Predicting artifacts in 3D imaging

Abstract

The invention relates to a method of estimating artifacts in 3D imaging by providing a 3D mask (14) representing an object. The X-rays simulating the X-ray source-detector pair (2, 3) pass through the object at a plurality of projection positions of the X-ray source-detector pair (2, 3) moving along a predetermined trajectory. An artifact value is assigned to each voxel of the 3D artifact image (19) according to a respective path length of the X-rays passing through the 3D mask. Visualization of a corresponding artifact map for a current C-arm tilt enables interactive optimization of the C-arm tilt.

Description

Technical Field

[0001] This invention relates to a method for estimating artifacts in 3D imaging, including the step of providing a 3D mask representing the object. Furthermore, this invention relates to an X-ray apparatus. Background Technology

[0002] Individuals with male or female gender identity are included in this term, regardless of grammatical usage.

[0003] Mobile C-arm systems are used for guidance using 2D imaging during orthopedic and trauma procedures, and are increasingly being used for 3D verification of implant placement using cone-beam computed tomography (CBCT) capabilities. Intraoperative verification of correct implant placement using 3D imaging is particularly crucial in the treatment of spinal fractures, as the placement of the anatomy of interest and screws within them is difficult to verify on projected images. In this context, metallic artifacts appearing in and around metallic objects in CBCT reconstruction obscure clinically relevant image features and thus complicate clinical decision-making.

[0004] Because metal artifacts in CBCT images alter the shape of metal implants and obscure the depicted anatomical structures surrounding these objects (e.g., the bone around the axis of a screw is hidden by metal artifacts), the usefulness of the imaging modality for intraoperative verification is drastically reduced.

[0005] The so-called Metal Artifact Reduction (MAR) method in post-processing fails when artifacts are significant. Image inpainting-based methods implemented in production (e.g., FS-MAR Frequency Split metal artifact reduction; https: / / pubmed.ncbi.nlm.nih.gov / 22482612 / ) first segment the metal voxels in the initial reconstruction, then process the projection data in the segmented projection traces to remove the contribution of the metal to the reconstruction. In the case of strong artifacts, deformation of the metal object leads to over-segmentation / under-segmentation of the object, which in turn negatively impacts algorithm performance.

[0006] Another class of methods, known as Metal Artifact Avoidance (MAA), seeks to improve the quality of the acquired raw data by adjusting the source-detector trajectory during image acquisition. By avoiding projected images with large metal biases, images reconstructed from this higher-fidelity data exhibit fewer artifacts. Since the optimized trajectory depends on the location, shape, and orientation of the metal object, additional X-ray images, so-called reconnaissance views, are required to predict the artifact-avoidance trajectory for the scene to be imaged. The trajectory generated by this optimization can be circular or non-circular. While the literature reports superior performance of non-circular trajectories, circular trajectories are easier to implement due to regulatory and practical reasons.

[0007] Wu, P., Sheth, N., Sisniega, A., Uneri, A., Han, R., Vijayan, R., Vagdargi, P., Kreher, B., Kunze, H., Kleinszig, G., Vogt, S., Lo, SF, Theodore, N., Siewerdsen, JH: C-arm orbits for metal artifact avoidance (MAA) in cone-beam CT; Physics in Medicine and Biology 65(16)(8 2020), https: / / doi.org / 10.1088 / 1361-6560 / ab9454 investigated the feasibility of optimizing circular or non-circular trajectories given several reconnaissance view X-ray images.

[0008] Rohleder, M., Kunze, H., Kleinszig, G., Maier, A., Kreher, B.: Robust 3D Digital Segmentation from X-Ray Projection Images for Metal Artifact Trajectory Optimization, Medical Imaging 2024: Physics of Medical Imaging (2024) https: / / doi.org / 10.1117 / 12.3005260 illustrates an end-to-end deep learning approach for back projection and subsequent segmentation of metal objects.

[0009] Siddon, RL: Fast calculation of the exact radiological path for a three-dimensional CT array, Medical Physics 12(2), 252-255(3 1985). https: / / doi.org / 10.1118 / 1.595715 introduces ray-traced forward projection for a given C-arm tilt.

[0010] US11790525 B2 introduced the concept of Metal Artifact Avoidance (MAA). This patent uses trajectory scores in the form of an objective function, which is a single score for each trajectory, and is therefore known as global MAA. Summary of the Invention

[0011] The purpose of this invention is to predict the accuracy of metal workpieces.

[0012] According to the present invention, this objective is achieved by the method and X-ray apparatus according to the independent claims. Furthermore, corresponding computer programs and computer-readable media are provided.

[0013] Advantageous further improvements are obtained from the dependent claims.

[0014] Specifically, a method for estimating artifacts in 3D imaging is provided. Artifacts occur in 3D imaging when, for example, a patient is examined by X-ray imaging and the patient has screws or other metal objects implanted in a part of their body (volume of interest). Metal artifacts obscure the anatomical structures surrounding the metal object. For example, the bone area where a metal screw is screwed is hidden by a metal artifact and is not shown in the X-ray image. Other objects, such as bone structures, can also cause artifacts. This method should be used to estimate or predict such artifacts.

[0015] In one step of the method of the present invention, at least one 3D mask representing an object is provided (when referring to a mask hereinafter, it may also mean several masks). The object may be one of a plurality of objects. Furthermore, the object may consist of multiple individual parts. For example, there are several screws in the spine of a patient. One of the screws or the overall arrangement of the screws may be considered as a metallic object. The 3D mask may contain voxels or parametric descriptions of the object.

[0016] In another step of the method of the invention, X-rays from the simulated X-ray source-detector pair pass through the object at multiple projection positions of the X-ray source-detector pair moving along a trajectory. For example, in a C-arm system, the source-detector pair moves along a circular track (trajectory) via a C-arm. However, the source-detector pair may also move along a trajectory other than a circular one. The source-detector pair occupies a protective position on the trajectory. In the case of a C-arm system, the X-ray source and X-ray detector are mounted at the end of the C-arm and positioned opposite each other in the projection positions. The corresponding X-rays at different projection positions of the X-ray source-detector pair are simulated as they pass through the object or object model.

[0017] Another step of the method of the present invention includes assigning artifact values ​​to each voxel of the 3D artifact image based on the corresponding path length of the X-rays passing through the 3D mask. The assignment can be performed by any function or relation (e.g., linear function, quadratic function, lookup table, etc.).

[0018] In one embodiment, the step of assigning artifact values ​​to each voxel of a 3D artifact image additionally depends on one or more other parameters provided by or to the X-ray source-detector pair of the associated X-ray system. Therefore, when assigning artifact values ​​to voxels, specific parameters characterizing the actual examination conditions can be considered.

[0019] According to another embodiment, at least one of the additional parameters includes metadata of the X-ray system, navigation data for navigating the object between the X-ray source-detector pair, or geometric data of the object. For example, the metadata may include control data of the X-ray system. The navigation data may include position data of the object within the X-ray system. The geometric data may be related to the patient's obesity.

[0020] In a specific embodiment, the method includes the step of determining a corresponding 2D path length projection for each projection location, wherein each 2D path length projection includes a value representing the corresponding path length of one of the simulated X-rays passing through the object. Therefore, the simulated X-rays at each projection location are used to generate a corresponding 2D projection for that projection location. The 2D path length projection is obtained by calculating the line integral along the simulated X-rays, thereby accumulating the trajectory or path length in the object. For example, if the simulated X-rays have a long trajectory length passing through the object, then a pixel of the 2D path length projection is dark. Otherwise, if the X-rays do not travel through the object, then the pixel of the 2D path length projection is white.

[0021] In a further step, corresponding artifact values ​​can be assigned to each path length, thereby obtaining a 2D artifact projection set from the 2D path length projection. This means that the 2D path length projection obtained from the above steps is transformed into a 2D artifact projection. The transformation is performed by assigning a corresponding artifact value to each path length value. After the transformation, each pixel of the 2D artifact projection represents an artifact value. Therefore, absolute artifact intensity can be used for each local location. Thus, the 2D artifact projection provides local resolution of absolute artifact intensity.

[0022] Finally, a back projection can be performed on this set of 2D artifact projections to obtain a 3D artifact image. Therefore, each voxel in the 3D artifact image represents the absolute artifact intensity. In other words, the 3D artifact image represents the object as an artifact intensity value. Thus, the 3D artifact image shows the volumetric resolution of the artifacts generated from the object.

[0023] The advantage of the method of this invention is that it does not calculate a single score for each scene, which would prevent the localization of expected artifacts. According to the method of this invention, more precisely, it calculates where expected artifacts are located in the 3D scene after image acquisition. This allows for adjustment of trajectory optimization on the region of interest and also allows the user to control image quality at the object level.

[0024] In a particular embodiment, one or more 3D masks are obtained by reconstructing a 2D X-ray reconnaissance view. Typically, such a reconnaissance view is obtained for physician orientation. The reconnaissance view is usually obtained at a low dose. One or more 3D masks can be generated by backprojection.

[0025] According to another embodiment, the 3D mask represents the object and at least one additional object, and the 3D mask is obtained based on segmented objects. This means that at least two objects exist in the volume of interest. For example, there are two or more screws in the spinal region to be examined. Metal objects are segmented to reliably separate them. For such segmentation, conventional segmentation algorithms can be used.

[0026] According to another embodiment, the trajectory has a principal plane that is tilted in a pre-given coordinate system. For example, the trajectory is a circular track with a principal plane perpendicular to the central axis of the track. The principal plane can be tilted at a certain angle (tilt angle). This tilt angle can be zero or can have a positive or negative value. Typical tilt angles are in the range of -30 to 30 degrees. Generally, such tilting results in improved X-ray images.

[0027] In another embodiment, the 3D artifact image is projected forward onto one of the reconnaissance views to obtain an overlay image. Thus, the artifacts are integrated into the reconnaissance views, allowing physicians to use familiar reconnaissance views rich in artifact information.

[0028] In another embodiment, artifact intensity is color-coded in the 2D artifact projection or 3D artifact image. Color coding makes it easier to identify the quality of the X-ray image. For example, red pixels or voxels indicate areas with strong artifacts, while blue pixels or voxels indicate smaller artifacts. In this case, the user can easily identify and locate areas with strong artifacts within the volume of interest.

[0029] Specifically, the color used for color encoding can be calibrated by determining a specific color for the artifact threshold of the reconstructed calibration phantom (e.g., a metal wedge). The length of the metal through the metal wedge increases along one dimension. Therefore, a specific metal length can be selected in the X-ray image, resulting in artifacts that are still acceptable. A corresponding color (e.g., red) can be assigned to that threshold length. Such calibration results in color-coded artifacts that can be compared with each other. Thus, calibrated color encoding allows for the prediction of absolute artifact intensity. While previous methods only predicted a fraction of a trajectory and only allowed relative comparisons between different trajectories, the method of the present invention can indicate whether a trajectory will result in good or bad image quality. This is based on calibration, which makes voxel effects comparable across different scenes. This allows users to decide, for example, whether the tilt of the C-arm is entirely necessary.

[0030] Additionally, a method for adjusting an X-ray apparatus is provided, which involves estimating artifacts as described above and adjusting the tilt of the X-ray source-detector pair's trajectory based on a 3D artifact image or a forward projection of the 3D artifact image. Adjusting the tilt of the X-ray source-detector pair's trajectory allows for optimization of the X-ray path through the object. Generally, a shorter path through the object results in fewer artifacts. Therefore, by tilting the trajectory appropriately, artifacts can be reduced or optimized.

[0031] In a particular embodiment, adjustments are performed automatically to minimize the artifact values ​​of the 3D artifact image or the orthogonal projection of the 3D artifact image by changing the tilt of the X-ray source-detector pair. Specifically, artifacts in a particular region of interest should be reduced below a certain threshold. This can be achieved by changing the tilt of the principal plane of the trajectory (i.e., the plane of the X-ray source-detector pair) until the artifact values ​​are below the specific threshold. This adjustment can be performed automatically or manually. Manual adjustment requires interactive control of the X-ray apparatus for the X-ray source-detector pair.

[0032] Furthermore, in another embodiment, the artifact values ​​of a region in the 3D artifact image, or the artifact values ​​of the forward projection of the 3D artifact image, are minimized, and this region is specified by the task. Therefore, artifact optimization is task-specific. For example, the task might aim to optimally image a small group of screws. In this case, artifacts of that group of screws can be minimized, and artifacts of screws outside this region are irrelevant.

[0033] As described above, adjustments can also be performed interactively by the operator of the X-ray source-detector pair, and the 3D artifact image or the orthogonal projection of the 3D artifact image can be updated when the tilt changes. Therefore, the operator can easily identify whether a specific change in tilt leads to better results.

[0034] As mentioned above, the object can be a metallic object or at least a part of a human or animal body. For example, the object is the skeletal structure of a shoulder or skull. A 3D mask can be considered a model of the object.

[0035] The above objective is also achieved by an X-ray apparatus comprising an X-ray source-detector pair and an estimation device capable of performing the above methods. The estimation device may include a computer or processor for calculating the corresponding metal artifacts.

[0036] The advantages and further developments of the above method are similarly applicable to X-ray equipment. Therefore, the described method steps can be regarded as functional features of X-ray equipment.

[0037] In a specific embodiment, the X-ray source-detector pair of the X-ray device is fixed at the C-arm. Therefore, the X-ray device refers to a C-arm system.

[0038] Alternatively, a computer program including instructions may be provided, which, when executed by the aforementioned computer or X-ray device, cause the computer or X-ray device to perform the methods defined above.

[0039] In addition, a computer-readable medium including instructions that, when executed by the computer or X-ray device described above, cause the computer or X-ray device to perform the methods defined above. Attached Figure Description

[0040] The invention will now be described in more detail with reference to the accompanying drawings, in which:

[0041] Figure 1 Main layout of the C-arm system;

[0042] Figure 2 A block diagram of the workflow for localizing and optimizing the interactive circular orbit for predicting metal artifacts.

[0043] Figure 3 A C-arm with corresponding coordinates;

[0044] Figure 4 Phantoms used for calibrating CBCT systems;

[0045] Figure 5 X-ray images of the phantom; and

[0046] Figure 6 Color voxel influence map. Detailed Implementation

[0047] The following examples illustrate preferred embodiments of the present invention.

[0048] Figure 1An example of a single-plane X-ray system with a C-arm 2 is shown, held by a support 1 in the form of a six-axis industrial or multi-joint robot. At its end, an X-ray radiation source, such as an X-ray source 3 with an X-ray tube and collimator, and an X-ray image detector 4 are attached as image acquisition units. The implementation of the X-ray diagnostic equipment does not rely on an industrial robot. Conventional C-arm devices can also be used.

[0049] The patient 6, or the technical object to be examined, is positioned on the platform 5 of the positioning stage in the beam path of the X-ray emitter 3. A system control unit 7, with a computer 8 for image processing, is connected to the X-ray diagnostic equipment, which receives and processes image signals from the X-ray image detector 4 (control elements not shown, for example). The X-ray image can then be viewed on the display of the monitoring lamp 9. The monitoring lamp 9 can be held in place by a longitudinally movable, pivotable, rotatable, and height-adjustable support system 10 mounted on the ceiling, which has a cantilever and a lowerable support arm. An estimation system 11 for estimating metallic artifacts in 3D imaging is also provided in the system control unit 7.

[0050] Figures 2 to 6 The examples involve metal objects. These metal objects are representative of all possible objects (such as bones).

[0051] It can be adopted Figure 2 The processing pipeline shown is used to calculate the current C-arm tilt δ. t The global MAA score (G-MAA) and / or spatially resolved local MAA score (L-MAA) are superimposed at the bottom. Given two or more reconnaissance views 12 of a scene, the distribution of (metal, skeleton, etc.) can be estimated as a volumetric (metal, skeleton, etc.) mask 14b. seg (x,y,z). This can be accomplished through backprojection of the metal object and subsequent segmentation, as detailed by Wu et al.13, or using an end-to-end deep learning method, as described by Rohleder et al. It can be assumed that this component of the pipeline is given.

[0052] Based on this knowledge of metal distribution, in, for example, Wu et al.'s Q... poly In step 15, following the objective function calculation, a score is derived for each trajectory (16). Projected images can be calculated for each tilted circular scan, and the average spectral shift for each simulated projection can be calculated. For example, the trajectory is scored by calculating the variance over the average spectral shift values ​​of the constitutive projected images of the trajectory. The resulting 1D objective function Q... poly (δ) is normalized relative to the worst and best possible trajectories within the evaluation angle range of δ∈[-30,30] degrees, as exemplarily shown by trajectory score 16. Details of this procedure can be found in Wu et al.

[0053] In order to calculate the spatial distribution of the expected metallic artifacts ( Figure 1 The local-MAA or L-MAA in the present invention is followed. Initially, the current tilt δ of the C-arm 2 is read. t And the path length image pδ t (u,v,θ) is calculated using the binary metal volume b seg The line integral of (x, y, z). That is, the X-ray path through the metal is simulated in step L1. A corresponding set of line integral projections 17 (also referred to herein as 2D metal length projections) is obtained.

[0054] The system matrix A θ,δt Defined as the tilt δ of a given C-arm t And the Siddon ray tracing forward projection of rotation θ (see above), which can be expressed as

[0055] p δt (u,v,θ)=A θ,δt b seg .

[0056] To better simulate the artifact bias caused by a certain projected length of the metal, the calculation in this disclosure is defined as a spectral shift of the difference between the monoenergetic and multienergetic forward models, similar to that of Wu et al. Thus, the corresponding artifact intensity value is assigned pixel-by-pixel to each metal length value (step L2) to obtain a set of metal artifact projections 18 (also referred to herein as 2D artifact projections). However, unlike the global MAA calculation, this disclosure calculates this for each pixel, rather than summing it over each projected image.

[0057] In step L3, the volumetric effect per unit volume of these projected domain spectral shift maps is calculated. For metal mask 14(b seg For each voxel defined as a metal in the image, the variance of its projected position in the previously calculated spectral shift image (i.e., metal artifact projection 18) can be calculated to obtain a 3D artifact image 19 (voxel influence). While this may seem similar to the objective function Q... poly (Trajectory score 16), but the significant difference here is that this value is normalized per unit volume and is therefore an absolute measure of the expected artifact intensity at that volume location. This results in the voxel influence map m δt (x,y,z) can be represented as

[0058] m δt (x, y, z) = var θ (S δt (u*, v*, θ))

[0059] Where u* and v* are at the tilt angle δ t The detector coordinates (x, y, z) after being projected downwards into view θ (compare) Figure 3 ).

[0060] Finally, in step L4, one or more overlay images 20 (also known as guided overlay) are calculated by, for example, the maximum projection of a voxel influence map (3D artifact image 19) along a known projection geometry onto at least one of the reconnaissance views 12. Maximum projection means that the maximum artifact value of all voxels that contribute to the pixels of the 2D projection is used for the projection.

[0061] Based on this one or more guidance overlays 20, the user 21 can interactively select (step L0) a trajectory by tilting the C-arm 2 to minimize the displayed artifact predictions. In this way, the C-arm tilt can be interactively optimized relative to the clinical task 22 (e.g., observing two specific screws in the spine). Therefore, it allows clinicians to find an optimized scan trajectory while taking into account the imaging task derived from the clinical situation and procedural context.

[0062] To standardize the unbounded and initially incomprehensible values ​​derived from the variance of the spectral shift per unit volume of voxel, as follows: Figures 4 to 6 The basic calibration method shown aims to establish a metric for the significance of artifacts and specify this metric as the upper limit in the color map presented to the physician.

[0063] like Figure 4 As shown, a wedge-shaped phantom 23, for example made of titanium (e.g., 50 × 150 × 5 mm), is positioned along the z-axis of a C-arm gantry on top of the cadaver 24 for comparison with a real patient. After acquiring a CBCT scan, the phantom is examined in slices passing through it. Figure 5 The volume shown is used for empirical identification of the “critical artifact threshold.” In the determined slice (see arrow 25), the height of the wedge phantom is measured to be 25 mm. This can be accomplished by a trained orthopedic surgeon by assessing the intensity uniformity within the metallic object and the severity of streaking artifacts around the wedge phantom. Using threshold-based segmentation of the object, steps L1-L3 of the previously described method are evaluated to produce a corresponding voxel influence map (3D artifact image) of the wedge phantom 23. The average value at the empirically determined height (location of arrow 25) is read out. This mean or average value is used to define… Figure 6 The color mapping is shown. This average value can then be used to normalize the calculated voxel influence for use in the final overlay image 20.

[0064] The exemplary calibration protocol described above was chosen because the phantom models, or simulates, the monotonically increasing variance of the spectral shift. The wedge thickness is approximately similar to the diameter of a pedicle screw (about 5 mm), while the increased height simulates the increased axial length.

[0065] Based on the aforementioned dynamically changing local MAA guidance, local metal artifact prediction can be used to provide an interactive optimization scheme. For example, in... Figure 2 As illustrated, the clinician or user (operator) 21 interprets the displayed overlay image 20 and adjusts the C-arm tilt, which in turn alters the displayed overlay image 20 (the guidance image). This interactive setup offers several advantages. Utilizing clinical and procedural context allows for tuning the tilt optimization problem and finding an optimal value specific to the imaging task. Furthermore, if practical interference with the optimal circular trajectory (such as obstacles in the operating room) is considered, this can be taken into account, and a suboptimal trajectory can be found as a compromise. Finally, the absolute localization metric allows for determining whether tilt is absolutely necessary, which is impossible with global MAA scores, as these are always normalized to best / worst cases.

Claims

1. A method for estimating artifacts in 3D imaging, the method being performed as follows: - Provides a 3D mask representing the object (14), Its features - Simulate X-rays from the X-ray source-detector pair (2, 3) passing through the object at multiple projection positions of the X-ray source-detector pair (2, 3) moving along a pre-given trajectory. - Assign artifact values ​​to each voxel of the 3D artifact image (19) according to the corresponding path length of the X-rays through the 3D mask.

2. The method according to claim 1, wherein, The allocation steps include: - Determine the corresponding 2D path length projection (17) for each projection location, wherein each 2D path length projection (17) includes a value representing the corresponding path length of one of the simulated X-rays passing through the 3D mask. - Assign the corresponding individual artifact value to each path length to obtain a set of 2D artifact projections (18) from the 2D metal length projection (17), and - The set of 2D artifact projections (18) are back-projected onto the 3D artifact image (19).

3. The method according to claim 1 or 2, wherein, The step of assigning the artifact values ​​to each voxel of the 3D artifact image (19) depends on one or more additional parameters provided to or by the X-ray system to which the X-ray source-detector pair (2, 3) belongs.

4. The method according to claim 3, wherein, At least one of the additional parameters includes metadata of the X-ray system, navigation data for navigating the object between the X-ray source-detector pairs (2, 3), or geometric data of the object.

5. The method according to any one of the preceding claims, wherein, The 3D mask (14) is obtained by reconstructing a 2D X-ray reconnaissance view (12).

6. The method according to any one of the preceding claims, wherein, The 3D mask (14) represents the object and at least one additional object, and the 3D mask (14) is obtained based on segmenting the object.

7. The method according to claim 5 or 6, wherein, The 3D artifact image (19) is projected forward onto one of the reconnaissance views (12) to obtain an overlay image (20).

8. The method according to any one of the preceding claims, wherein, The artifact intensity is color-coded in the 2D artifact projection (18) or the 3D artifact image (19).

9. The method according to claim 8, wherein, The color used for color coding is calibrated by determining a specific color for the artifact threshold of the reconstructed calibration phantom (metal wedge) (23).

10. The method according to any one of the preceding claims, wherein, The trajectory has a principal plane that is inclined in a predetermined coordinate system.

11. A method for adjusting an X-ray apparatus, the method being performed by estimating artifacts according to claim 10 and adjusting the tilt of the trajectory of the X-ray source-detector pair (2, 3) based on a 3D artifact image (19) or a forward projection of the 3D artifact image (19).

12. The method according to claim 11, wherein the adjustment is performed automatically as follows: minimizing the artifact value of the 3D artifact image (19) or the artifact value of the forward projection of the 3D artifact image (19) by changing the tilt of the X-ray source-detector pair (2, 3).

13. The method according to claim 12, wherein, Minimize the artifact value of a region of the 3D artifact image (19) or the artifact value of the forward projection of the 3D artifact image (19), and the region is specified by the task.

14. The method according to claim 11, wherein, The adjustment is performed interactively by the operator (21) of the X-ray source-detector pair (2, 3), and the 3D artifact image (19) or the forward projection of the 3D artifact image (19) is updated during the tilt change.

15. The method according to any one of the preceding claims, wherein, The object is a metallic object or at least a part of a human or animal body.

16. The method according to any one of the preceding claims, wherein, The artifact value is normalized per unit volume and is therefore an absolute measure of the expected artifact intensity at the location within that unit volume.

17. An X-ray apparatus comprising an X-ray source-detector pair (2, 3) and an estimation means capable of performing the method according to any one of the preceding claims.

18. The X-ray apparatus according to claim 17, wherein, The X-ray source-detector pair (2, 3) is fixed at the C-arm.

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