to perform medical imaging of a patient, and to provide the processing unit with information about each scan repeated at a TR repetition time, and the information about each scan of the scan sequence has the duration of each scan. The at least one sensor is configured to acquire sensor data of the patient undergoing medical imaging. The processing unit is configured to select a portion of the sensor data over the acquisition period. The processing unit is configured to determine the presence of peripheral nerve stimulation PNS, and the determination of the presence of PNS involves utilizing information about the individual scans of the scan sequence and the sensor data of the patient, utilizing the duration of the individual scans and utilizing a portion of the sensor data over the acquisition period which is converted to the frequency domain. The processing unit is configured to select sensor data within the frequency domain at a center frequency equal to the reciprocal of the duration of each individual scan, and to determine the signal intensity of the sensor data within the frequency domain at the center frequency, and the determination of the presence of the PNS is based on the signal intensity. Imaging system.

7. The system according to claim 6, wherein, if the presence of a PNS is determined, the processing unit is configured to control the MRI unit to adjust the gradient power of the coils of the MRI unit.

8. A method for detecting peripheral nerve stimulation, wherein the method is: a) A step of configuring a scan sequence for a magnetic resonance imaging (MRI) unit used to perform medical imaging of a patient, and providing a processing unit with information about individual scans repeated at a TR repetition time, wherein the information about the individual scans of the scan sequence has a duration for each individual scan; b) The steps of acquiring sensor data of the patient undergoing medical imaging using at least one sensor, and selecting a portion of the sensor data over the acquisition period using the processing unit, c) A step of determining the presence of peripheral nerve stimulation PNS by the processing unit, comprising: utilizing information regarding the individual scans of the scan sequence and the sensor data of the patient; utilizing the duration of the individual scans; and utilizing a portion of the sensor data over the acquisition period which is converted to the frequency domain. The processing unit selects sensor data within the frequency domain at a center frequency equal to the reciprocal of the duration of each individual scan, and determines the signal intensity of the sensor data within the frequency domain at the center frequency, wherein the determination of the presence of the PNS is based on the signal intensity. d) A step of optionally outputting an instruction that the existence of the PNS has been determined. A method having

9. A computer program element for controlling the apparatus and / or the system described in any one of claims 1 to 5, wherein the computer program element is configured to perform the method described in claim 8 when executed by a processor.

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