Method and apparatus for detecting a needle in magnetic-resonance images

Abstract

A method for detecting a needle in magnetic-resonance images, wherein a needle artifact and/or a needle location is identified by means of algorithms based on artificial intelligence within the context of machine learning. Also, corresponding apparatus, control facility, and magnetic resonance tomography system.

Claims

1. A method for detecting a needle in magnetic-resonance images, wherein a needle artifact and/or a needle location is identified by means of algorithms based on artificial intelligence within the context of machine learning; the method comprising: providing location data on the location of a number of tracking slices; imaging the number of tracking slices in the form of magnetic-resonance images with the location data; identifying picture elements in a tracking slice depicting needle artifacts by means of a needle-artifact algorithm trained on needle artifacts; identifying a needle location by means of a needle-location algorithm trained on an estimation of the needle location from the identified picture elements in a tracking slice depicting needle artifacts; calculating a location of a monitoring slice based on the identified needle location; imaging the monitoring slice in its calculated location; and displaying the monitoring slice or further identifying the needle location.

2. The method as claimed in claim 1, wherein the further identifying the needle location comprises: identifying the picture elements in the monitoring slice depicting needle artifacts by means of a needle-artifact algorithm trained on needle artifacts; identifying the needle location by means of a needle-location algorithm trained on an estimation of the needle location based on the needle artifacts in the monitoring slice; and displaying the needle location or performing a further iteration to identify the needle location.

3. The method as claimed in claim 2, wherein the performing the further iteration to identify the needle location comprises: calculating the location of a number of tracking slices based on the identified needle location from the monitoring slice, wherein the number of tracking slices are aligned orthogonally to the identified needle location; calculating location data in accordance with the calculated location of this number of tracking slices; and repeating the calculating steps of the performing the further iteration in the form of an iteration.

4. The method as claimed in claim 2, further comprising using a simultaneous multislice imaging method for the imaging of the tracking slices or monitoring slices.

5. The method as claimed in claim 2, wherein the imaging the tracking slice or monitoring slice comprises imaging two or more contrasts for each slice, wherein at least one white marker contrast is imaged.

6. The method as claimed in claim 5, wherein the imaging of the contrasts comprises modifying a real-time pulse sequence in order to reduce the imaging time, and the method further comprises changing gradient moments of existing gradient objects in a slice-selection direction, a phase-encoding direction, or a readout direction.

7. The method as claimed in claim 1, further comprising: additionally identifying the needle location, in a tracking slice or monitoring slice, using imaging parameters, values for position vectors and normal vectors of the slices, the alignment of the slices relative to the BO field, field of view, or hardware parameters for gradient non-linearities.

8. The method as claimed in claim 2, further comprising: performing the identifying of the picture elements, in a tracking slice or monitoring slice, which depict needle artifacts by means of a deep-learning network; and subsequently improving, with a convolutional recurrent neural network, generating a U-net convolutional network for biomedical image segmentation from the respective slice a probability map that segments the needle artifact in the image and the segmentation.

9. (canceled)

10. (canceled)

11. (canceled)

12. A control facility for controlling a magnetic resonance tomography system, which is embodied to perform a method as claimed in claim 1.

13. (canceled)

14. (canceled)

15. A non-transitory computer program product with a computer program, which can be loaded directly into a storage facility of a control facility of a magnetic resonance tomography system, with program segments for executing the steps of the method as claimed in claim 1 when the computer program is executed in the control facility of the magnetic resonance tomography system.

16. A non-transitory computer-readable medium on which program segments that can be read and executed by a computer are stored in order to execute the steps of the method as claimed in claim 1, when the program segments are executed by the computer.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The disclosure is described again in more detail below with reference to the appended figures and with reference to exemplary embodiments. Herein, the same components are given identical reference characters in the different figures. The figures are generally not true to scale and show:

[0071] FIG. 1 a schematic depiction of a magnetic resonance tomography system according to an exemplary embodiment of the disclosure;

[0072] FIG. 2 a sketch depicting the slices required for the method;

[0073] FIG. 3 a flowchart for a possible sequence of a method according to the disclosure; and

[0074] FIG. 4 a preferred AI network.

[0075] The figures only show elements that are essential for the disclosure or helpful for understanding the disclosure.

DETAILED DESCRIPTION

[0076] FIG. 1 is a rough schematic depiction of a magnetic resonance tomography system 1. It includes on the one hand the actual magnetic resonance scanner 2 with an examination space 3 or patient tunnel in which a patient or test subject in whose body the examination object O and a needle N for a medical intervention is located is positioned on a couch 8.

[0077] The magnetic resonance scanner 2 is equipped in the usual manner with a basic field magnetic system 4, a gradient system 6 together with an RF transmitting antenna system 5 and an RF receiving antenna system 7. In the exemplary embodiment shown, the RF transmitting antenna system 5 is a whole-body coil permanently installed in the magnetic resonance scanner 2, whereas the RF receiving antenna system 7 consists of local coils to be arranged on the patient or test subject (here this is symbolized by only one single local coil). However, in principle, the whole-body coil can also be used as the RF receiving antenna system and the local coils as the RF transmitting antenna system as long as these coils can each be switched to different operating modes. Here, the basic field magnetic system 4 is embodied in the normal manner in that it generates a basic magnetic field in the longitudinal direction of the patient, i.e. along the longitudinal axis of the magnetic resonance scanner 2 extending in the z direction. The gradient system 6 includes in the usual manner individually controllable gradient coils to enable gradients to be switched in the x, y or z direction independently of one another. The magnetic resonance scanner 2 also contains shim coils (not shown) which can be embodied in the usual manner.

[0078] The magnetic resonance tomography system 1 shown in FIG. 1 is a whole-body system with a patient tunnel into which a patient can be completely introduced. However, in principle, the disclosure can also be used on other magnetic resonance tomography systems, for example with a laterally open, C-shaped housing. It is only essential for it to be possible to produce corresponding images of the examination object O.

[0079] The magnetic resonance tomography system 1 is configured to produce images of different slices. Imaging of individual slices is known to the person skilled in the art. To this end, generally gradient pulses are applied during a scan at exactly specified positions in time and with a precisely defined course over time. As a result, the examination object O is scanned in individual slices together with the needle N and the associated k-space. Here, two tracking slices T are shown which are arranged orthogonally to the needle N.

[0080] The magnetic resonance tomography system 1 furthermore comprises a central control facility 13, which is used to control the MR system 1. This central control facility 13 includes a sequence control unit 14. This controls the sequence of radio-frequency pulses (RF pulses) and gradient pulses as a function of a selected pulse sequence PS or a sequence of a plurality of pulse sequences for producing images of a plurality of slices in a volume region of interest of the examination object within a scanning session. Such a pulse sequence PS can, for example, be specified and parameterized as part of a scanning or control protocol. Usually, different control protocols for different scans or scanning sessions are stored in a memory 19 and can be selected by a user (and possibly amended if required) and then used to perform the scan.

[0081] To output the individual RF pulses of a pulse sequence PS, the central control facility 13 comprises a radio-frequency transmission facility 15, which generates and amplifies the RF pulses and feeds them via a suitable interface (not shown in detail) into the RF transmitting antenna system 5. To control the gradient coils of the gradient system 6 in order to switch the gradient pulses appropriately in accordance with the specified pulse sequence PS, the control facility 13 comprises a gradient system interface 16. Gradient pulses could be applied via this gradient system interface 16. The sequence control unit 14 communicates in a suitable manner, for example by transmitting sequence control data SD, with the radio-frequency transmission facility 15 and the gradient system interface 16 in order to execute the pulse sequence PS.

[0082] The control facility 13 also comprises a radio-frequency receiving facility 17 (which also communicates in a suitable manner with the sequence control unit 14) in order to receive magnetic resonance signals in a coordinated manner by means of the RF receiving antenna system 7 within the readout window specified by the pulse sequence PS and thus acquire the raw data.

[0083] Here, a reconstruction unit 18 accepts the acquired raw data and reconstructs magnetic resonance image data therefrom. This reconstruction is also generally performed on the basis of parameters that can be specified in the respective scan or control protocol P. This image data can then be stored in a memory 19, for example.

[0084] The manner by which suitable raw data can be acquired by the irradiation of RF pulses and the switching of gradient pulses and MR images or parameter maps reconstructed therefrom is known in principle to the person skilled in the art and will not, therefore, be explained in any more detail here.

[0085] To carry out the method according to the disclosure (see for example FIG. 3), the control facility 13 includes an apparatus 11 for detecting a needle N in magnetic-resonance images K. This apparatus communicates with the other components, in particular with the image reconstruction unit 16, the sequence control unit 14, or the gradient system interface 16, a memory 19 and preferably also a display unit 9 via a data interface 20.

[0086] This data interface 20 is configured to acquire location data LD provided on the location of a number of tracking slices T, which here it receives from the memory 19 or the terminal 11. The data interface 20 is furthermore used to output commands for imaging

a) a number of tracking slices T in the form of magnetic-resonance images K with the location data LD provided,
b) a monitoring slice M in its calculated location,

[0087] and preferably furthermore to output data for displaying the monitoring slice M and/or further identification of the needle location LN.

[0088] An artifact-identifying unit 21 is used to identify the picture elements in a tracking slice T that depict elements of the needle N, wherein the artifact-identifying unit 21 includes a needle-artifact algorithm NA.sub.T trained on needle artifacts in the tracking slice T. It can additionally comprise a needle-artifact algorithm NA.sub.M trained on needle artifacts in a monitoring slice M.

[0089] A needle-location identifying unit 22 is used to identify a needle location LN, wherein the needle-location identifying unit 22 comprises a needle-location algorithm NL.sub.T trained on an estimation of the needle location LN in the tracking slice T. It can additionally include a needle-location algorithm NL.sub.M trained on an estimation of the needle location LN in a monitoring slice M.

[0090] A slice-location calculating unit 23 is used to calculate the location of a slice, here at least one monitoring slice M, which is structured such that it includes the identified needle location LN.

[0091] The central control facility 13 can be operated via a terminal 10 with an input unit and a display unit 9 via which the entire magnetic resonance tomography system 1 can hence be operated by one operator. Magnetic resonance tomography images can also be displayed on the display unit 9 and scans can also be planned and started by means of the terminal.

[0092] The magnetic resonance tomography system 1 according to the disclosure and in particular the control facility 13 can moreover comprise a multiplicity of further components, which are not shown individually here but are usually present on such systems, such as, for example a network interface in order to connect the entire system to a network and to enable the exchange of raw data and/or image data or parameter maps and also further data, such as, for example, patient-related data or control protocols.

[0093] The manner by which suitable raw data can be acquired by the irradiation of RF pulses and the generation of gradient fields and magnetic resonance tomography images reconstructed therefrom is known in principle to the person skilled in the art and will not be explained in any more detail here. Similarly, a wide variety of scan sequences, such as, for example, EPI scan sequences or other scan sequences for the generation of diffusion-weighted images are known in principle to the person skilled in the art.

[0094] FIG. 2 is a perspective sketch depicting the slices required for the method. A needle N travels from left to right, wherein the tip of the needle N is indicated as an arrow head. Two tracking slices T are aligned orthogonally to the needle, wherein the needle passes through the tracking slices T at the penetration points D. These penetration points D are connected by a monitoring slice M. In the depiction, the monitoring slice M only includes the space between the two tracking slices T. This does not necessarily have to be the case since an MRI system is basically able to accommodate slices of any size (within the limits imposed by the system). Therefore, the tip of the needle N could also lie within the monitoring slice M and the endpoint of the needle N determined very accurately in the monitoring slice M in the case of needle-artifact determination and location determination.

[0095] FIG. 3 is a block diagram depicting the exemplary course of a method according to the disclosure for detecting a needle in magnetic-resonance images.

[0096] Step I entails the provision of location data LD on the location of two tracking slices T. This location data LD is initially estimations of the needle location and can, for example, be obtained by manually inputting or from data on the type of intervention.

[0097] Step II entails the imaging of the two tracking slices T in the form of two contrasts in each case with a magnetic resonance tomography system 1 as is depicted, for example, in FIG. 1. A needle artifact NA is indicated in one of the contrasts K. The other contrasts K also contain needle artifacts NA, which, for purposes of simplicity, are not additionally identified by reference characters. Herein, the tracking slices T are imaged with the location data LD provided.

[0098] Step III entails the identification of the pixels in a tracking slice T which depict needle artifacts NA by means of a needle-artifact algorithm NA.sub.T trained on needle artifacts NA. Herein, two probability maps W are obtained in which the penetration points D can be recognized.

[0099] Step IV entails the identification of a needle location LN by means of a needle-location algorithm NL.sub.T trained on an estimation of the needle location LN from the identified pixels which depict needle artifacts NA in the two tracking slices T.

[0100] Step V entails the calculation of the location of a monitoring slice M which is structured such that it includes the identified needle location LN.

[0101] Step VI entails the imaging of a monitoring slice M in its calculated location.

[0102] Step VII entails displaying the monitoring slice.

[0103] Step VIII entails the identification of the pixels in the monitoring slice M, which depict needle artifacts NA by means of a further needle-artifact algorithm NA.sub.M trained on needle artifacts NA in the monitoring slice. Since the needle artifacts NA are generally structured differently in the monitoring slice than in the tracking slice, although the needle-artifact algorithm NA.sub.M generally operates here in accordance with the same basic principle as that used above with the tracking slice, it was trained differently. Here once again, a probability map is created.

[0104] Step IX entails the identification of the needle location LN by means of a needle-location algorithm NL.sub.M trained on an estimation of the needle location based on the segmented needle artifacts NA in the monitoring slice M.

[0105] Step X entails displaying the needle location LN in the monitoring slice M.

[0106] Step XI entails the calculation of the location of a further two tracking slices T1 based on the identified needle location LN from monitoring slice M, wherein the number of the further two tracking slices T1 are aligned orthogonally to the identified location of the needle.

[0107] This calculated location is used as location data LD for a repetition of the method in the context of an iteration step. This iteration can be performed multiple times, for example until a limit value for the difference in the values for the needle location between two successive iterations is fallen below.

[0108] FIG. 4 shows a preferred AI network for the determination of needle artifacts NA in slices, which in this example are two tracking slices T. The left-hand side shows two contrasts K, which were produced in the context of the imaging of the two tracking slices T. This first entails the identification of the pixels in a contrast K which depict elements of the needle N by means of a U-net convolutional network U for biomedical image segmentation. This U-net convolutional network U generates from the respective contrast (i.e. from the respective tracking slice T) a probability map W that segments the needle artifact NA in the image. Here, it may be seen that, in addition to the needle artifact NA, the probability map W contains further striated structures. These depict errors, which are eliminated in a further step by means of a convolutional recurrent neural network R. This improves segmentation. These two networks together produce the needle-artifact algorithm NA.sub.T.

[0109] In a subsequent step, possibly with additional scan parameters P (for example from a DICOM header), the location of the needle is identified with a needle-location algorithm NL.sub.T depicting a further deep-learning network, for example a convolutional network).

[0110] Finally, reference is made once again to the fact that the method described in detail above and the magnetic resonance tomography system 1 are exemplary embodiments only and can be modified by the person skilled in the art in a variety of ways without leaving the scope of the disclosure. Furthermore, the use of the indefinite articles a or an does not exclude the possibility that the features in question may also be present on a multiple basis. Similarly, the terms unit and module do not preclude the possibility that the components in question consist of a plurality of interacting partial components, which could also be spatially distributed.