Apparatus and method for detecting critical stations in a multi-station scan and magnetic resonance imaging system

Abstract

The invention relates to multi-station scanning. To improve selection of stations, an apparatus for detecting critical stations in a multi-station scan is provided. The apparatus comprises an input unit, a processing unit and an output unit. The input unit is configured to receive image data acquired from a patient lying on a table before starting a diagnostic scan with a magnetic resonance imaging system. The processing unit is configured to analyze the image data of the patient to identify a spatial location of a lung of the patient, to align the spatial location of the lung of the patient with a planned multi-station scan to identify critical stations potentially affected by respiratory motion of the patient, and to assign breath holds to the identified critical stations. The output unit is configured to provide the identified critical stations. Thus, selection of critical stations can be automatically and consistently fulfilled without operator intervention. Tedious and time-consuming steps of manually identifying and selecting stations potentially affected by respiratory motion can be avoided.

Description

Technical Field

[0001] This invention relates to multistation scanning, and more particularly to apparatus for detecting key stations in multistation scanning, magnetic resonance imaging systems, methods for detecting key stations in multistation scanning, computer program units, and computer-readable media. Background Technology

[0002] For whole-body imaging, a common approach is multi-station scanning, where different parts of the body in the longitudinal direction are measured sequentially and cascaded after each scan. This is to identify stations where breath-holding marking may be affected by respiratory movements.

[0003] The selection of stations potentially affected by respiratory movements is currently done manually by technicians based on visual inspection. This is a cumbersome and time-consuming process, contributing to the hospital's limited patient throughput.

[0004] US2014 / 0084921A1 discloses an automatic multi-station MR imaging method using an MR overview image composed of several partial images.

[0005] Zangos et al. disclosed a method for multi-station MR imaging that relies on (whole-body) localization images in their January 1, 2015 Internet publication, “First experiences with the Whole-Body Dot Engine” (URL: http: / / clinical-mri.com / first-experiences-with-the-whole-body-dot-engine). This was also disclosed by Reiner et al. in their January 1, 2016 Internet publication, 'Whole-Body Dot Engine: First Clinical Experience with Automated Chest, Abdomen and PelvisExaminations' (Magnetom Flash).

[0006] JP4822834 discloses that breath-holding is used to reduce artifacts at specific stations in multi-station imaging affected by respiratory motion.

[0007] EP3719520A2 discloses the use of an optical camera in magnetic resonance imaging for patient location identification. Summary of the Invention

[0008] The choice of site may need to be improved.

[0009] The object of the invention is achieved by the subject matter of the independent claims, wherein further embodiments are incorporated in the dependent claims. It should be noted that the aspects described below also apply to apparatus for detecting key stations in multi-station scanning, MRI systems, methods for detecting key stations in multi-station scanning, computer program units, and computer-readable media.

[0010] According to a first aspect of the invention, an apparatus for detecting critical stations in a multi-station scan is provided. The apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to receive image data acquired from a patient lying on a worktable before a diagnostic scan is initiated using a magnetic resonance imaging system. The processing unit is configured to: analyze the received image data of the patient to identify the spatial location of the patient's lungs; align the spatial location of the patient's lungs with a planned multi-station scan to identify critical stations potentially affected by the patient's respiratory movements; and assign breath-holding to the identified critical stations. The image data includes optical images captured by an optical camera. The output unit is configured to provide the identified critical stations.

[0011] In other words, an automated analysis is proposed, for example, of optical images acquired directly from the patient before the stage is moved into the chest cavity, or acquired from the patient while in the chest cavity, to detect areas at risk of being affected by respiratory motion (typically in and around the lungs). These areas are compared with different stations on the scan, and potentially affected stations are identified and assigned to breath-holding.

[0012] Optical cameras can use visible light and / or near-infrared light to image a patient. In some examples, the optical camera may be a standard camera used to capture two-dimensional optical images of the patient. In other examples, the optical camera may be a rangefinder camera used to capture depth images of the patient.

[0013] Therefore, the selection of critical stations can be met automatically and consistently without operator intervention. This avoids the tedious and time-consuming process of manually identifying and selecting stations potentially affected by respiratory movements. This will be discussed below, and particularly regarding... Figure 2 The example shown will be explained.

[0014] According to an embodiment of the present invention, the image data includes optical images captured by an in-bore optical camera.

[0015] An example of an in-bore optical sensor is the Philips VitalEye. The in-bore optical camera can be positioned inside the upper left plastic housing and focused on the upper part of the object.

[0016] According to an embodiment of the present invention, the processing unit is configured to identify the spatial location of the patient's lungs by locating the thoracic cavity based on the optical image using marker detection.

[0017] The spatial location of the lungs can be identified using a body model approximating the surface parametric parameters, skeletal detection methods, or 3D registration with an annotated average 3D body model. This will be discussed below, and particularly regarding... Figure 2 The example shown will be explained.

[0018] According to an embodiment of the invention, the processing unit is configured to apply a safety margin around the identified spatial location of the lung to cover portions of the body potentially affected by the patient's respiratory movements.

[0019] The areas at risk of being affected by respiratory movements are typically in and around the lungs. In some cases, a safety margin can cover other parts of the body that may also be affected by respiratory movements, such as the abdomen.

[0020] According to a second aspect of the present invention, a magnetic resonance imaging (MRI) system is provided, comprising: An MRI scanner, which has a bore and a worktable for supporting the patient; An optical camera is configured to capture optical images of the patient lying on the worktable before a diagnostic scan is initiated using the MRI scanner; The device for detecting key stations in multi-station scanning, based on the first aspect and any associated examples; and The patient instruction device is configured to provide respiratory instructions for detected key stations.

[0021] In some examples, the patient instruction device may be a speaker used to provide automated voice prompts to the patient to inhale, exhale, and hold their breath.

[0022] In some examples, the patient instruction device may be an intracavitary display that provides visual instructions to prompt the patient to inhale, exhale, and hold their breath.

[0023] According to an embodiment of the present invention, the optical camera includes an in-bore optical camera.

[0024] According to an embodiment of the present invention, the MRI system further includes a controller configured to adjust a set of scanning parameters for each station and / or apply different sets of scanning parameters for different stations based on the actual anatomical structures.

[0025] According to a third aspect of the present invention, a method for detecting key stations in a multi-station scan is provided, comprising: a) Receive image data acquired from a patient lying on a worktable before a diagnostic scan is initiated using a magnetic resonance imaging system, wherein the image data includes optical images captured by an optical camera; b) Analyze the patient's image data to identify the spatial location of the patient's lungs. c) Align the spatial location of the patient's lungs with a planned multi-station scan to identify key stations potentially affected by the patient's respiratory movements, and assign breath-holding to the identified key stations; and d) Provide the identified key stations.

[0026] According to an embodiment of the invention, step b) further includes applying a safety margin around the identified spatial location of the lung to cover portions of the body also affected by the patient's respiratory movements.

[0027] According to an embodiment of the present invention, the method further includes providing breathing instructions for the detected critical stations.

[0028] According to embodiments of the present invention, the method further includes adjusting a set of scanning parameters for each station based on the actual anatomical structure, and / or applying different sets of scanning parameters to different stations.

[0029] According to another aspect of the invention, a computer program unit is provided for controlling a device according to a first aspect and any associated example, the computer program unit being configured, when run by a processor, to perform a method according to a third aspect and any associated example.

[0030] According to another aspect of the present invention, a computer-readable medium including the aforementioned computer program unit is provided.

[0031] These and other aspects of the invention will become apparent and will be explained with reference to the embodiments described below. Attached Figure Description

[0032] Exemplary embodiments of the invention will be described below with reference to the following figures: Figure 1 An example of an MRI system is shown; Figure 2 Another example of an MRI system is shown; Figure 3 illustrates an example of a workflow for performing multi-station scanning; Figure 4 A flowchart is shown for a method for detecting key stations in a multi-station scan. Detailed Implementation

[0033] Figure 1An example of a magnetic resonance imaging (MRI) system 100 is illustrated schematically and exemplary. The MRI system includes an MRI scanner 110, a device 10 for detecting key stations in a multi-station scan, and a patient instruction device 120.

[0034] Typically, the MR scanner 110 generates a polarizing magnetic field in an area commonly referred to as the magnet "bore" 112. The image to be produced by the MR scanner 110 is defined by selecting an appropriate sequence of nuclear magnetic resonance (NMR) imaging pulses to be performed by the pulse generator. The location and orientation of the slice or three-dimensional region to be imaged are also defined and determined by the physician based on the specific patient anatomy they wish to see during the procedure being performed.

[0035] MRI scanner 110 measures the nuclear magnetic resonance (NMR) signal of protons from within the patient and picks up images of proton density distribution, relaxation time distribution, etc. In whole-body imaging (whole-body MRI), the patient is repeatedly moved on a worktable 114 placed on it. MRI images of the patient's entire body are acquired and screening is performed. In multi-station imaging, the worktable 114 is moved progressively to acquire whole-body images. As the patient is divided into multiple stations (imaging zones), the patient is imaged in sync with the movement of the worktable. For example, in a three-station CEMRA (contrast-enhanced magnetic resonance angiography) study of the subject's peripheral vascular system, these stations are typically located at the level of the origin of the renal artery, the level of the thigh (for imaging the femoral artery), and the level of the lower leg (for imaging the popliteal artery and its trifurcation branches). Complete images are acquired at each station, but no data acquisition is performed as the worktable 114 moves from station to station.

[0036] The overall operation of the MRI scanner 110 can be controlled by an operator from a console 130. The console 130 can be coupled to a monitor 140, on which acquired MRI images or imager settings can be observed or viewed. An operator, such as a medical laboratory technician, can control image acquisition operations via the console 130 by “pointing” at specific patient anatomy, and this “pointing” is sensed by a tracking coil. As a result, MRI images are acquired, combined, and generated on the monitor 140, providing a whole-body image to the physician.

[0037] Device 10 can be any computing device configured to detect key stations in a multi-station scan, including desktop and laptop computers, smartphones, tablets, etc. Figure 1In the example, components of device 10 are shown integrated into a single unit. However, in alternative examples, some or all components may be arranged as separate modules in a distributed architecture and connected by a suitable communication network. Device 10 and its components may be arranged as dedicated FPGAs or hardwired independent chips. In some examples, device 10 or some of its components may reside in console 130 and run as software routines.

[0038] Device 10 includes an input unit 12, a processing unit 14, and an output unit 16. Each unit may be a portion of an application-specific integrated circuit (ASIC), electronic circuitry, a processor (shared, dedicated, or grouped) and / or memory (shared, dedicated, or grouped) executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functions, or may include an application-specific integrated circuit (ASIC), electronic circuitry, a processor (shared, dedicated, or grouped) and / or memory (shared, dedicated, or grouped) executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functions.

[0039] Input unit 12 is configured to receive image data acquired from a patient lying on the worktable before diagnostic scanning begins using MRI scanner 110. Input unit 12 may be implemented as an Ethernet interface, USB™ interface, wireless interface such as WiFi™ or Bluetooth™, or any comparable data transfer interface that enables data transfer between input peripherals and processing unit 14. Image data acquired from the patient lying on the worktable before diagnostic scanning begins is used as input for station planning. Examples of image data may include survey scans acquired with an MR scanner or optical images from a camera.

[0040] exist Figure 1 In this example, device 10 is configured to receive a survey scan acquired using MRI scanner 110. Images acquired from the MR survey scan are initially obtained as a set of three-plane, low-resolution, large-field-of-view locators, equivalent to "positioning views" in CT. Survey scans acquired using an MR system provide low-resolution images of, for example, lung morphology based on the resonant radiofrequency signals of protons in tissues and fluids, known as proton-MRI or 1H-MRI. Multi-station surveys can also be multi-station whole-body scans, most often with two orthogonal slices. The images are stitched together and then used for planning.

[0041] In another example, such as Figure 2As shown, device 10 can be configured to receive image data captured using optical camera 150. Optical camera 150 can be a camera-based sensor, but the use of a three-dimensional non-contact motion scanner, employing, for example, light detection and ranging (LIDAR) or radio detection and ranging (RADAR), is also envisioned. Optical camera 150 can image the patient using visible and / or near-infrared light. Optical camera 150 can be mounted somewhere above the patient to image the patient before moving the patient table into the chamber. For example, optical camera 150 can be mounted on the ceiling or wall of the examination room. In this way, it is possible to automatically analyze the optical images acquired from the patient directly before moving the table into the chamber. For example, optical camera 150 can be mounted on an MRI scanner. The optical camera can be calibrated to the MR system so that any processing performed on color or depth images can be transferred to the MR system at true size. In the example, the optical camera can be an in-chamber camera, such as the Philips VitalEye. The camera can be arranged inside a plastic housing in the upper left corner and can be focused on the upper body of the object. In this way, optical images obtained from the patient located in the thorax can be automatically analyzed before a diagnostic scan is started using a magnetic resonance imaging system.

[0042] Return to Figure 1 Then, the processing unit 14 automatically analyzes the received image data to identify the spatial location of the patient's lungs.

[0043] exist Figure 1 In one example, this could be done, for instance, by segmenting the lungs from an image acquired from an MR survey scan.

[0044] exist Figure 2 In the example, different methods were envisioned to identify the spatial location of a patient's lungs in the received optical images.

[0045] In the example, a body model that approximates the body surface, such as the SCAPE model, can be used. See, for example, D. Anguelov et al. (2005), “SCAPE: shape completion and animation of people” (ACMTrans. Graph., 24(3), 408-416). This model provides an accurate and scalable representation of the shape and pose of the human body. It can be used for surface matching with contours derived from sensed image data. This yields an accurate description of the patient's body, which can be used to locate anatomical regions of interest, such as the patient's lungs.

[0046] In another example, a skeleton detection method is used to detect and track a patient's body. The body is described as a set of joint locations of the skeleton. The coordinates of the skeletal joints are the starting points for locating the spatial position of the patient's chest region and lungs. The elongated structures formed by the patient's limbs can be followed into an image footprint of the torso to obtain clues about where the joints are located.

[0047] In another example, 3D registration with an annotated average 3D body model can be utilized. For this purpose, a generalized 3D body model with segmented and annotated organs (e.g., a CT dataset) can be used to approximate the location of relevant anatomical structures (such as the lungs) of the patient. The generalized 3D body model is fitted and scaled to the patient's 3D sensed body shape. Fitting and scaling can be done via 3D registration techniques. The anatomical information from the generalized and scaled 3D model can then be used to identify anatomical structures of interest.

[0048] The processing unit 14 then aligns the spatial location of the lung identified using each of the methods described above with the planned multi-station scan to identify key stations potentially affected by the patient's respiratory movements. Key stations may include stations that at least partially overlap with the identified lung / pleural cavity. Optionally, a safety margin may be applied around the lung / pleural cavity to also cover parts of the body that are only occasionally / slightly affected by respiratory movements (e.g., the abdomen).

[0049] Figures 3A-3D An example of the workflow is illustrated schematically. Figure 3A The planning image, which can be acquired from an optical camera, is shown. The planning image can be displayed on the monitor 140.

[0050] Figure 3B The diagram illustrates station planning for multi-station MR scans. In some examples, these stations can be defined manually based on user-provided input. In other examples, stations can be defined automatically based on anatomical landmarks detected on the planned images.

[0051] Figure 3C This illustrates the identification of lung regions and the assignment of breath-holding to corresponding stations. The key stations for these identifications are... Figure 3C Double lines are used for indication, but the use of colored lines or any other labels is also envisioned. To identify these key stations, device 10 is configured to identify the spatial location of the lungs according to at least one of the methods described above, and then align the identified spatial location of the lungs with a planned multi-station scan. Figure 3C As shown, the identified key stations include those covering the lungs / pleural cavity. A safety margin can be applied around the lungs / pleural cavity to also cover the abdomen of patients who may be affected by respiratory movements.

[0052] Figure 3DAn example of multi-station scanning is shown. Multi-station scanning is performed as usual, and the combination of images from different stations is also performed as usual. Patient instruction device 120 (such as...) Figure 1 and 2 The speaker shown can be used to automatically prompt the patient to inhale, exhale, and hold their breath via voice. In another example (not shown), the patient instruction device 120 can be an intracorporeal display that provides visual breathing instructions to prompt the patient to inhale, exhale, and hold their breath.

[0053] Optionally, the MRI system 100 may also include a controller configured to adjust the set of scanning parameters for each station and / or apply different sets of scanning parameters to different stations based on the actual anatomical structures. Figure 1 and 2 In the example shown, the controller resides in console 130 and runs as a software routine. In some other examples, the controller and its components may be arranged as a dedicated FPGA or a hardwired standalone chip.

[0054] For example, console 130, as an example of a controller, can be configured to automatically adjust various scanning parameters for each station based on the actual anatomical structure. For example, the spatial resolution, field of view, number of averages, etc., of each station can be adjusted based on the actual anatomical structure. For example, a pre-trained deep learning network can be applied to derive scanning parameters from the actual anatomical structure and patient data (e.g., age, sex, body mass index, etc.). For example, a deep neural network with multiple layers between the input and output layers can be applied. The pre-trained deep learning network has been trained on a training dataset that includes previously recorded patient data, anatomical structures, and associated scanning parameters from the same patient and / or from other patients.

[0055] The console 130 can also be configured to automatically apply different sets of scanning parameters to different stations. For example, the load on the body coil changes as it moves from station to station, and the applied B1 can be adjusted without repeated preparation measurements.

[0056] refer to Figure 4 The flowchart of a method 200 for detecting key stations in a multi-station scan is shown.

[0057] In step 210 (i.e., step a), image data is received from the patient lying on the worktable before the diagnostic scan is initiated using the magnetic resonance imaging system. Image data is acquired as input for station planning. This includes optical images from a camera mounted somewhere above the patient—for example, before the patient table is moved into the chamber. This step may also be referred to as the “planning phase.”

[0058] In step 220 (i.e., step b), the patient's image data is analyzed to identify the spatial location of the patient's lungs. In other words, the planned images acquired in step a) are automatically analyzed to identify the spatial location of the patient's lungs. This can be accomplished, for example, by locating the thoracic cavity from optical images, such as using the aforementioned body surface approximation parameter body model, skeleton detection methods, or 3D registration using an annotated average 3D body model. This step may also be referred to as the "analysis phase".

[0059] In step 230 (i.e., step c), the spatial location of the patient's lungs is aligned with the planned multi-station scan to identify key stations potentially affected by the patient's respiratory movements. Breath-holding is then assigned to the identified key stations.

[0060] In this step, the multi-station scan can be planned manually by the operator or automatically based on the detection of anatomical landmarks. The lung location identified in step b) is automatically aligned with the planned multi-station scan; that is, all stations containing body parts that move due to breathing are marked by corresponding software. These marked stations are automatically set to breath-hold, so that for these stations, the patient is prompted, for example, to inhale, exhale, and hold their breath via automated voice prompts. Optionally, a safety margin can be applied around the lungs / thoracic cavity to also cover body parts that are only occasionally / slightly affected by respiratory movements. This step may also be referred to as the "assignment phase."

[0061] In step 240 (i.e., step d), the identified key stations are output to, for example... Figure 1 and 2 Console 130 in the system is used to perform multi-station scans. This step can also be referred to as the "output phase".

[0062] Optionally, method 200 may also include using, for example, automated voice or visual instructions to instruct the patient to inhale, exhale, and / or hold their breath at detected key stations.

[0063] Optionally, method 200 may also include the steps of adjusting the set of scanning parameters for each station (e.g., spatial resolution, field of view, number of averages) according to the actual anatomical structure and / or applying different sets of scanning parameters to different stations.

[0064] In another exemplary embodiment of the invention, a computer program or a computer program unit is provided, characterized in that it is adapted to perform the method steps of the method according to one of the preceding embodiments on a suitable system.

[0065] Therefore, the computer program unit can be stored on a computer unit, which can also be part of an embodiment of the present invention. The computing unit can be adapted to perform the steps of the methods described above or to induce the execution of the steps of the methods described above. Furthermore, it can be adapted to operate components of the apparatus described above. The computing unit is adapted to automatically operate and / or execute user commands. The computer program can be loaded into the working memory of a data processor. The data processor can thus be configured to perform the methods of the present invention.

[0066] This exemplary embodiment of the invention covers both computer programs that use the invention from the outset and computer programs that convert existing programs into programs that use the invention through updates.

[0067] Furthermore, the computer program unit is capable of providing all the necessary steps of the process for implementing exemplary embodiments of the methods described above.

[0068] According to another exemplary embodiment of the invention, a computer-readable medium, such as a CD-ROM, is provided, wherein the computer-readable medium has computer program units stored on the computer-readable medium, the computer program units being described in the preceding portion.

[0069] Computer programs can be stored / distributed on suitable media, such as optical storage media or solid-state media provided with or as part of other hardware, but computer programs can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0070] However, the computer program may also exist on a network such as the World Wide Web and can be downloaded from such a network to the working memory of a data processor. According to another exemplary embodiment of the invention, a medium is provided for making a computer program unit available for download, wherein the computer program unit is arranged to perform the method described in one of the previously described embodiments according to the invention.

[0071] It must be noted that embodiments of the present invention are described with reference to different subjects. Specifically, some embodiments are described with reference to claims of the method type, while others are described with reference to claims of the device type. However, those skilled in the art will understand from the above and below description that, unless otherwise indicated, any combination of features relating to different subjects, in addition to any combination of features belonging to one type of subject, is also considered to be disclosed in this application. However, all features can be combined to provide synergistic effects beyond the simple sum of the features.

[0072] Although the invention has been described and illustrated in detail in the accompanying drawings and the foregoing description, such description and illustration are to be considered illustrative or exemplary rather than restrictive. The invention is not limited to the disclosed embodiments. Other variations of the disclosed embodiments will be understood and implemented by those skilled in the art in practicing the claimed invention by studying the drawings, description, and dependent claims.

[0073] In the claims, the word "comprising" does not exclude other units or steps, and the words "a" or "an" do not exclude multiple. A single processor or other unit may perform the functions of several items recited in the claims. Although specific measures are recited in different dependent claims, this does not indicate that combinations of these measures cannot be advantageously used. Any reference numerals in the claims should not be construed as limiting the scope.

Claims

1. An apparatus (10) for detecting key stations in a multi-station scan, comprising: Input unit (12); Processing unit (14); as well as Output unit (16); The input unit is configured to receive image data acquired from a patient lying on a worktable before a diagnostic scan is initiated using a magnetic resonance imaging system, wherein the image data includes optical images captured by an optical camera; The processing unit is configured to: analyze received image data of the patient to identify the spatial location of the patient's lungs; align the spatial location of the patient's lungs with a planned multi-station scan to identify key stations potentially affected by the patient's respiratory movements; and assign breath-holding to the identified key stations; and The output unit is configured to provide the identified key stations.

2. The device according to claim 1, in, The image data includes optical images captured by an in-bore optical camera.

3. The device according to claim 1 or 2, in, The processing unit is configured to identify the spatial location of the patient's lungs by locating the thoracic cavity based on landmark detection according to the optical image.

4. The device according to claim 1 or 2, in, The processing unit is configured to apply a safety margin around the identified spatial location of the lung to cover parts of the body potentially affected by the patient's respiratory movements.

5. A magnetic resonance imaging (MRI) system (100), comprising: MRI scanner (110) having a bore (112) and a worktable (114) for supporting the patient. An optical camera (150) is configured to capture optical images of the patient lying on the worktable before a diagnostic scan is initiated using the MRI scanner; The apparatus for detecting critical stations in a multi-station scan according to any one of claims 1-4; and The patient instruction device (120) is configured to provide respiratory instructions for detected critical stations.

6. The MRI system according to claim 5, further comprising: in, The optical camera includes an in-bore optical camera.

7. The MRI system according to claim 5 or 6, further comprising: The controller (130) is configured to adjust the set of scanning parameters for each station and / or apply different sets of calibration parameters for different stations based on the actual anatomical structures.

8. A method (200) for detecting key stations in a multi-station scan, comprising: a) Receive (210) image data acquired from a patient lying on a worktable before a diagnostic scan is initiated using a magnetic resonance imaging system, wherein the image data includes optical images captured by an optical camera; b) Analyze the patient's image data (220) to identify the spatial location of the patient's lungs. c) Align the spatial location of the patient's lungs with a planned multi-station scan (230) to identify key stations potentially affected by the patient's respiratory movements, and assign breath-holding to the identified key stations; and d) Provide the key stations identified by (240).

9. The method according to claim 8, in, Step b) also includes applying a safety margin around the identified spatial location of the lung to cover parts of the body that are also affected by the patient’s respiratory movements.

10. The method according to claim 8 or 9, further comprising: Provide breathing instructions for the detected critical stations.

11. The method according to claim 8 or 9, further comprising: The set of scanning parameters for each station is adjusted based on the actual anatomical structure; and / or Different sets of calibration parameters are applied to different stations.

12. A computer program unit for controlling a device according to any one of claims 1 to 4 or a system according to any one of claims 5 to 7, said computer program unit being configured, when run by a processor, to perform the method according to any one of claims 8 to 11.

13. A computer-readable medium comprising a computer program unit as claimed in claim 12.

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