Method for generating a volume model of an object to be examined, control device, X-ray device, computer program and electronically readable data medium

Abstract

To provide means for compensating for a truncation for cone beam computed tomography based on two X-ray spectra.SOLUTION: A data set 9 includes first 2D projection images 7 of an object 5 created in a first X-ray spectrum and second 2D projection images 8 of the object 5 created in a second X-ray spectrum. The method includes: supplementing the 2D projection images 7, 8 with fill projections; reconstructing a preliminary volume model 19 of the object 5 from the supplemented 2D projection images 7, 8; determining a spectral X-ray absorption property of a volume element 18; generating virtual 2D projection images 20 on the basis of the preliminary volume model 19 of the object 5 as a function of the spectral X-ray absorption property of the volume element 18; and reconstructing a volume model 11 of the object 5 on the basis of the virtual 2D projection images 20 and / or on the basis of the supplemented 2D projection images 7, 8 and the virtual 2D projection images 20.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for generating a volume model of an examination object, and also to a control device, an X-ray device, a computer program and an electronically readable data medium. [Background technology]

[0002] Cone beam computed tomography (CBCT) and limited angle tomography are imaging techniques for producing reconstructed images of an object being examined.

[0003] In both of these methods, an X-ray source irradiates the object under examination with X-rays, and a main beam area diverges from the X-ray source. In cone-beam computed tomography, this main beam area is cone-shaped. Opposite the X-ray source is a flat-panel detector, which detects the transmitted X-rays as a two-dimensional projection image of the object under examination. The object under examination is positioned between the X-ray source and the flat-panel detector. To enable the reconstruction of a reconstructed image of the object under examination, a data set is acquired, which includes multiple two-dimensional projection images of the object under examination.

[0004] To acquire two-dimensional projection images of the test object for a data set, the x-ray source and flat panel detector are moved around the test object along a generally circular trajectory. At each position along the trajectory, a respective two-dimensional projection image of the test object is generated. The data set thus includes multiple two-dimensional projection images that depict the test object at various orientations. The two-dimensional projection images of the data set are processed using a predetermined reconstruction method to generate a reconstructed image of the test object.

[0005] For optimal reconstruction of an object in cone-beam computed tomography (CBCT), the X-rays must penetrate the object at every position, i.e., the entire object must be located within the cone-shaped main beam area during the acquisition of each projection image.

[0006] One special type of cone beam computed tomography (CBCT) is dual energy cone beam computed tomography (DE-CBCT), in which two different x-ray spectra are used to obtain two-dimensional projection images of the examined object.

[0007] Three-dimensional reconstruction and spectral evaluation of two-dimensional projection images from dual-energy cone-beam computed tomography are particularly challenging due to a series of effects, including scatter, beam hardening, metal artifacts, and cupping artifacts, among others.

[0008] On the other hand, a particular challenge arises when truncation occurs in the two-dimensional projection images of dual-energy cone-beam computed tomography: when truncation occurs, parts of the patient or examined object are not imaged over the entire angular range required for three-dimensional reconstruction, and as a result, these parts of the patient provide insufficient information for the reconstruction.

[0009] For conventional cone-beam computed tomography (CT), which uses only one imaging spectrum, numerous correction methods are known to address truncation and related reconstruction artifacts. However, this model does not take into account the very spectral effects, which results in reconstruction artifacts in the model for dual-energy cone-beam CT.

[0010] A fundamental problem with limited-angle tomography is that the two-dimensional projection images are acquired only over a relatively limited angular range, which can likewise lead to reconstruction artifacts during reconstruction.

[0011] From the prior art in this subject area, the following methods are known, but these methods do not address the above problem:

[0012] The publication "Non-Patent Document 1" describes a dual-energy fan-beam CT technique based on deep learning methods. This publication relates to a dual-energy CT scanner with a larger image reconstruction volume for the first X-ray spectrum than for the second X-ray spectrum. In this case, a global 3D reconstruction is available for the first spectrum, while a truncation is performed for the second spectrum. The 3D reconstruction of the second X-ray spectrum is predicted using a neural network based on the 3D reconstruction of the first X-ray spectrum.

[0013] Although the publication "Non-Patent Document 2" describes a method for correcting for truncation, this correction does not cover the case where two X-ray spectra are used. [Prior art documents] [Non-patent literature]

[0014] [Non-Patent Document 1] Clark, Darin P., et al. "Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography." Medical physics 47.9 (2020): 4150-4163. [Non-patent document 2] Huang, Yixing, et al. "Data extrapolation from learned prior images for truncation correction in computed tomography." IEEE Transactions on Medical Imaging 40.11 (2021): 3042-3053. [Non-patent document 3] Maass, Nicole, et al. “Empirical multiple energy calibration EMEC for material-selective CT.” 2011 IEEE Nuclear Science Symposium Conference Record. IEEE, 2011. Summary of the Invention [Problem to be solved by the invention]

[0015] The object of the present invention is to provide a solution for compensating for truncation in tomography methods in which projection images are acquired using at least two X-ray spectra, in particular for dual-energy cone-beam computed tomography and limited-angle tomography. [Means for solving the problem]

[0016] This problem is solved by the subject matter of the independent claims. Advantageous developments and preferred embodiments are the subject matter of the dependent claims.

[0017] According to the present technique, a truncated model of the examined object is created from two-dimensional projection images of measurements for dual energy cone beam computed tomography and limited angle tomography using spectral characteristics and / or assumed material composition.

[0018] A first aspect of the invention relates to a method for generating a volume model of a study object from a data set.

[0019] The data set includes a first two-dimensional projection image of the test object acquired from the test object at a first X-ray spectrum. The data set also includes a second two-dimensional projection image of the test object acquired from the test object at a second X-ray spectrum. In other words, the data set includes two-dimensional projection images of the test object at two X-ray spectra that map independently of each other. The X-ray spectra can include various energy spectra.

[0020] Each of the two-dimensional projection images maps a projection volume of the object under test, which is located in the field of view of the respective two-dimensional projection image. In other words, each of the two-dimensional projection images includes a respective object projection that maps the projection volume of the object under test. Each projection volume is a partial volume of the entire volume of the object under test. The projection volume is a volume that is located in the respective field of view of the X-ray device when each of the two-dimensional projection images is created. The projection volume of a two-dimensional projection image is identical to the entire volume of the object under test if the object under test is entirely located within the field of view of the X-ray device when the two-dimensional projection image is captured. If the object under test is not entirely located within the field of view, the two-dimensional projection image maps only the portion of the entire volume of the object under test that is within the field of view. In this case, the projection volume does not entirely include the entire volume. Therefore, the two-dimensional projection image lacks a missing region that would map the portion of the entire volume outside the projection volume. Because the entire volume of the object under test is not captured in the two-dimensional projection image, information for reconstructing the missing region is missing. This region is called a truncated volume. On the other hand, the portion of the overall volume that was located within each field of view when each of the two-dimensional projection images was acquired is referred to as the primary acquisition region.

[0021] The method comprises steps performed by a controller.

[0022] The first step comprises receiving, by the control device, a data set from an X-ray device, which may be a device for performing CT examinations producing two-dimensional projection images of the object under examination.

[0023] In a next step, the two-dimensional projection images are interpolated, the interpolating step including extending at least one of the truncated two-dimensional projection images of the first two-dimensional projection image and / or the truncated two-dimensional projection image of the second two-dimensional projection image with a fill projection, the fill projection mapping a filled volume of the object under test that is located outside the field of view of the at least one truncated two-dimensional projection image.

[0024] The filled volume is a portion of the object under test that is located outside the field of view of each of the two-dimensional projections. In other words, it is a portion of the entire volume of the object under test that is located outside the projection volume mapped by the two-dimensional projections. In order to be able to consider the portion of the entire volume that is not included in the projection volume of each of the two-dimensional projections, the individual two-dimensional projections are configured to be interpolated by the filled projection. In other words, the individual two-dimensional projections are interpolated and / or extrapolated to compensate for missing areas that occur because the object under test is not entirely located within the field of view. Therefore, it is not necessary to interpolate all of the two-dimensional projections, but only those that are truncated from the object under test.

[0025] In a step of the method, a provisional volume model of the test object is reconstructed based on the interpolated two-dimensional projection images. In other words, the interpolated two-dimensional projection images, including at least one extended truncated two-dimensional projection image, are configured to be used to reconstruct the provisional volume model of the test object. The reconstruction can be performed by a conventional reconstruction method according to the prior art.

[0026] In a further step, spectral X-ray absorption properties of at least one volume element are determined, the at least one volume element being located within a filled volume of the at least one truncated two-dimensional projection image. In other words, the at least one volume element is located within a truncated volume of the provisional volume model. The spectral X-ray absorption properties are determined in response to the filled two-dimensional projection image.

[0027] In a next step, virtual 2D projection images of the examination object are generated for both X-ray spectra based on the provisional volume model of the examination object. The virtual 2D projection images are generated depending on the spectral X-ray absorption properties of at least one volume element. In other words, a virtual examination of the examination object is performed by the control device. This results in the generation of virtual 2D projection images of the provisional volume model. When generating the virtual 2D projection images, the spectral X-ray absorption properties of the at least one volume element are taken into account. In other words, it is possible to simulate, for example, the absorption of the beam of the X-ray spectrum depending on the X-ray absorption properties of the at least one volume element.

[0028] In a next step, a volume model of the object to be examined is reconstructed based on the virtual 2D projection images or based on the interpolated 2D projection images and the virtual 2D projection images. Reconstruction methods known from the prior art can be applied. The reconstruction can be based solely on the virtual 2D projection images. Alternatively, the reconstruction can be based on the interpolated 2D projection images and the virtual 2D projection images. For example, the virtual 2D projection images can be used for regions of the object to be examined that were not illuminated through the acquired 2D projection images.

[0029] The present invention advantageously provides the ability to compensate for truncation in dual energy cone beam computed tomography.

[0030] The invention also includes developments that provide further advantages.

[0031] In one development of the invention, the volume model of the examination object is configured to include a single-energy volume model of the examination object for the first X-ray spectrum and a single-energy volume model of the examination object for the second X-ray spectrum. In other words, the volume model includes two mutually independent single-energy volume models for each X-ray spectrum. The single-energy volume model of the examination object for the first X-ray spectrum is configured to be reconstructed based on a virtual two-dimensional projection image generated for the first X-ray spectrum or based on a supplemented first two-dimensional projection image. In other words, the reconstruction of the single-energy volume model of the examination object for the first X-ray spectrum is performed based on a virtual two-dimensional projection image generated for the first X-ray spectrum. Alternatively, the reconstruction is performed based on the first projection image and a virtual two-dimensional projection image generated for the first X-ray spectrum. Correspondingly, the single-energy volume model of the examination object for the second X-ray spectrum is configured to be reconstructed based on a virtual two-dimensional projection image generated for the second X-ray spectrum or based on a supplemented second two-dimensional projection image. In other words, the reconstruction of the single-energy volume model of the examination object for the second X-ray spectrum is performed based on the virtual two-dimensional projection images generated for the second X-ray spectrum. Alternatively, the reconstruction is performed based on the second projection images and the virtual two-dimensional projection images generated for the second X-ray spectrum.

[0032] In one development of the invention, the volume model of the examination object is configured to include dual-energy volume models of the examination object for both X-ray spectra, in other words, a dual-energy volume model characterizing the examination object in both X-ray spectra is generated, and the dual-energy volume model of the examination object for both X-ray spectra is configured to be reconstructed based on virtual two-dimensional projection images generated in both X-ray spectra or based on a first interpolated two-dimensional projection image and a second interpolated two-dimensional projection image.

[0033] In one development of the invention, the step of completing the 2D projection image includes a deep learning method. In other words, the 2D projection image is completed with a filled projection by applying a deep learning method. For example, the artificial neural network underlying the deep learning method can be trained on 2D projection images and / or volume models of objects of the object category. This allows the truncated region of the object to be reconstructed based on known objects. One possible method is disclosed in the publication "Non-Patent Document 2." For example, the truncated 2D projection image can be provided to the artificial neural network as input data together with further information about the object. Further 2D projection images can also be provided as input data. The neural network can complete the truncated 2D projection image with a filled projection and reconstruct it as output data.

[0034] In one development of the invention, the step of completing the two-dimensional projection image comprises providing a filled projection based on a predetermined standard model of the test object. In other words, a standard model of the test object is provided for use in the completion by the filled projection. For example, the standard model can be a water cylinder model. When generating the filled projection, the test object can be assumed to be located in a cylinder volume made of water.

[0035] In one development of the invention, the method is configured to include a step of reconstructing a provisional volume model of the examination object based on the uninterpolated two-dimensional projections, i.e., generating the provisional volume model based on the uninterpolated two-dimensional projections before generating the provisional volume model based on the interpolated two-dimensional projections, which has the advantage that the 2D projections can be interpolated taking into account the untruncated volume of the provisional volume model.

[0036] In one development of the invention, the method comprises identifying a material in at least one subregion of the two-dimensional projection based on a model of the subregion. In other words, the method is configured to identify the material of the subregion by performing an identification method on at least one subregion of the two-dimensional projection. Models assigned to various materials can be stored in a database of the control device. Bone material can be distinguished from soft tissue, for example, by the characteristic structure of the model. The materials can be assigned respective spectral absorption properties in the database. This allows the spectral absorption properties of at least one volume element to be determined based on the material.

[0037] In one development of the invention, the method is configured to include a step of identifying a sub-object volume of at least one sub-object of the test object in the provisional volume model, in other words, checking the provisional volume model for the presence or absence of a sub-object volume.

[0038] In the next step, the subobject volume is interpolated as a filled volume in the provisional volume model. In other words, a part of the subobject volume may be located outside the main acquisition region, so that it is not possible to completely reconstruct the subobject volume based on the uninterpolated 2D projection images. By identifying the subobject, the subobject volume of the subobject can be retrieved from the database, and the subobject volume can be interpolated in the volume model.

[0039] In one development of the invention, the method is configured to include a step of identifying image features of at least one subobject of the test object in the two-dimensional projection image. In other words, the method is configured to identify image features assigned to the subobject of the test object in the two-dimensional projection image. The image features may be, for example, contours, edges, or points of the subobject. The image features can be identified in the two-dimensional projection image by the control device using image recognition methods. For example, the method can be configured to provide a database to the control device that can describe the subobjects and / or the image features of the subobjects.

[0040] In a next step, the position of the subobject volume of at least one subobject in the volume model of the object under examination is determined depending on the position of each image feature of the at least one subobject in the two-dimensional projection image. In other words, image features of the at least one subobject are identified in each two-dimensional projection image of the object under examination, and the positions of each of these image features in the projection image are determined. Based on the positions of the image features assigned to each subobject, the position of the subobject in the volume model is determined.

[0041] In one development of the invention, at least one volume element is arranged in the truncated sub-object volume, and the step of determining the spectral X-ray absorption properties of the at least one volume element comprises retrieving the spectral X-ray absorption properties of the sub-object from a database. In other words, the spectral X-ray absorption properties can be stored for the sub-object. From the database, the spectral X-ray absorption properties of at least a region of the sub-object volume containing the at least one volume element can be retrieved. The spectral X-ray absorption properties can be assigned to the volume element.

[0042] In one development of the invention, the step of determining the spectral X-ray absorption properties of at least one volume element comprises the steps of: identifying a surface element assigned to the at least one volume element in one of the first two-dimensional projections, i.e., identifying a surface element that maps the volume element in the first two-dimensional projection; and identifying a surface element assigned to the at least one volume element in one of the second two-dimensional projections, i.e., identifying the surface element in the second two-dimensional projection. This makes it possible to provide projection values, also referred to as signal intensity values, acquired at the surface element for determining the X-ray absorption properties of the volume element. In a subsequent step, the spectral X-ray absorption properties of the volume element are determined based on the projection values ​​of the surface element in the two-dimensional projections of both X-ray spectra.

[0043] One development of the invention is configured to include the following steps executed by the X-ray device:

[0044] acquiring a first two-dimensional projection image of the test object at a first X-ray spectrum; and acquiring a second two-dimensional projection image of the test object at a second X-ray spectrum. One step includes providing a data set including the first two-dimensional projection image and the second two-dimensional projection image to a controller.

[0045] In one development of the invention, the two-dimensional projection images are generated by dual-energy cone-beam computed tomography. The two-dimensional projection images can be provided to the control device by an external X-ray device. Alternatively, the two-dimensional projection images can be acquired using a method with an X-ray device and provided to the control device.

[0046] In one development of the invention, the two-dimensional projection image is generated by limited angle tomography. The two-dimensional projection image can be provided to the control device by an external X-ray device. Alternatively, the two-dimensional projection image can be acquired using a method with an X-ray device and provided to the control device.

[0047] In other words, the method is applied to two-dimensional projection images obtained by limited-angle tomography. Additional information for limited-angle tomography can be generated, for example, by performing spectral calculations on the projection images multiple times using different X-ray spectra, if necessary. The spectral information can be used as input data for model-based or iterative limited-angle tomography.

[0048] For applications or situations that may arise in the method and that are not explicitly described herein, the method may be configured to output error messages and / or requests for user feedback and / or to set default settings and / or predetermined initial states.

[0049] A second aspect of the invention relates to a control device configured to generate a volume model of a study object from a data set.

[0050] The data set includes a first two-dimensional projection image of the test object created at a first X-ray spectrum and a second two-dimensional projection image of the test object created at a second X-ray spectrum, each of the two-dimensional projection images mapping a projection volume of the test object located in a field of view of the respective two-dimensional projection image. The control device is designed to be configured to receive the data set from the X-ray device. The control device is configured to complement the two-dimensional projection images.

[0051] The control device is configured to complement the two-dimensional projection images by extending at least one truncated two-dimensional projection image of the first two-dimensional projection image and / or the second two-dimensional projection image using the control device with a fill projection, the fill projection mapping a fill volume of the inspection object that is located outside the field of view of the at least one truncated two-dimensional projection image.

[0052] The control device is configured to determine, in response to the interpolated two-dimensional projection image, a spectral X-ray absorption characteristic of at least one volume element arranged within a filled volume of the at least one truncated two-dimensional projection image. The control device is configured to generate, based on a provisional volume model of the examination object, a virtual two-dimensional projection image of an X-ray spectrum of the examination object in response to the spectral X-ray absorption characteristic of the at least one volume element. The control device is configured to reconstruct a volume model of the examination object based on the virtual two-dimensional projection image or based on the interpolated two-dimensional projection image and the virtual two-dimensional projection image.

[0053] The control device may include, in particular, one or more computers, one or more microcontrollers, and / or one or more integrated circuits, such as one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and / or one or more systems-on-chips (SoCs). The computing unit may also include one or more processors, such as one or more microprocessors, one or more central processing units (CPUs), one or more graphic processing units (GPUs), and / or one or more signal processors, in particular one or more digital signal processors (DSPs). The computing unit may also include a physical or virtual computer network of the above units, etc.

[0054] A third aspect of the present invention relates to an X-ray device comprising at least one control device according to the second aspect of the present invention. The X-ray device is configured to acquire a first two-dimensional projection image of an object under examination, made in a first X-ray spectrum, and a second two-dimensional projection image of the object under examination, made in a second X-ray spectrum, each of the two-dimensional projection images mapping a respective object projection of a respective projection volume of the object under examination, each projection volume being a sub-volume of the entire volume of the object under examination, located in a respective field of view of each of the two-dimensional projection images. The X-ray device is configured to supply a data set of the two-dimensional projection images of the object under examination to the control device for generating a volume model of the object under examination.

[0055] A fourth aspect of the present invention relates to a computer program directly loadable into the memory of a control device, the computer program comprising program means for executing the steps of the method when the program is executed on the control device. Thus, the method of the present disclosure may take the form of a computer program product for executing the method on the control device, when the method is executed on the control device.

[0056] A fifth aspect of the invention relates to an electronically readable data medium having stored thereon electronically readable control information, the control information comprising at least one computer program as defined above, the computer program being configured to carry out the method as defined above when the data medium is used in a control device.

[0057] Regardless of the grammatical gender of a particular concept, masculine, feminine, or other gender-identity persons are included in that concept.

[0058] Further features of the present invention will become apparent from the claims, the figures, and the description of the figures. Features and combinations of features set forth in the above description and features and combinations of features set forth in the following description of the figures and / or later in the figures may be included not only in the respective combinations described, but also in other combinations of the present invention. In particular, embodiments and combinations of features of the present invention that do not necessarily have all of the features of the claims originally set forth may also be included. Also, embodiments and combinations of features of the present invention that go beyond or deviate from the feature combinations set forth by backward reference in the claims may also be included.

[0059] The present invention will now be described in more detail with reference to specific exemplary embodiments and associated schematic drawings. In the figures, identical or functionally identical elements may be designated by the same reference numerals. The description of identical or functionally identical elements is not necessarily repeated with respect to different figures. [Brief explanation of the drawings]

[0060] [Figure 1] FIG. 1 shows a schematic diagram of the acquisition of a two-dimensional projection image of an object to be examined by an X-ray device. [Figure 2] FIG. 2 shows a schematic diagram of a two-dimensional projection image of an object under inspection. [Figure 3] Figure 3 shows a schematic diagram of the reconstructed volume model. [Figure 4] FIG. 4 shows a schematic diagram of the method flow. DETAILED DESCRIPTION OF THE INVENTION

[0061] FIG. 1 shows a schematic diagram of the acquisition of a two-dimensional projection image of an object to be examined by an X-ray device.

[0062] The X-ray device 1 may comprise an acquisition device 2 which may include an X-ray source 3 and an acquisition screen 4. The X-ray source 3 may be configured to emit X-rays along a cone-shaped volume in the direction of the acquisition screen 4. The X-ray device 1 may be configured to perform dual-energy cone-beam computed tomography and / or limited-angle tomography. For this purpose, the X-ray device 1 may output X-rays in two different X-ray spectra.

[0063] An inspection object 5 can be arranged between the X-ray source 3 and the acquisition screen 4. X-rays are absorbed by the inspection object 5, so that the acquisition screen 4 acquires two-dimensional projection images 7, 8 of the inspection object 5. The absorption of the X-rays in the inspection object 5 can depend on the X-ray spectrum of the X-rays. X-rays of a first X-ray spectrum can be acquired in the first two-dimensional projection image 7, and X-rays of a second X-ray spectrum can be acquired in the second two-dimensional projection image 8.

[0064] The reconstruction of the volume model 11 of the examination object 5 requires the acquisition of multiple two-dimensional projection images 7, 8 of the examination object 5 from different directions. For this purpose, the X-ray device 1 can be configured to move the X-ray source 3 and the acquisition screen 4 around the examination object 5, for example along a circular trajectory 6. Respective two-dimensional projection images 7, 8 of the examination object 5 in predetermined directions can be acquired in respective X-ray spectra and added to a data set 9. A control device 10 of the X-ray device 1 can be configured to reconstruct the volume model 11 of the examination object 5 based on the first two-dimensional projection image 7 and the second two-dimensional projection image 8.

[0065] In order to reconstruct the volume model 11 of the test object 5 entirely, it is necessary that the entire volume 14 of the test object 5 is acquired entirely during each measurement. For this purpose, the entire volume 14 must be located within a cone during each acquisition of the respective two-dimensional projection images 7, 8, so that the test object 5 is illuminated entirely and the projection images 7, 8 include the test object 5 in all its dimensions. On the other hand, at least in some positions of the acquisition device 2, missing regions 28 of the test object 5 may be located outside the cone and therefore not acquired by the respective projection images 7, 8. In this case, so-called truncation occurs. This can lead to errors in the volume model 11 of the test object 5.

[0066] One sufficiently illuminated region is referred to as the primary reconstruction volume 27. The other region is referred to as the truncation volume 25. The truncation volume 25 may include a volume that was outside the field of view during at least one acquisition of one of the projections 7, 8. For reconstruction, the two-dimensional projections 7, 8 may need to be supplemented with a filled projection 15. The control device 10 may be configured to supplement the two-dimensional projections 7, 8 with the filled projection 15 and / or to supplement a provisional volume model 19 created based on the provisional, unfilled two-dimensional projections 7, 8 with a filled volume 16. For the purpose of supplementation with the filled projection 15, a standard model of the object 5 may be assumed, onto which, for example, a water cylinder may be mapped. In this case, the truncation volume 25 may be supplemented as the filled volume 16 with a water cylinder.

[0067] Within the inspection object 5, subobjects 23 may be arranged, each having a respective subobject volume 24. The subobject volumes 24 may be partially located within the main reconstruction volume 27 and partially located outside the main reconstruction volume 27. To generate the filled volume 16, for example, image features 21 of the subobjects 23 may be identified in the two-dimensional projections 7 and 8, and their positions in the two-dimensional projections 7 and 8 may be determined. Based on this, the control device 10 may determine the positions of the subobject volumes 24 of the subobjects 23 in the volume model 11. The subobject volumes 24 of the subobjects 23 may be stored, for example, in a database of the control device 10. This allows the control device 10 to identify the subobjects 23 in the two-dimensional projections 7 and 8 of the inspection object 5 based on the image features 21. A portion of the subobject volume 24 in the truncated volume 25 that is not mapped in one of the two-dimensional projections 7 and 8 may be completed in the other projection 7 and 8 as a filled projection 15. The missing portion may be determined or estimated based on the subobject volumes 24 stored in the database.

[0068] The sub-object volume 24 can also be identified in a provisional volume model 19 of the object 5, which can be determined based on the uninterpolated two-dimensional projection images 7, 8. X-ray absorption properties can be determined for at least one volume element 18 of the provisional volume model 11. The X-ray absorption properties can be determined, for example, based on a material 22, which can be recognized based on a structure in a sub-region of one of the two-dimensional projection images 7, 8. The X-ray absorption properties of the material 22 can be stored in a database of the control device 10.

[0069] After the provisional volume model 11 is generated, the control device 10 can generate a virtual two-dimensional projection image 20 based on the provisional volume model 18 .

[0070] FIG. 2 shows a schematic diagram of two-dimensional projection images 7, 8 of an object 5 to be inspected.

[0071] The two-dimensional projection images 7, 8 may have object projections 12 that map a projection volume 13 of the test object 5 for each measurement. The projection volume 13 may describe a portion of the entire volume 14 of the test object 5 that is located within the cone and thus within the field of view 17 of the respective measurement. Missing regions 28 outside the object projections 12 may represent portions of the two-dimensional projection images 7, 8 that are filled in.

[0072] Two-dimensional projection images 7, 8 of the object 5 can be generated based on two positions for each X-ray spectrum. Due to the X-ray absorption characteristics of materials, the intensity values ​​of the two-dimensional projection images 7, 8 can differ from each other depending on the X-ray spectrum. This allows, for example, material identification. The contour of the subobject 23 can show an image feature 21 of the subobject 23 that can be identified by the control device 10. Based on the position of the image feature 21, the position of the subobject 23 in the volume model 11 can be determined.

[0073] FIG. 3 shows a schematic diagram of the reconstructed volume model 11 .

[0074] It can be seen that the truncation volume 25 outside the main reconstruction volume 27 is completed by the fill volume 16. As an example, a volume element 18 is shown whose x-ray absorption properties have been determined.

[0075] FIG. 4 shows a schematic diagram of the method flow.

[0076] In step S1, the X-ray device 1 can perform a DE-CBCT scan of the object 5. During the DE-CBCT scan, two-dimensional projection images 7 and 8 can be acquired using first and second X-ray spectra. The DE-CBCT scan can be acquired using known dual-energy techniques, such as dual sources, fast kV switching, fast filter switching, or dual-layer and photon-counting detectors. Both X-ray spectra can be distinguished from each other in at least one wavelength range. As a result, truncation may occur, resulting in missing regions 28 of the object 5, e.g., lateral regions, being cropped out of the two-dimensional projection images 7 and 8. The cropped missing regions 28 may have been located outside the field of view 17 when the projection images 7 and 8 were acquired. The two-dimensional projection images 7 and 8 of the object 5 from the X-ray spectra can be added to a data set 9, which can be provided to a control device 10 for generating a volume model 11.

[0077] Optionally, in step S2, the control device 10 can perform a first image reconstruction for reconstructing the provisional volume model 19 using known steps according to the prior art for reconstructing a main reconstruction volume 27. The non-truncated regions can describe the entire volume 14 of the examined object 5 in the main reconstruction volume 27. The reconstruction can optionally be performed for the two-dimensional projections 7 of the first X-ray spectrum and the two-dimensional projections 8 of the second X-ray spectrum independently of each other. Optionally, a truncated volume 25 shown in both two-dimensional projections 7, 8 of the X-ray spectrum can also be estimated using known methods for correcting for truncation.

[0078] In optional step S3, if possible for the selected dual-energy imaging technique, dual-energy 2D projection images 7, 8 can be calculated based on the corresponding 2D projection images 7, 8 of each x-ray spectrum using weighted subtraction or other image calculation methods. If necessary, the corresponding 2D projection images 7, 8 can be combined with motion compensation.

[0079] In the dual-energy 2D projection images 7, 8, sub-objects 23, such as bone or contrast agents, can be highlighted or alternatively suppressed. Material-specific projection values ​​and / or X-ray absorbance characteristics can be determined based on the corresponding 2D projection images 7, 8 of different X-ray spectra, using methods described, for example, in the publication [3].

[0080] In step S4, sub-objects 23 and / or image features 21 having sufficient contrast can be segmented and / or detected in the 2D projections 7, 8 or in dual-energy 2D projections derived from the respective 2D projections 7, 8. The sub-objects 23 can be, for example, bones, skin surfaces, or contrast agent accumulations, such as contrasted vessels, implants, external objects, or organs. Also, image features 21, such as landmarks, exemplarily described above, of the sub-objects 23 in the 2D projections 7, 8 can be acquired. The entire sub-object volume 24 of the sub-object 23 or at least a portion of the sub-object volume 24 can be acquired.

[0081] Optionally, the subobjects 23 can be classified and / or assigned. For example, one type of bone can be identified as the left elbow, if desired, using the known orientation of the patient on the supine treatment table. Corresponding image features 21 acquired in multiple 2D projections 7, 8 can be used to estimate the bone's location. Similarly, the outline of the patient's arm, body surface, implants, or other organs / objects can be detected in the truncated volume 25.

[0082] In this case, detection can be aided by spectral differences in the two-dimensional projections 7, 8. For example, segmentation of the sub-objects 23 can be performed using a convolutional neural network, using the corresponding two-dimensional projections 7, 8 of different X-ray spectra as input values ​​for the convolutional neural network, which can thereby more easily distinguish between sub-objects 23, especially between overlapping sub-objects 23.

[0083] For example, an anatomical sub-object 23, such as a bone or other sub-object 23, can be highlighted by a two-dimensional dual-energy calculation of the corresponding two-dimensional projections 7, 8 that match the projections 7, 8 of both different X-ray spectra. This sub-object 23 may be located outside the main reconstruction volume 27, particularly due to lateral detector truncation. The dual-energy calculation may also enable detection and triangulation or limited-angle tomography of the sub-objects 23 within the truncation volume 25, allowing material determination or material estimation of the material of these sub-objects 23. This may improve the truncation model for the patient.

[0084] In step S5, the two-dimensional projections 7, 8 can first be extrapolated using deep learning methods such as those described in the publication "Patent Document 2" and / or using a standard model of the examined object 5, e.g., a simple water cylinder model or a patient model. The interpolated image regions, also referred to as filled projections 15, of the two-dimensional projections 7, 8 can then be compared with acquired and / or interpolated image regions of other two-dimensional projections 7, 8.

[0085] In step S6, the provisional volume model 19 can be completed with the sub-object volumes 24 of the acquired sub-objects 23. For example, if a bone or a part of a bone or another detected sub-object 23 is located at least partially within the truncation volume 25, the position of this bone in the volume model 11 and / or its contour can be adjusted and / or if the sub-object volume 24 does not yet exist in at least one region of the volume model 19, this sub-object volume 24 can be newly added to the volume model 19.

[0086] In step S7, a reconstruction of the main reconstruction volume 27 can be performed using the filled projections 15 interpolated in the two-dimensional projection images 7, 8 in areas outside the main reconstruction volume 27, and a provisional volume model 19 of the examined object 5 can be located in the area relevant for imaging. In the truncated area, the spectral X-ray absorption properties of at least one volume element 18 of the provisional volume model 19 can be determined based on the previously performed classification of the sub-object 23.

[0087] In step S8, a virtual two-dimensional projection image 20 can be generated of the provisional volume model 19. In the first and second X-ray spectra, missing regions 28 of the virtual two-dimensional projection images 20 can be interpolated into the two-dimensional projection images 7 and 8.

[0088] In step S9, separate reconstructions of the volume model 11 for both X-ray spectra can be performed independently of each other, taking into account the virtual two-dimensional projection images 20. Alternatively, a common reconstruction of the volume model 11 for both X-ray spectra can be performed to reconstruct the volume model 11 as a dual-energy reconstruction image. The reconstruction can be performed using only the virtual two-dimensional projection images 20. Alternatively, the reconstruction can be performed using the interpolated two-dimensional projection images 7, 8 and the virtual two-dimensional projection image 20. In this case, for example, the interpolated two-dimensional projection images 7, 8 can be at least partially interpolated by the virtual two-dimensional projection image 20. This can be performed, for example, at the edges of the field of view in order to expand the field of view using the virtual two-dimensional projection image 20. This can prevent truncation at the edges of the object 5 to be examined.

[0089] The volume model 11 extracted from the two-dimensional projections 7, 8 of the dual-energy CBCT method can also be used for further correction steps, taking into account material properties. Correction steps can include, for example, scatter correction and / or metal artifact correction. Correction steps can also be used during interventional procedures, for example, when determining the patient's dose distribution.

Claims

1. A method for generating a volumetric model (11) of an object (5) from a dataset (9), comprising: the data set (9) comprises a first two-dimensional projection image (7) of the test object (5) generated in a first X-ray spectrum and a second two-dimensional projection image (8) of the test object (5) generated in a second X-ray spectrum, The two-dimensional projections (7, 8) each map a projection volume (13) of the object (5) to be examined, the projection volume (13) being located in the field of view (17) of each of the two-dimensional projections (7, 8), Executed by the control device (10), receiving said data set (9) from the X-ray device (1); a step of complementing the two-dimensional projections (7, 8), comprising extending at least one truncated two-dimensional projection of the first two-dimensional projection (7) and / or the second two-dimensional projection (8) with a filled projection (15), the filled projection (15) mapping a filled volume (16) of the object (5) that is located outside the field of view (17) of the at least one truncated two-dimensional projection; - reconstructing a provisional volume model (19) of the object (5) based on the interpolated two-dimensional projection images (7, 8); determining spectral X-ray absorption properties of at least one volume element (18) of the provisional volume model (19) in response to the interpolated two-dimensional projection (7, 8), the at least one volume element (18) being located within the filled volume (16) of the at least one truncated two-dimensional projection; generating, based on the provisional volume model (19) of the test object (5), virtual two-dimensional projections (20) of the first and second X-ray spectra of the test object (5) as a function of the spectral X-ray absorption properties of the at least one volume element (18); - reconstructing the volume model (11) of the object (5) based on the virtual two-dimensional projection image (20) or based on the interpolated two-dimensional projection images (7, 8) and the virtual two-dimensional projection image (20); A method for providing the above.

2. the volume model (11) of the inspection object (5) includes a single-energy volume model of the inspection object (5) for the first X-ray spectrum and a single-energy volume model of the inspection object (5) for the second X-ray spectrum; the single-energy volume model of the object (5) for the first X-ray spectrum is reconstructed based on the virtual two-dimensional projection image (20) generated in the first X-ray spectrum or based on the interpolated first two-dimensional projection image (7); the single-energy volume model of the object (5) for the second X-ray spectrum is reconstructed on the basis of the virtual two-dimensional projection image (20) generated in the second X-ray spectrum or on the basis of the interpolated second two-dimensional projection image (8); The method of claim 1.

3. 2. The method of claim 1, wherein the volume model (11) of the object (5) comprises a dual-energy volume model of the object (5) for both X-ray spectra, and the dual-energy volume model of the object (5) for both X-ray spectra is reconstructed based on the virtual two-dimensional projection images (20) generated in both X-ray spectra or based on the interpolated first two-dimensional projection image (7) and the interpolated second two-dimensional projection image (8).

4. The method of claim 1 , wherein the step of completing the two-dimensional projections (7, 8) comprises applying deep learning methods.

5. 2. The method of claim 1, wherein the step of complementing the two-dimensional projection image (7, 8) comprises providing the filled projection (15) based on a predetermined standard model of the inspected object (5).

6. a step of reconstructing a provisional volume model (19) of the object (5) based on the interpolated two-dimensional projection images (7, 8) by the control device (10), which is executed before the step of reconstructing a provisional volume model (19) of the object (5) based on the interpolated two-dimensional projection images (7, 8), 2. The method of claim 1, further comprising the step of: reconstructing the interim volume model (19) of the object (5) based on the uninterpolated two-dimensional projections (7, 8).

7. Executed by the control device (10), - identifying the material (22) of at least one or more subregions of one of said two-dimensional projections (7, 8) based on a model of said subregion; - determining the spectral X-ray absorption characteristic of the at least one volume element (18) as a function of the material (22) of the sub-region; The method of claim 1 further comprising:

8. Executed by the control device (10), - identifying in said provisional volume model (19) a subobject volume (24) of at least one subobject (23) of said test object (5); - Completing the truncated sub-object volumes (24) as filled volumes (16) in the provisional volume model (19); The method of claim 1 further comprising:

9. Executed by the control device (10), - identifying image features (21) of said at least one sub-object (23) of said object (5) in said two-dimensional projection (7, 8); determining the position of the subobject volume (24) of the at least one subobject (23) in the temporary volume model (19) of the test object (5) depending on the position of the image feature (21) of each of the at least one subobject (23) in the two-dimensional projection images (7, 8); The method of claim 8 further comprising:

10. the at least one volume element (18) is disposed within the truncated sub-object volume (24); determining the spectral X-ray absorption characteristics of the at least one volume element (18) comprises retrieving the spectral X-ray absorption characteristics of the sub-object (23) from a database; The method of claim 8, comprising:

11. Determining the spectral X-ray absorption characteristics of the at least one volume element (18) comprises: - identifying a surface element (26) assigned to said volume element (18) in one of said first two-dimensional projections (7); - identifying the surface elements (26) assigned to the volume elements (18) in one of the second two-dimensional projections (8); determining the spectral X-ray absorption characteristics of the volume element (18) based on the projection values ​​of the surface element (26) in the two-dimensional projection images (7, 8) of both X-ray spectra; The method of claim 1 , comprising:

12. Executed by the X-ray device (1), - acquiring the first two-dimensional projection image (7, 8) of the object under examination (5) in the first X-ray spectrum; - acquiring the second two-dimensional projection image (7, 8) of the object under examination (5) in the second X-ray spectrum; - providing said data set (9) comprising said first two-dimensional projection image (7) and said second two-dimensional projection image (8) to said control device (10); The method of claim 1 further comprising:

13. 13. The method according to claim 12, wherein the two-dimensional projection images (7, 8) are acquired by the X-ray device (1) by dual energy cone beam computed tomography.

14. 13. The method according to claim 12, wherein the two-dimensional projection images (7, 8) are acquired by the X-ray device (1) by limited angle tomography.

15. A control device (10) configured to generate a volumetric model (11) of an object (5) from a dataset (9), comprising: the data set (9) comprises a first two-dimensional projection image (7) of the test object (5) generated in a first X-ray spectrum and a second two-dimensional projection image (8) of the test object (5) generated in a second X-ray spectrum, the two-dimensional projections (7, 8) map a projection volume (13) of the object (5) to be examined, the projections (7, 8) being arranged in a field of view (17) of each of the two-dimensional projections (7, 8), The control device (10) receiving said data set (9) from the X-ray device (1); - completion of the two-dimensional projection images (7, 8), by extending at least one truncated two-dimensional projection image of the first two-dimensional projection image (7) and / or the second two-dimensional projection image (8) with a filled projection (15), which maps and completes a filled volume (16) of the object (5) that is located outside the field of view (17) of the at least one truncated two-dimensional projection image; - reconstructing a provisional volume model (19) of the object (5) based on the interpolated two-dimensional projection images (7, 8); determining, in response to the interpolated two-dimensional projection image (7, 8), a spectral X-ray absorption characteristic of at least one volume element (18) arranged within the filled volume (16) of the at least one truncated two-dimensional projection image; generating a virtual two-dimensional projection image (20) of the first X-ray spectrum and the second X-ray spectrum of the test object (5) based on the provisional volume model (19) of the test object (5) as a function of the spectral X-ray absorption properties of the at least one volume element (18); reconstructing the volume model (11) of the object (5) based on the virtual two-dimensional projection image (20) or based on the interpolated two-dimensional projection images (7, 8) and the virtual two-dimensional projection image (20); A control device (10) characterized by being configured as follows.

16. X-ray device (1) comprising at least one control device (10) according to claim 15.

17. A computer program directly loadable into the memory of a control device (10) according to claim 15, comprising program means for carrying out the steps of the method according to any one of claims 1 to 14 when the program is executed in the control device (10).

18. 15. An electronically readable data medium having stored thereon electronically readable control information, the control information comprising a computer program which, when executed in at least one of the control devices (10), performs the steps of the method according to any one of claims 1 to 14, the electronically readable data medium being configured to perform the method according to any one of claims 1 to 14 when the data medium is used in the control device (10).

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