Patient-model-based determination of a stimulation of a magnetic resonance imaging

Abstract

A method for determining peripheral nerve stimulation during MR imaging of a patient in a MR scan unit for a MR pulse sequence is described. In the method, a plurality of model-based candidate stimulations are determined dependent on a unit vector potential of the gradient magnet field generated during MR imaging and dependent on candidate data models for different object parameter values. A model-based candidate data stimulation is selected as a stimulation model for the patient dependent on an individual patient model. A distribution of a vector potential of a gradient magnetic field acting on the patient is determined as a function of a unit gradient current for a determined position of the patient in the MR scanning unit. The nerve stimulation of the patient is determined for the determined position based on the selected candidate stimulation and a gradient current of a gradient pulse of the MR pulse sequence.

Claims

1. A method for determining a peripheral nerve stimulation during a magnetic resonance (MR) imaging of a patient in a MR scan unit for a MR pulse sequence, the method comprising: determining, by a stimulation determining device, a plurality of model-based candidate stimulations as a function of a unit vector potential of a gradient magnetic field generated during the MR imaging and candidate models for different object parameter values; selecting, by the stimulation determining device, a model-based candidate stimulation as a stimulation model for the patient depending on an individual patient model for the patient, which is determined depending on the object parameter values of the patient; determining, by the stimulation determining device, a distribution of a vector potential of the gradient magnetic field acting on the patient, generated during MR imaging, as a function of a unit gradient current for a determined position of the patient in the MR scan unit; and determining, by the stimulation determining device, the peripheral nerve stimulation of the patient for the determined position based on the selected model-based candidate stimulation, the determined vector potential acting on the patient, and a gradient current of a gradient pulse of the MR pulse sequence.

2. The method of claim 1, wherein, in the determining of the model-based candidate stimulations, a virtual interface cylinder is placed around a candidate model and location-dependent stimulation values for partial surfaces of the virtual interface cylinder are determined.

3. The method of claim 2, wherein the determined model-based candidate stimulations are determined based on a sum of the plurality of model-based candidate stimulations assigned to the partial surfaces of the virtual interface cylinder.

4. The method of claim 1, wherein the individual patient model is generated as a function of one or more individual object parameter values comprising: a size of the patient, a weight of the patient, a fat distribution of the patient, a muscle distribution of the patient, a body mass index of the patient, a sex of the patient, or combinations thereof.

5. The method of claim 1, wherein the stimulation of the patient is determined for the determined position by multiplication of: (1) the selected model-based candidate stimulation, (2) the determined vector potential acting on the patient, and (3) the gradient current of the gradient pulse of the MR pulse sequence.

6. The method of claim 1, wherein the distribution of a vector potential acting on the patient is arranged on a cylindrical surface around the patient.

7. The method of claim 1, wherein the individual patient model is selected for individual MR imaging by comparing individual patient data with the candidate models.

8. The method of claim 7, wherein the individual patient data comprises the object parameter values of the patient.

9. The method of claim 7, wherein, when none of the candidate models correspond to the individual patient data with sufficient accuracy, an interpolation between two candidate models is performed and an interpolated candidate model is generated as a patient model.

10. The method of claim 1, further comprising: determining a maximum gradient strength of the MR pulse sequence, at which an allowed maximum patient stimulation is not exceeded; and adapting the MR pulse sequence to the determined maximum gradient strength.

11. The method of claim 10, further comprising: generating the MR pulse sequence, gradients of which do not exceed the allowed maximum patient stimulation; stimulating an area to be examined of the patient using the generated MR pulse sequence; acquiring magnetic resonance signals; and reconstructing image data based on the acquired magnetic resonance signals.

12. A stimulation determination device comprising: a candidate model determination unit configured to determine a plurality of model-based candidate stimulations as a function of a unit vector potential and candidate models for different patients; a selection unit configured to select a model-based candidate stimulation as a stimulation model for a patient depending on an individual patient model for the patient, which is determined depending on object parameter values of the patient; a vector potential determination unit configured to determine a distribution of a vector potential of a gradient magnetic field, acting on the patient, generated during magnetic resonance (MR) imaging as a function of a unit gradient current for a determined position of the patient in a MR scan unit; and a stimulation determination unit configured to determine a nerve stimulation of the patient for the determined position based on the selected model-based candidate stimulation, the determined vector potential acting on the patient, and a gradient current of a gradient pulse of a MR pulse sequence.

13. A pulse sequence optimization device comprising: an input interface configured to receive parameter values of a patient and a position of the patient in a scanner of a magnetic resonance (MR) system; a stimulation determining device configured to: determine a plurality of model-based candidate stimulations as a function of a unit vector potential and candidate models for different patients; select a model-based candidate stimulation as a stimulation model for the patient depending on an individual patient model for the patient, which is determined depending on object parameter values of the patient; determine a distribution of a vector potential of a gradient magnetic field, acting on the patient, generated during MR imaging as a function of a unit gradient current for a determined position of the patient in the scanner of the MR system; and determine a nerve stimulation of the patient for the determined position based on the selected model-based candidate stimulation, the determined vector potential acting on the patient, and a gradient current of a gradient pulse of a pulse sequence; a gradient strength determination unit configured to determine a maximum gradient current strength of the pulse sequence, in which an allowed maximum patient stimulation is not exceeded, based on the determined patient stimulation; and a pulse sequence optimization unit configured to adapt the pulse sequence to the determined maximum gradient strength based on the determined maximum gradient current strength.

14. A magnetic resonance (MR) imaging system comprising: a high-frequency transmission system; a gradient system; a control device configured to control the high-frequency transmission system and the gradient system based on a pulse sequence in order to carry out a desired measurement; and a pulse sequence optimization device configured to adapt the pulse sequence to a patient, the pulse sequence optimization device comprising: an input interface configured to receive parameter values of the patient and a position of the patient in a scanner of the MR imaging system; a stimulation determining device configured to: determine a plurality of model-based candidate stimulations as a function of a unit vector potential and candidate models for different patients; select a model-based candidate stimulation as a stimulation model for the patient depending on an individual patient model for the patient, which is determined depending on object parameter values of the patient; determine a distribution of a vector potential of a gradient magnetic field, acting on the patient, generated during MR imaging as a function of a unit gradient current for a determined position of the patient in the scanner of the MR imaging system; and determine a nerve stimulation of the patient for the determined position based on the selected model-based candidate stimulation, the determined vector potential acting on the patient, and a gradient current of a gradient pulse of the pulse sequence; a gradient strength determination unit configured to determine a maximum gradient current strength of the pulse sequence, in which an allowed maximum patient stimulation is not exceeded, based on the determined nerve stimulation of the patient; and a pulse sequence optimization unit configured to adapt the pulse sequence to the determined maximum gradient current strength.

15. The stimulation determination device of claim 12, wherein, in the determination of the model-based candidate stimulations, a virtual interface cylinder is configured to be placed around a candidate model and location-dependent stimulation values for partial surfaces of the virtual interface cylinder are determined.

16. The stimulation determination device of claim 15, wherein the determined model-based candidate stimulations are determined based on a sum of the plurality of model-based candidate stimulations assigned to the partial surfaces of the virtual interface cylinder.

17. The stimulation determination device of claim 12, wherein the stimulation of the patient is configured to be determined for the determined position by multiplication of: (1) the selected model-based candidate stimulation, (2) the determined vector potential acting on the patient, and (3) the gradient current of the gradient pulse of the MR pulse sequence.

18. The stimulation determination device of claim 12, wherein the individual patient model is configured to be selected for individual MR imaging by comparing individual patient data with the candidate models.

19. The stimulation determination device of claim 18, wherein, when none of the candidate models correspond to the individual patient data with sufficient accuracy, an interpolation between two candidate models is configured to be performed and an interpolated candidate model is configured to be generated as a patient model.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The disclosure is explained in more detail below with reference to the attached figures based on exemplary embodiments, in which:

(2) FIG. 1 depicts a flowchart, which illustrates a method for determining a peripheral nerve stimulation during MR imaging of a patient in a MR scanning unit for a MR pulse sequence according to an embodiment.

(3) FIG. 2 depicts a flowchart, which illustrates a gradient strength determination method according to an embodiment.

(4) FIG. 3 depicts a flowchart, which illustrates a magnetic resonance imaging method according to an embodiment.

(5) FIG. 4 depicts a block diagram, which illustrates a stimulation determination device according to an embodiment.

(6) FIG. 5 depicts a block diagram, which illustrates a pulse sequence optimization device according to an embodiment.

(7) FIG. 6 depicts a schematic representation of a magnetic resonance imaging system according to an embodiment.

(8) FIG. 7 depicts a representation of the concept of the virtual cylindrical interface surface for calculating a PNS response to a unit vector potential according to an embodiment.

(9) FIG. 8 depicts a representation of the concept of the cylindrical surface for calculating a vector potential based on a unit gradient current in connection with the virtual cylindrical interface surface illustrated in FIG. 7.

DETAILED DESCRIPTION

(10) In order to make the object, technical solutions, and advantages of the present disclosure more apparent, the present disclosure will be further described in detail by way of embodiments hereinafter.

(11) FIG. 1 shows a flow chart 100, which illustrates a method for determining peripheral nerve stimulation during MR imaging of a patient in a MR scanning unit for a MR pulse sequence according to an embodiment. In act 1.I, a number of models for candidate stimulations PNS (A.sub.e (x.sub.i)) are first calculated for different patient parameter values PM as a function of a unit vector potential A.sub.e (x.sub.i). Here, x.sub.i are the positions x.sub.i on a surface of a virtual interface cylinder IC around a patient area in a scanning unit. The patient parameter values can include, (e.g., the height of patient P, sex of the patient, fat distribution of the patient, muscle distribution of the patient, body mass index (BMI) of the patient, and the like). In accordance with this data, an individual model, (e.g., generated by machine learning), is used, with which a model-specific distribution of the peripheral candidate stimulation values PNS (A.sub.e (x.sub.i)) is calculated. The distribution of the peripheral candidate stimulation values PNS (A.sub.e (x.sub.i)) is calculated on the surface of a virtual interface cylinder IC surrounding a patient area. This calculation is still independent of the actual patient position because the calculation takes place independently of a specific patient P and is stored beforehand in a database. The candidate stimulation data PNS (A.sub.e (x.sub.i)) are calculated using a grid, which includes values of the unit vector potential A.sub.e (x.sub.i) evenly distributed over the interface cylinder IC and are stored in matrix form. Because the actual MR measurement, its protocol, and the position of the patient P in the scan unit of the MR system used for the MR measurement are not yet known when the candidate stimulation data PNS (A.sub.e (x.sub.i)) are stored in advance, the calculation of the candidate stimulation values PNS (A.sub.e (x.sub.i)) is based on unit vectors A.sub.e (x.sub.i) of a vector potential of a magnetic field or a gradient magnetic field.

(12) In act 1.II, depending on the known parameter values of a specific patient P, a suitable patient model PM.sub.ind is selected and thus the candidate stimulation data PNS (A.sub.e (x.sub.i)) assigned to this patient model PM.sub.ind is selected as a suitable stimulation model PNS.sub.P.

(13) In act 1.III, a distribution of a vector potential A (x.sub.i, I.sub.e, x.sub.P) of the gradient magnetic field generated during the MR imaging, acting on the patient P is calculated as a function of a unit gradient current I.sub.e for a determined position x.sub.P of the patient P in the MR scan unit 2 (see FIG. 6) and the individual patient model PM.sub.ind. These values are also calculated on a long cylindrical surface CS, which has the same radius and circumference as the interface cylinder surface IC and is aligned with this when viewed in the axial direction, but already takes the patient position x.sub.P into account. The values of this vector potential A (x.sub.i) are also calculated as discrete values over a grid over the cylindrical surface CS and stored in matrix form. The vector potential A (x.sub.i) is calculated as a function of a unit gradient current I.sub.e.

(14) In act 1.IV, the two matrices PNS (A.sub.e (x.sub.i)) and A (x.sub.i) are finally multiplied with one another and also multiplied by a gradient current value in order to obtain the actual peripheral patient stimulation. The gradient current value for a MR protocol may, if necessary, be adjusted in such a way that the peripheral patient stimulation PNS does not exceed a predetermined value. The patient stimulation PNS.sub.tot, which acts on the entire body and is related to a unit vector potential A.sub.e (x.sub.i), is given by
PNS.sub.tot=Σ.sub.e,x.sub.iPNS(A.sub.e(x.sub.i)  (1)

(15) Here, e stands for the three gradient axes x, y, z. The value x.sub.i indicates the positions of the grid fields of the virtual interface cylinder IC. As already mentioned, this patient stimulation is multiplied by the matrix of the vector potential A(x.sub.i) and the gradient current value of a gradient pulse of a pulse sequence PS, which results in the already mentioned peripheral patient stimulation PNS.

(16) FIG. 2 shows a flow chart 200, which illustrates a gradient strength determination method according to an embodiment. In act 2.I, a patient stimulation PNS dependent on a gradient strength I is calculated for a predetermined pulse sequence PS using the method illustrated in FIG. 1. Then, in act 2.II, a maximum gradient strength I.sub.max of the pulse sequence PS is determined, at which a permitted maximum patient stimulation PNS.sub.max is not exceeded. Finally, the pulse sequence PS is configured to the determined maximum gradient strength I.sub.max, with gradient pulses and gradient currents being dimensioned accordingly in such a way that the determined limits are observed.

(17) FIG. 3 shows a flow chart 300, which illustrates a magnetic resonance imaging method according to an embodiment. In act 3.I, a pulse sequence PS is generated, the gradients of which do not exceed a predetermined maximum patient stimulation PNS.sub.max. This calculation is carried out by the gradient strength determination method shown in FIG. 2. The actual MR imaging process then begins in act 3.II, wherein an area FOV of a patient P to be examined is scanned by irradiating the generated pulse sequence PS. The magnetic resonance signals RD generated in the process are acquired in act 3.III and image data BD are reconstructed based on the acquired magnetic resonance signals RD.

(18) FIG. 4 illustrates a stimulation determination device 40 according to an embodiment. The stimulation determination device 40 also includes a candidate model determination unit 41 for determining a plurality of model-based candidate stimulations PNS.sub.tot(PM) as a function of a unit vector potential A.sub.e (x.sub.i) and candidate models PM (PPW) for different patients P with different patient parameter values PPW. The candidate models PM (PPW) are determined for a plurality of individual information for a measurement as well as different patient parameter values PPW. The candidate stimulations PNS.sub.tot(PM) determined based on these candidate models PM (PPW) are subsequently stored in a data memory 41a. Part of the stimulation determination device 40 is also a selection unit 42 for selecting a model-based candidate data stimulation PNS.sub.tot(PM) as a stimulation model PNS.sub.P for an individual patient P as a function of an individual patient model PM.sub.ind for this individual patient P, which is determined depending on the patient parameter values PPW.sub.ind of the individual patient P. The individual patient model PM.sub.ind is determined based on entered patient parameter values PPW.sub.ind by the candidate model determination unit 41 and forwarded to the selection unit 42. The stimulation determination device 40 also includes a vector potential determination unit 43 for determining a distribution of a vector potential A (x.sub.i, I.sub.e) that acts on the patient P and that is generated during the MR imaging by the gradient magnetic field B, as a function of a unit gradient current I.sub.e for an individual position x.sub.P of the individual patient P. Finally, the stimulation determination device 40 also includes a stimulation determination unit 44 for determining the nerve stimulation PNS of an individual patient P for the determined position x.sub.P based on a multiplication of the stimulation patient stimulation PNS.sub.tot(PM.sub.ind, A.sub.e) selected from the candidate stimulation models PNS.sub.tot(PM) with the determined vector potential A (x.sub.i, I.sub.e) acting on the individual patient P and with a gradient current I of a gradient pulse GP of a MR pulse sequence.

(19) FIG. 5 shows a pulse sequence optimization device 50 according to an embodiment. The pulse sequence optimization device 50 has an input interface 51 for receiving parameter values PPW of a patient P and a position x.sub.P of the patient P in a scan unit in a MR system. In addition, the pulse sequence optimization device 50 includes a stimulation determination device 40, as shown in FIG. 4. The nerve stimulation PNS determined by the stimulation determination device 40 is transmitted to a gradient strength determination unit 52, which is also part of the pulse sequence optimization device 50 and is set up to determine a maximum gradient current strength I.sub.max of a pulse sequence PS provided for a measurement, in which a permitted maximum patient stimulation PNS.sub.max is not exceeded, based on the determined patient stimulation PNS. The determined maximum gradient current strength I.sub.max is transmitted to a pulse sequence optimization unit 53 which is also included by the pulse sequence optimization device 50 and which is set up to adapt the pulse sequence PS of a MR measurement of an individual patient P to the determined maximum gradient current strength I.sub.max. In particular, the amplitudes of the gradients of the pulse sequence PS is adapted in accordance with the determined maximum gradient current intensity I.sub.max. This may lead to an increase in the amplitudes of the gradients as well as a decrease. An increase may lead to improved image quality, while by a decrease the maximum permitted patient stimulation is maintained.

(20) A magnetic resonance system 1 (hereinafter referred to as “MR system” for short) is roughly schematically shown in FIG. 6. The MR system 1 includes the magnetic resonance scanner 2 with an examination zone 3 or patient tunnel in which a patient P, or here a patient or test person, for example, in whose body there is a certain organ, is on a couch 8, may be brought.

(21) The magnetic resonance scanner 2 may be equipped with a basic field magnet system 4, a gradient system 6, a radio-frequency (RF) transmission antenna system 5, and a RF reception antenna system 7. In the embodiment shown, the RF transmission antenna system 5 is a whole-body coil permanently installed in the magnetic resonance scanner 2, whereas the RF reception antenna system 7 includes local coils to be arranged on the patient or test person (in FIG. 6 symbolized only by a single local coil). In principle, however, the whole-body coil may also be used as an RF receiving antenna system and the local coils as an RF transmitting antenna system, provided that these coils may each be switched to different modes of operation.

(22) The MR system 1 also has a central control device 13, which is used to control the MR system 1. This central control device 13 includes a sequence control unit 14 for pulse sequence control. This is used to control the sequence of high-frequency pulses (RF pulses) and gradient pulses depending on a selected imaging sequence PS. Such an imaging sequence may be specified within a measurement or control protocol PR. Different control protocols PR for different measurements may be stored in a memory 19 and may be selected by an operator (and changed if necessary) and then used to carry out the measurement. Before the control protocols PR are sent to the sequence control unit 14, they are sent to a pulse sequence optimization device 50 for optimization. The pulse sequence optimization device 50 modifies gradient parameters in a received protocol PR or the pulse sequence PS based thereon, as explained in connection with FIG. 2 and FIG. 5 and sends the optimized control protocol PR.sub.opt to the sequence control unit 14 for pulse sequence control.

(23) To output the individual RF pulses, the central control device 13 has a high-frequency transmission device 15, which generates the RF pulses, amplifies the RF pulses, and feeds the RF pulses into the RF transmission antenna system 5 via a suitable interface (not shown in detail). To control the gradient coils of the gradient system 6, the control device 13 has a gradient system interface 16. The sequence control unit 14 communicates in a suitable manner, (e.g., by sending out sequence control data SD), with the high-frequency transmission device 15 and the gradient system interface 16 for sending out the pulse sequences PS. The control device 13 also has a high-frequency receiving device 17 (likewise communicating in a suitable manner with the sequence control unit 14) in order to acquire magnetic resonance signals, (e.g., raw data RD), in a coordinated manner, which magnetic resonance signals have been received from the RF transmitting antenna system 7. A reconstruction unit 18 takes over the acquired raw data RD and reconstructs the MR image data BD therefrom. These image data BD may then be stored in a memory 19, for example.

(24) The central control device 13 may be operated via a terminal with an input unit 10 and a display unit 9, via which the entire MR system 1 may thus also be operated by an operator. MR images may also be displayed on the display unit 9, and measurements may be planned and started using the input unit 10, if necessary in combination with the display unit 9, and in particular suitable control protocols with suitable measurement sequences such as explained above may be selected and modified, if necessary.

(25) The MR system 1 and, in particular, the control device 13 may also have a large number of other components that are not shown in detail here but may be present on such devices, such as a network interface to connect the entire system to a network and to be able to exchange raw data RD and/or image data BD or parameter maps, but also other data, such as patient-relevant data or control protocols.

(26) How suitable raw data RD may be acquired by irradiating RF pulses and generating gradient fields and how MR images BD may be reconstructed therefrom is known in principle to the person skilled in the art and is not explained in more detail here. There are also a wide variety of measurement sequences, such as EPI sequences, GRE measurement sequences, or TSE measurement sequences for generating dynamic or static images, which are in principle well known to the person skilled in the art.

(27) FIG. 7 shows a schematic representation 70 of the calculation of a candidate stimulation PNS.sub.tot(PM). Stimulation values PNS (A.sub.e (x.sub.i)) are calculated for an individual candidate model PM.sub.ind on an interface cylinder surface IC as a function of unit vector potentials A.sub.e (x.sub.i). If such a candidate model is selected for a MR imaging for an individual patient P, the stimulation values PNS (A.sub.e (x.sub.i) are multiplied with a vector potential A (x.sub.i) that is dependent on the patient position x.sub.P of the specific patient P in a MR scan unit.

(28) This process is illustrated in FIG. 8 in a schematic representation 80. A vector potential A (x, y, z, I) of a magnetic field, as it is formed in a scanning unit during MR imaging in a recording area around a cylindrical surface CS, is shown there. Furthermore, an interface cylinder surface IC is drawn in, which has grid areas to which unit vectors A.sub.e(x.sub.i) of the vector potential A (x, y, z, I) are assigned.

(29) Finally, it is pointed out once again that the methods and devices described above are exemplary embodiments of the disclosure and that the disclosure may be varied by a person skilled in the art without departing from the scope of the disclosure, insofar as it is specified by the claims. The method and the magnetic resonance imaging system were explained primarily based on an application for recording medical image data. However, the disclosure is not restricted to use in the medical field, but rather the disclosure may also be applied to the recording of images for other purposes. For the sake of completeness, it is also pointed out that the use of the indefinite article “a” or “an” does not exclude the possibility that the relevant features may also be present several times. Likewise, the term “unit” does not exclude that it includes several components, which may also be spatially distributed.

(30) It is to be understood that the elements and features recited in the appended claims may be combined in different ways to produce new claims that likewise fall within the scope of the present disclosure. Thus, whereas the dependent claims appended below depend from only a single independent or dependent claim, it is to be understood that these dependent claims may, alternatively, be made to depend in the alternative from any preceding or following claim, whether independent or dependent, and that such new combinations are to be understood as forming a part of the present specification.

(31) While the present disclosure has been described above by reference to various embodiments, it may be understood that many changes and modifications may be made to the described embodiments. It is therefore intended that the foregoing description be regarded as illustrative rather than limiting, and that it be understood that all equivalents and/or combinations of embodiments are intended to be included in this description.

REFERENCE NUMBERS

(32) 1 magnetic resonance system 2 MR scanning unit 3 examination zone 4 basic field magnet system 5 RF transmission antenna system 6 gradient system 7 RF receiving antenna system 8 bed 9 Display unit 10 input unit 13 central control device 14 sequence control unit 15 radio frequency transmitter 16 gradient system interface 17 radio frequency receiving equipment 18 reconstruction unit 19 memory 40 stimulation detection device 41 candidate model determination unit 41a data storage 42 selection unit 43 vector potential determination unit 44 stimulation determination unit 50 pulse sequence optimization device 51 input interface 52 gradient strength determination unit 53 pulse sequence optimization unit 70 illustration of the calculation of a candidate stimulation 80 schematic representation of a vector potential A (x.sub.i) vector potential A.sub.e (x.sub.i) unit vector A (x.sub.i, I.sub.e, x.sub.p) vector potential acting on the patient as a function of a unit gradient current and the position of a patient A (x, y, z, I) current-dependent vector potential BD image data CS cylindrical surface GP gradient pulse IC interface cylinder I.sub.e unit gradient current I gradient current P patient PM patient parameter value PM.sub.ind patient model PM (PPW) candidate model PNS (A.sub.e (x.sub.i)) model for candidate stimulations PNS.sub.max maximum patient stimulation PNS.sub.P stimulation model for an individual patient PNS.sub.tot patient stimulation PNS.sub.tot (PM) model-based candidate stimulation PNS.sub.tot (PM.sub.ind, A.sub.e) selected patient stimulation PPW parameter value of a patient PPW.sub.ind patient parameter value of an individual patient PR measurement or control protocol PR.sub.opt optimized control protocol PS pulse sequence RD magnetic resonance signals/raw data SD sequence control data x.sub.p position