Automatic detection of optimal X-ray emitter position for mobile X-ray

Abstract

A computer-implemented method for determining a position of an X-ray emitter of a mobile X-ray device to obtain high-quality mobile X-ray images is provided, the method comprising the steps of receiving a camera-acquired image resulting from a camera monitoring a patient during an X-ray imaging session, a reference X-ray image of an internal body structure to be examined in the X-ray examination, and X-ray detector position information, detecting and locating a plurality of anatomical landmarks in the camera-acquired image, determining a distance from the X-ray emitter to the patient based on the camera-acquired image, generating a virtual X-ray image of the internal body structure based on the detected plurality of anatomical landmarks, the distance from the X-ray emitter to the patient, and the X-ray detector position information, registering the virtual X-ray image of the internal body structure to the reference X-ray image of the internal body structure, and determining at least one parameter for adjusting the position of the X-ray emitter based on the registration result.

Description

[Technical Field]

[0001] The present invention relates to a computer-implemented method for determining the position of an X-ray emitter of a mobile X-ray device, an X-ray emitter position determining device, an X-ray imaging system, a computer program product, and a computer-readable medium. [Background technology]

[0002] Mobile X-ray, also known as portable X-ray, is an important image acquisition modality. It allows X-rays to be taken in places where static X-ray imaging is impractical, such as emergency departments (EDs) and intensive care units (ICUs). Mobile X-rays are primarily intended for patients lying in bed. In contrast to static X-rays, acquiring X-ray images with portable X-rays is more challenging because there are more degrees of freedom than with static X-ray images.

[0003] FIG. 1 illustrates the basic operating principle of a mobile X-ray device 10. First, a technician places an X-ray detector 12 behind a patient, shown in FIG. 1 as a bedside patient. Then, the technician manually sets the position of the X-ray emitter 14 so that the desired field of view can be captured by the X-ray detector 12. Finally, the technician acquires an image by activating the X-ray emitter 14. There are many parameters that can have a significant impact on image quality, such as the position of the X-ray detector 12 (e.g., coordinates and rotation angle) and the position of the X-ray emitter (e.g., distance to the patient, rotation angle, and voltage). Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, there may be a need to acquire higher quality mobile x-ray images. [Means for solving the problem]

[0005] The object of the present invention is solved by the subject matter of the independent claims, further embodiments are incorporated in the dependent claims.

[0006] According to a first aspect of the present invention, there is provided a computer-implemented method for determining a position of an X-ray emitter of a mobile X-ray device, the method comprising: a) receiving (i) a camera-acquired image resulting from a camera monitoring a patient during an X-ray imaging session, the camera being registered to an origin of an X-ray emitter, (ii) a reference X-ray image of an internal body structure being examined in the X-ray examination, and (iii) position information of an X-ray detector; b) detecting and localizing a plurality of anatomical landmarks in the camera-acquired image; c) determining the distance from the X-ray emitter to the patient based on the camera-acquired image; d) generating a virtual X-ray image of the internal body structure based on the detected plurality of anatomical landmarks, the distance from the X-ray emitter to the patient, and the position information of the X-ray detector; e) registering the virtual x-ray image of the internal body structure with the reference x-ray image of the internal body structure; f) determining at least one parameter for adjusting the position of the X-ray emitter based on the alignment result; It has.

[0007] In other words, this disclosure proposes a method that can automatically locate the optimal x-ray emitter position for an image using either a reference x-ray image, such as a previous high-quality image from the same patient, or a reference image from an atlas that is deemed appropriate for this imaging case. In the method disclosed herein, a reduction in the radiation dose received by the patient can be achieved by reducing the number of retakes. Furthermore, reduced turnaround time can be achieved by avoiding low-quality images being sent to the PACS and rejected upon review. Furthermore, costs can also be reduced because multiple job iterations are reduced.

[0008] This is explained in more detail below, and particularly with respect to the example shown in FIG.

[0009] According to one embodiment of the present invention, the step of generating a virtual X-ray image of an internal body structure further comprises: generating a pseudo-density image of the patient based on the detected plurality of anatomical landmarks; projecting a pseudo-density image of the patient onto the x-ray detector based on the camera position, the distance from the x-ray emitter to the patient, and the x-ray detector position information using cone beam projection to obtain a virtual x-ray image of the internal body structure; It has.

[0010] This is explained in more detail below, and particularly with respect to the example shown in FIG.

[0011] According to an embodiment of the present invention, the camera comprises a three-dimensional (3D) camera and / or a two-dimensional (2D) camera, and the distance from the X-ray emitter to the patient is determined based on depth information in the camera-acquired images or neural network-based depth estimation.

[0012] This is explained in more detail below, particularly with respect to step 230 shown in FIG.

[0013] According to one embodiment of the present invention, the step of registering the virtual X-ray image of the internal body structure with the reference X-ray image of the internal body structure further comprises: applying a pre-trained neural network to register a virtual x-ray image of the internal body structure to a reference x-ray image of the internal body structure, the pre-trained neural network being trained to generate residual camera position parameters of a camera from the virtual x-ray image, the reference x-ray image, and the detected plurality of anatomical landmarks, the residual camera position parameters being usable to transform the virtual x-ray image to the reference x-ray image; It has.

[0014] This is explained in more detail below, and particularly with respect to the example shown in FIG.

[0015] According to one embodiment of the present invention, the at least one parameter for adjusting the position of the X-ray emitter is: parameters for adjusting the distance from the X-ray emitter to the patient, and parameters for adjusting the rotation angle of the X-ray emitter, Contains one or more of the following: According to one embodiment of the present invention, the computer-implemented method further comprises: providing a command signal to guide a user to manually adjust the position of the X-ray emitter based on the at least one determined parameter; It has.

[0016] According to one embodiment of the present invention, the computer-implemented method further comprises: providing a control signal usable to control a mobile x-ray robotic arm of the mobile x-ray device to adjust the position of the x-ray emitter based on the at least one determined parameter; It has.

[0017] According to one embodiment of the present invention, the reference X-ray image comprises: Previously obtained x-ray images of the patient's internal structures, and Reference X-ray images from the atlas, Contains one or more of the following:

[0018] According to a second aspect of the present invention, there is provided an X-ray emitter position determining apparatus having a processing unit configured to perform the steps of the method according to the first aspect and any associated examples.

[0019] This is explained in more detail below, and particularly with respect to the example shown in FIG.

[0020] According to a third aspect of the present invention, a mobile X-ray device having an X-ray emitter; a camera mountable to the mobile X-ray device and configured to capture camera-acquired images from a patient during an X-ray imaging session, the camera being aligned with an origin of the X-ray emitter; an X-ray detector; a tracker device attached to or embedded in the X-ray detector and configured to provide position information of the X-ray detector; an X-ray emitter position determination device according to the second aspect and any related examples configured to determine at least one parameter for adjusting a position of the X-ray emitter; An X-ray imaging system is provided, comprising:

[0021] This is explained in more detail below, and particularly with respect to the example shown in FIG.

[0022] According to one embodiment of the present invention, the tracker device comprises one or more of a marker device, a gyroscope, and an antenna.

[0023] According to one embodiment of the present invention, the mobile X-ray device further comprises a mobile X-ray robotic arm supporting the X-ray emitter, and the X-ray emitter positioning device is configured to provide a control signal for controlling the mobile X-ray robotic arm of the mobile X-ray device to adjust the position of the X-ray emitter.

[0024] According to one embodiment of the present invention, the mobile X-ray device further comprises a display configured to display instructions provided by the X-ray emitter positioning device that guide a user to manually adjust the position of the X-ray emitter.

[0025] The predicted orientation can be used by the X-ray machine and displayed for manual correction by the technician, or automatic correction by a mobile X-ray robotic arm (if present) can be achieved.

[0026] According to another aspect of the present invention, there is provided a computer program product having instructions which, when executed by a processing unit, cause the processing unit to perform the steps of the methods disclosed herein.

[0027] According to a further aspect of the present invention, there is provided a computer readable medium having a computer program product stored thereon.

[0028] It should be understood that all combinations of the foregoing concepts and additional concepts discussed in more detail below (to the extent such concepts are not mutually inconsistent) are considered to be part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are considered to be part of the inventive subject matter disclosed herein.

[0029] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0030] In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead being placed generally upon illustrating the principles of the invention. [Brief explanation of the drawings]

[0031] [Figure 1] The basic operating principle of a mobile X-ray device is shown. [Figure 2] 1 illustrates an exemplary X-ray imaging system. [Figure 3] 1 shows a flowchart illustrating a computer-implemented method for determining the position of an X-ray emitter of a mobile X-ray device. [Figure 4] 1 illustrates an exemplary alignment process. [Figure 5] 1 shows a flow diagram illustrating the generation of a virtual X-ray image of an internal body structure according to an embodiment. [Figure 6] 3 illustrates an exemplary operating principle of the exemplary X-ray imaging system shown in FIG. 2. DETAILED DESCRIPTION OF THE INVENTION

[0032] In clinical practice, mobile X-ray systems are limited in terms of image quality compared to stationary X-ray systems, i.e., they can produce poor views, missed or clipped anatomical structures, and poor images can lead to re-images, which increase the patient's radiation dose.

[0033] To this end, methods, devices, and systems are provided to improve the image quality of mobile X-rays during the image acquisition phase and overcome one or more of the problems described above. In particular, a system is proposed that can automatically acquire higher quality mobile X-ray images. The system disclosed herein may automatically locate the optimal X-ray emitter position for the image using either a previous high-quality image from the same patient or a reference image from an atlas deemed appropriate for this imaging case.

[0034] Figure 2 shows an exemplary X-ray imaging system 100 according to one embodiment of the present invention. The X-ray imaging system 100 includes a mobile X-ray device 10 having an X-ray emitter 14. As shown in Figure 2, the mobile X-ray device 10 further includes a chassis 16 that supports an arm, such as a robotic arm 18, and has a system with wheels 20 for manual or motorized movement that allows the device to be transported. The robotic arm 18 can move horizontally and / or vertically and can support at its end a head assembly 22 on which the X-ray emitter 14 is disposed.

[0035] The X-ray imaging system 100 further includes a camera 24 that is attachable to the mobile X-ray device 10 and configured to capture camera-acquired images from the patient during an X-ray imaging session. The camera 24 is aligned with the origin of the X-ray emitter 14. For example, as shown in FIG. 2, the camera 24 may be located on the head assembly 22. In some examples, the camera 24 may be an embedded camera. In other examples, the camera 24 may be detachably attached to the head assembly 22. In some examples, the camera 24 may be a two-dimensional (2D) camera configured to capture a scene using one imaging lens and one image sensor. The resulting image is referred to as a 2D image. In some examples, the camera 24 may be a three-dimensional (3D) camera for capturing 3D images. A 3D camera may be a range camera that generates a 2D image showing the distance from a particular point to points within the scene. A 3D camera may be, for example, a stereo camera, which is a type of camera with two or more lenses with a separate image sensor or film frame for each lens.

[0036] 2 further includes a portable X-ray detector 12, which may be placed behind the patient to measure the flux, spatial distribution, spectrum, and / or other characteristics of the X-rays. The portable X-ray detector 12 may be fitted with a tracker device 26, such as a gyroscope, antenna, marker, or other device that helps to locate the position of the portable X-ray detector relative to the X-ray emitter 14.

[0037] 2 further includes an X-ray emitter position determination device 30 configured to determine at least one parameter for adjusting the position of the X-ray emitter 14. In general, the X-ray emitter position determination device 30 may include various physical and / or logical components that communicate and manipulate information, which may be implemented as hardware components (e.g., computing devices, processors, logic devices), executable computer program instructions (e.g., firmware, software) executed by the various hardware components, or any combination thereof, as desired for a given set of design parameters or performance constraints.

[0038] In some implementations, the X-ray emitter position determining device 30 may be embodied as or within a device, such as the mobile X-ray device 10 or mobile device shown in FIG. 2. The X-ray emitter position determining device 30 may include one or more microprocessors or computer processors that execute appropriate software. The processing unit of the device 10 may be embodied by one or more of these processors. The software may be downloaded and / or stored in corresponding memory, for example, volatile memory such as RAM or non-volatile memory such as flash. The software may include instructions that configure one or more processors to perform the functions described herein.

[0039] It should be noted that the X-ray emitter position determining apparatus 30 may be implemented with or without a processor, and may be implemented as a combination of dedicated hardware that performs some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) that performs other functions. For example, the functional units of the X-ray emitter position determining apparatus 30 may be implemented in a device or apparatus in the form of programmable logic, such as, for example, a field programmable gate array (FPGA). In general, each functional unit of an apparatus may be implemented in the form of a circuit.

[0040] While FIG. 2 may show the X-ray emitter position determination device 30 embodied within a mobile X-ray device 10, it will be understood that in some implementations the X-ray emitter position determination device 30 may be embodied as or within a mobile device, such as a tablet computer.

[0041] The X-ray emitter position determining device 30 is configured to perform the method disclosed herein, which is described in detail below in conjunction with the flow chart shown in FIG.

[0042] 3 shows a flowchart illustrating a computer-implemented method 200 for determining the position of an X-ray emitter of a mobile X-ray device. Method 200 may be implemented as a device, module, or associated component in a set of logical instructions stored on a non-transitory machine- or computer-readable storage medium, such as random access memory (RAM), read-only memory (ROM), programmable ROM (PROM), firmware, flash memory, etc.; in configurable logic, such as a programmable logic array (PLA), field programmable gate array (FPGA), complex programmable logic device (CPLD), etc.; in fixed-function hardware logic using circuit technologies, such as application-specific integrated circuit (ASIC), complementary metal-oxide semiconductor (CMOS), or transistor-transistor logic (TTL) technology, etc.; or as any combination thereof. For example, computer program code for performing the operations shown in method 200 may be written in any combination of one or more programming languages, including object-oriented programming languages, such as JAVA, SMALLTALK, C++, Python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. For example, the exemplary method may be implemented as an apparatus 30 shown in FIG.

[0043] In step 210, method 200 includes receiving (i) a camera-acquired image resulting from a camera monitoring a patient during an X-ray imaging session, the camera being aligned with the origin of an X-ray emitter, (ii) a reference X-ray image of an internal body structure being examined in the X-ray examination, and (iii) position information of an X-ray detector.

[0044] The camera-acquired images may originate from camera 24, shown in FIG. 2 , monitoring a patient during an X-ray imaging session. In some examples, camera 24 may be a video camera configured to transmit a real-time video stream to device 30, which may have a video processing unit to process the real-time video stream. In some examples, camera 24 may be a camera configured to capture images and transmit the images to device 30, which may have an image processing unit to process the images. In some examples, the camera-acquired images may include one or more 3D images. In some examples, the camera-acquired images may include one or more 2D images.

[0045] The camera 24 is aligned with the origin of the X-ray emitter 14. Figure 4 illustrates an exemplary alignment process. When an object is imaged, its representation is stored in a matrix of pixels that can be addressed by coordinates x, y. Typically, the origin (i.e., the 0,0 point) is located in the upper left corner of the matrix, with the x-axis running from left to right and the y-axis running from top to bottom. Unless the object and camera are rigidly attached to each other, imaging the object twice will result in two different matrices with two different coordinate systems. In the example of Figure 4, the patient's nose in image "a" is located to the lower right than in image "b." It is possible to align the two images together to evaluate a coordinate transformation that allows one image to be transformed into the other. Because the imaging plane is perpendicular to the camera axes, the transformation of an image directly translates into the camera movement required to reacquire one image when a second image is available.

[0046] In this disclosure, we propose to use image registration in two separate examples:

[0047] The transformation from the video camera to the emitter coordinate system is constant and can be explicitly expressed when designing the relative positions of the two objects,

[0048] The transformation between the emitter positions during the two different imaging sessions can be more complex than the translation and rotation shown in Figure 4. Therefore, it is proposed to learn this transformation using a neural network, e.g., a convolutional network, as will be explained in more detail below.

[0049] A reference x-ray image of the internal body structure to be examined in the x-ray examination may be downloaded from a picture archiving and communication system (PACS). The reference x-ray image may be a previous scan of the patient of good quality or any reference image or atlas of good quality. In some examples, the reference x-ray image may be acquired from a different patient.

[0050] The position information of the X-ray detector 12 may be obtained from a tracker device 26 attached to the X-ray detector 12, such as a marker device, a gyroscope, an antenna, or any combination thereof.

[0051] At step 220, method 200 further includes detecting and locating multiple anatomical landmarks in the camera-acquired image. In some examples, a patient model may be determined prior to imaging to match imaging parameters to the patient's anatomy. The model may include the locations of anatomical landmarks, such as the shoulders, pelvis, torso, knees, etc. The acquired surface image of the patient may be compared to a library of pre-modeled surface images to determine a model corresponding to the patient. This determination may be performed by a neural network trained based on the library of pre-modeled surface images. In some examples, the locations of the head, shoulders, torso, knees, and ankles may be determined based on 2D or 3D images. The neural network may be trained to automatically detect landmarks. For example, supervised learning can be applied to anatomical landmark localization. See, e.g., Gite, S., Mishra, A., and Kotecha, K. (2022). Enhanced lung image segmentation using deep learning. Neural Computing and Applications, 1-15.

[0052] In step 230, the method 200 further includes determining a distance from the X-ray emitter to the patient based on the camera-acquired image. If the camera is a 3D camera, the distance from the X-ray emitter to the patient may be determined based on depth information in the camera-acquired image. If the camera is a 2D camera, the distance from the X-ray emitter to the patient may be determined based on neural network-based depth estimation. For example, self-supervised learning may be applied to the distance or depth estimation. See, for example, Bian, J., Li, Z., Wang, N., Zhan, H., Shen, C., Cheng, M. M., & Reid, I. (2019). Unsupervised scale-consistent depth and ego-motion learning from monocular video. Advances in neural information processing systems, 32.

[0053] At step 240, the method 200 further includes generating a virtual X-ray image of the internal body structure based on the plurality of detected anatomical landmarks, the distance from the X-ray emitter to the patient, and the position information of the X-ray detector.

[0054] FIG. 5 shows a flow diagram illustrating one embodiment of step 240.

[0055] In step 310 of step 240, a pseudo-density image of the patient is generated based on the plurality of detected anatomical landmarks.

[0056] In step 320 of step 240, a pseudo density image of the patient is projected onto the X-ray detector based on the camera position of the camera, the distance from the X-ray emitter to the patient, and the position information of the X-ray detector using cone beam projection to obtain a virtual X-ray image of the internal body structures.

[0057] In other words, virtual X-ray images can be generated in two steps. First, anatomical landmarks are used to generate a pseudo-density image of the patient, for example, by a neural network. Such a network can be trained using annotated image pairs, e.g., CT images and corresponding masks. The neural network may be a convolutional network. Adversarial training can be used to train this model. See, for example, Park, T., Liu, MY, Wang, TC, & Zhu, JY (2019). Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE / CVF conference on computer vision and pattern recognition (pp. 2337-2346). The resulting pseudo-density image is projected onto a detector given the camera position, distance to the patient, and relative detector position using cone-beam projection. The resulting image is a virtual X-ray image.

[0058] Returning to FIG. 3, at step 250, the method 200 further includes registering the virtual x-ray image of the internal body structure with the reference x-ray image of the internal body structure.

[0059] In some examples, a pre-trained neural network can be applied to align a virtual X-ray image of an internal body structure with a reference X-ray image of the internal body structure. The pre-trained neural network is trained to generate residual camera position parameters from the virtual X-ray image, the reference X-ray image, and a plurality of detected anatomical landmarks. The residual camera position parameters can be used to transform the virtual X-ray image to the reference X-ray image. For example, the neural network can be trained as follows: The neural network takes three inputs: the reference X-ray image, the virtual X-ray image, and an anatomical landmark map. The model output is the residual camera position parameters. These parameters can be applied to transform the virtual image toward the reference image. The required images for training are artificially sampled from simulated CT projections using static detector parameters and a flexible emitter. The alignment results are used to predict the direction of how to adjust the emitter position. The neural network can be a convolutional network. Self-supervised learning can be applied to train this model. See, e.g., Dalca, AV, Balakrishnan, G., Guttag, J., & Sabuncu, MR (2018, September). Unsupervised learning for fast probabilistic diffeomorphic registration. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 729-738). 88Springer, Cham.

[0060] In step 260, the method 200 further includes determining at least one parameter for adjusting the position of the X-ray emitter based on the alignment result. That is, the alignment result is used to predict a direction for adjusting the emitter position. The at least one parameter may include one or more of a parameter for adjusting a distance from the X-ray emitter to the patient and a parameter for adjusting a rotation angle of the X-ray emitter.

[0061] In some examples, the device 30 may provide a command signal to guide a user to manually adjust the position of the X-ray emitter based on the at least one determined parameter. In some examples, the command signal may be an audio signal to guide a technician to manually modify the position of the X-ray emitter. In some examples, the command signal may be displayed instructions used to guide a technician to manually modify the position of the X-ray emitter. In some other examples, the device 30 may provide a control signal usable to control the mobile X-ray robotic arm 18 of the mobile X-ray device 10 to adjust the position of the X-ray emitter 14 based on the at least one determined parameter.

[0062] FIG. 6 illustrates an exemplary principle of operation of the exemplary X-ray imaging system 100 shown in FIG.

[0063] The mobile X-ray device 10 is placed in front of the patient (indicated by "a") and the X-ray detector 12 is placed behind the patient.

[0064] The technician may download a reference X-ray image (denoted "b") from the PACS. It may be a previous scan of good quality, or a reference image or atlas of good quality. The reference X-ray image is provided to device 30. Camera 24 transmits a camera-acquired image, e.g., a real-time video stream or image, to device 30.

[0065] The device 30 may have a first neural network, also referred to as neural network A, to predict anatomical landmarks and distances to the patient (denoted by "c") based on camera-acquired images in the patient video.

[0066] The device 30 may also have a second neural network, also referred to as neural network B, to predict a virtual X-ray image using anatomical landmarks (denoted "d") and X-ray emitter (denoted "f") and detector positions (denoted "e"). The virtual X-ray image is generated in two steps. The 3D anatomical landmarks from neural network A are used by neural network B to generate a pseudo-density image of the patient. Such a network can be trained using an annotated image pair, e.g., a CT image and corresponding mask. The resulting pseudo-density image is projected onto the detector given the camera position, distance to the patient, and relative detector position using cone-beam projection. The resulting image is a virtual X-ray image.

[0067] The device 30 may also have a third neural network, also referred to as neural network C, to align a virtual X-ray image (denoted "h") to a reference X-ray image (denoted "i") using anatomical landmarks (denoted "g"). Neural network C is trained as follows: it takes three inputs: a reference X-ray image, a virtual X-ray image, and an anatomical landmark map. The output of the model is residual parameters of the camera position. These parameters can be applied to transform the virtual image toward the reference image. The required images for training are artificially sampled from pseudo-CT projections using static detector parameters and flexible emitter parameters. The alignment results are used to predict the direction of how to adjust the emitter position.

[0068] The predicted direction (indicated by "j") can be used by the X-ray machine and displayed for manual correction by the technician, or automatic correction by a mobile X-ray robotic arm (if present) can be achieved.

[0069] The methods, devices, and systems disclosed herein can automatically locate the optimal X-ray emitter position for the image using either a previous high-quality image from the same patient or a reference image from an atlas deemed appropriate for the imaging case, and can automatically acquire higher-quality mobile X-ray images. The methods, devices, and systems disclosed herein can reduce the number of retakes and therefore achieve a reduction in the radiation dose received by the patient. The methods, devices, and systems disclosed herein can avoid low-quality images being sent to the PAC and rejected upon review, thus achieving a reduced turnaround time. The methods, devices, and systems disclosed herein can also reduce costs because multiple job iterations can be reduced.

[0070] In another exemplary embodiment of the invention, a computer program or a computer program element is provided, characterized in that it is configured to perform, on a suitable system, the method steps of the method according to one of the previous embodiments.

[0071] Thus, a computer program element may be stored on a computing unit that may be part of an embodiment of the present invention. This computing unit may be configured to perform or direct the performance of the steps of the above-mentioned method. Furthermore, it may be configured to operate the components of the above-mentioned apparatus. The computing unit may be configured to operate automatically and / or to execute a user's order. The computer program may be loaded into the working memory of a data processor. The data processor may thus be equipped to perform the method of the present invention.

[0072] This exemplary embodiment of the present invention encompasses both computer programs that use the present invention from the beginning, and computer programs that convert existing programs into programs that use the present invention by means of an update.

[0073] Furthermore, the computer program element may be capable of providing all the steps necessary to fulfill the procedures of the exemplary embodiments of the methods described above.

[0074] According to a further exemplary embodiment of the present invention, a computer readable medium, such as a CD-ROM, is presented, the computer readable medium having stored thereon computer program elements, the computer program elements being described by the preceding sections.

[0075] The computer program may be stored and / or distributed on a suitable medium, such as an optical storage medium or a solid-state medium 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 telecommunications systems.

[0076] However, the computer program may also be presented over a network such as the World Wide Web and can be downloaded into the working memory of a data processor from such a network. According to a further exemplary embodiment of the present invention, a medium for making a computer program element available for downloading is provided, the computer program element being configured to perform a method according to one of the aforementioned embodiments of the present invention.

[0077] It should be noted that the embodiments of the present invention are described with reference to different subject matters. In particular, some embodiments are described with reference to method-type claims, and other embodiments are described with reference to apparatus-type claims. However, those skilled in the art will understand from the above and below description that, unless otherwise specified, any combination of features belonging to one type of subject matter, as well as any combination between features relating to different subject matters, is considered to be disclosed in the present application. However, all features can be combined to provide a synergistic effect greater than the simple sum of the features.

[0078] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered exemplary or explanatory and not restrictive. The invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the dependent claims.

[0079] In the claims, the word "comprise" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be interpreted as limiting the scope.

Claims

1. 1. A computer-implemented method for determining a position of an X-ray emitter of a mobile X-ray device, comprising: receiving (i) a camera-acquired image resulting from a camera monitoring a patient during an X-ray imaging session, the camera being aligned with the origin of the X-ray emitter; (ii) a reference X-ray image of an internal body structure being examined in the X-ray examination; and (iii) X-ray detector position information; detecting and locating a plurality of anatomical landmarks within the camera-acquired image; determining a distance from the x-ray emitter to the patient based on the camera-acquired image; generating a virtual X-ray image of the internal body structure based on the detected anatomical landmarks, the distance from the X-ray emitter to the patient, and the position information of the X-ray detector; registering the virtual x-ray image of the internal body structure with the reference x-ray image of the internal body structure; determining at least one parameter for adjusting the position of the X-ray emitter based on the alignment result; 1. A computer-implemented method comprising:

2. The generation of the virtual X-ray image of the internal body structure based on the detected plurality of anatomical landmarks further includes: generating a pseudo-density image of the patient based on the detected anatomical landmarks; projecting the pseudo density image of the patient onto the x-ray detector based on a camera position of the camera, the distance from the x-ray emitter to the patient, and position information of the x-ray detector using cone beam projection to obtain the virtual x-ray image of the internal body structure; The computer-implemented method of claim 1 , comprising:

3. the camera includes a three-dimensional camera and a two-dimensional camera; the distance from the X-ray emitter to the patient is determined based on depth information in the camera-acquired images or neural network-based depth estimation.

3. A computer-implemented method according to claim 1 or 2.

4. The step of registering the virtual x-ray image of the internal body structure with the reference x-ray image of the internal body structure further comprises: applying a pre-trained neural network to register the virtual x-ray image of the internal body structure to the reference x-ray image of the internal body structure, the pre-trained neural network being trained to generate residual parameters of camera positions of the camera from the virtual x-ray image, the reference x-ray image, and the detected anatomical landmarks, the residual parameters of camera positions being usable to transform the virtual x-ray image to the reference x-ray image; 4. The computer-implemented method of claim 1, comprising:

5. The at least one parameter for adjusting the position of the X-ray emitter is: a parameter for adjusting the distance from the x-ray emitter to the patient; and a parameter for adjusting the rotation angle of the X-ray emitter; including one or more of 5. A computer-implemented method according to any one of claims 1 to 4.

6. providing a command signal to guide a user to manually adjust a position of the X-ray emitter based on the at least one determined parameter; 6. The computer-implemented method of claim 1, comprising:

7. providing a control signal usable to control a mobile X-ray robot arm of the mobile X-ray device to adjust the position of the X-ray emitter based on the at least one determined parameter; 7. The computer-implemented method of claim 1, comprising:

8. The reference X-ray image a patient's previously acquired x-ray image of the internal body structure; and Reference X-ray images from the atlas, including one or more of A computer-implemented method according to any one of claims 1 to 7.

9. An X-ray emitter position determining device comprising a processing unit configured to perform the steps of the method according to any one of claims 1 to 8.

10. a mobile X-ray device having an X-ray emitter; a camera mountable to the mobile x-ray device and configured to capture camera-acquired images from a patient during an x-ray imaging session, the camera being aligned with an origin of the x-ray emitter; and an X-ray detector; a tracker device attachable to or embedded in the X-ray detector and configured to provide position information of the X-ray detector; an X-ray emitter position determination device according to claim 8, configured to determine at least one parameter for adjusting the position of the X-ray emitter; An X-ray imaging system comprising:

11. the tracker device a marker device; A gyroscope and The antenna and including one or more of The X-ray imaging system according to claim 10.

12. the mobile X-ray device further comprising a mobile X-ray robotic arm supporting the X-ray emitter; the X-ray emitter positioning device is configured to provide a control signal to control the mobile X-ray robotic arm of the mobile X-ray device to adjust the position of the X-ray emitter.

12. The X-ray imaging system according to claim 10 or 11.

13. a display configured to display instructions provided by the X-ray emitter positioning device to guide a user to manually adjust the position of the X-ray emitter; 13. The X-ray imaging system according to claim 10, further comprising:

14. A computer program comprising instructions which, when executed by a processing unit, cause the processing unit to perform the steps of the method according to any one of claims 1 to 8.

15. A computer readable medium storing the computer program of claim 14.

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