DEVICE AND METHOD FOR CALCULATING A RECORDING METRAJECTORY

Description

[0001] Exemplary embodiments of the present invention relate to a device and a corresponding method for calculating an acquisition trajectory of a CT system. A further exemplary embodiment relates to a corresponding computer program.

[0002] X-ray computed tomography (CT) requires the most complete possible dataset of radiographic images. Such a dataset is considered as complete as possible when the image data processing following the acquisition of individual radiographic images, e.g., reconstruction, can answer a question arising from a testing task with sufficient accuracy. A specific sequence of source, object, and detector positions and orientations is called a trajectory.

[0003] Classical CT systems address this task by manipulating the object or source and detector, with the aim of generating a trajectory that scans as large a portion of a circle as finely as possible.

[0004] Unusually large and / or complex objects, on the one hand, and the need for particularly fast measurements, on the other, lead to the requirement of obtaining the information necessary for the inspection task from significantly fewer radiographic images, potentially under conditions of limited accessibility to the test object. The use of modern, large-scale X-ray systems with a high number of degrees of freedom and a large working area offers the possibility of employing unconventional and novel trajectories that take into account the material properties and accessibility of the test object and utilize them as efficiently as possible. This creates the problem that a specific, optimal trajectory must be found. The number of possible trajectories is very large, especially when considering the possibility that other CT parameters, such as voltage, filtering, current, and exposure time, are also part of the trajectory. Furthermore, the position or...The relative position and orientation of the object relative to the CT system must be part of the trajectory. Therefore, an improved concept is needed. US 7441953 B2 describes a medical imaging system using robotic arms.

[0005] The object of the present invention is to create a concept that optimizes the acquisition trajectory of a CT system in such a way that the number of acquisitions is reduced and yet the data set obtained by means of the acquisitions is sufficient to fulfill the testing task.

[0006] The problem is solved by the independent patent claims.

[0007] Exemplary embodiments of the present invention provide a computing unit for calculating an acquisition trajectory of a CT system. The computing unit comprises a receiver interface, an optimizer, and a control unit. The receiver interface serves to receive measurement data, such as intermediate images or previously calculated object data, or simulation data relating to the object to be acquired, i.e., for example, an X-ray simulation (which is based, for example, on CAD data of the object to be acquired). The optimizer is configured to determine the acquisition trajectory to be followed, starting from the known degrees of freedom of the CT system (rotational CT system, helical CT system, or CT system with a large number of degrees of freedom), from the measurement and / or simulation data described above, and from a test task. The test task is derived from a group comprising a plurality of test tasks, such as...Create a volume model, create a surface model, or detect defects. Based on the determined acquisition trajectory, data is then output accordingly, which serves to control a manipulation unit (e.g., with one or more actuators / robots) of the computed tomography system. This can be either control data or, more generally, data describing the position to which the controller then moves. Further features are defined by the independent patent claims.

[0008] The invention is based on the understanding that, in X-ray imaging systems or CT systems capable of acquiring 2D, 3D, or 4D (3D + time) information about an object, the trajectory, or more generally, the positions to be traversed, can be optimized, taking into account the specific inspection task and the object being inspected, in such a way as to reduce the number of individual images and thus also the number of positions to be traversed. The rationale behind this is that each task, such as the detection of surface geometry compared to the detection of a volume model, predetermines the boundary conditions for the trajectory as well as for the acquisition parameters, such as X-ray voltage or exposure time. The object itself also has, or can have, a further influence.As a rule, the simpler the object and the simpler the inspection task (surface determination), the more the number of scans along the trajectory can be reduced. However, the angles approached in space should be sufficiently distributed along the trajectory. If the inspection task (e.g., detecting voids) and the geometry of the object are very complex, the number of angles approached in space should generally be increased to achieve a sufficiently high resolution in the volume model.

[0009] This is precisely where the improved concept comes into play. Before or during the actual image acquisition, it analyzes the basic features of the object being tested to, for example, recognize its geometry and then determines the trajectory depending on the selected acquisition mode. As a result, the optimizer defines the trajectory (i.e., the sequence of manipulator positions and orientations) that produces an image of optimal quality, or sufficient quality for the task, with as few individual images as possible. This has the advantage that optimal image quality can be achieved with a minimum number of X-ray projections and thus with a very short measurement time. This sufficiently accelerates each measurement for the given inspection task.

[0010] Depending on the specific implementation, the measurement and / or simulation data can be interim data generated during the measurement process, which is sufficiently detailed to identify the object's boundary conditions (e.g., undercuts or highly variable surfaces, such as those found in a heat sink), or a geometric or volume model derived, for example, from CAD data. In an implementation using a real-time model, either the intermediate images or the interim data output by the image processing system (e.g., a voxel model that is not yet fully reconstructed or for which the data set is not yet finalized, or intermediate images, i.e., 2D data), or even the partially (depending on the task) constructed dataset itself, can serve as input for calculating the trajectory.

[0011] Depending on the specific embodiment, the processing unit has a user interface through which the respective inspection task can be defined. For example, three basic inspection tasks can be distinguished. A first inspection task involves averaging a geometric structure and / or a geometric surface structure. For this task, the acquisition trajectory or the settings of the CT system are selected so that high-frequency features of the object being scanned are easily detectable, and in particular, large gradients, such as those originating from edges, are detectable during the scan. A second inspection task, according to another embodiment, involves detecting attenuation coefficients across the volume of the object being scanned. Here, the requirements for the acquisition and thus also for the acquisition trajectory are higher.The attenuation coefficient can be viewed as a material property that, in combination with additional information (e.g., material density), allows conclusions to be drawn about the material. Therefore, longer exposure times and a greater number of angles in space are generally required. Another testing task, according to one embodiment, involves detecting deviations, such as defects or voids, within the volume of the object being imaged. Here, it is not only necessary that the density within the volume is easily detectable, but above all, that the density and density changes are very well resolved. The imaging trajectory can then be chosen to zoom in on specific areas that indicate a local density variation. According to some embodiments, it would also be conceivable to aim for a successive improvement in such areas.Tracing a Fibonacci grid on the surface of a sphere would also be possible. Tracing a trajectory with varying magnification would also be possible.

[0012] According to the invention, the acquisition trajectory is chosen such that collisions, e.g. with the object or with the test environment, are avoided. This is possible because, based on the previously determined measurement or simulation data, the free space is now sufficiently well known after the object has been identified.

[0013] In other embodiments, the optimizer can also be configured to specify, or more precisely, vary the acquisition parameters for each position on the acquisition trajectory. For example, if the object has a different, e.g., thinner, length in one spatial direction than in another, a short exposure time may be sufficient in one case, while in the other, the exposure time for the longer length must be increased accordingly. The acquisition parameters are adjusted not only the exposure time, but also the X-ray voltage, the X-ray current, or X-ray filtration.

[0014] According to another embodiment, the correction data can also vary for each angle in space and are therefore advantageously stored in addition to the respective positions on the recording trajectory. The correction data essentially consists of the so-called bright images, from which calibration takes place. If such a bright image is not available for every position on the recording trajectory, it can be determined or simulated based on the neighboring positions.

[0015] Another embodiment relates to a CT system with an optimizer as characterized above.

[0016] Another embodiment provides a corresponding method with the steps of receiving measurement and simulation data, determining the recording trajectory and outputting the data to control the manipulation unit.

[0017] According to another embodiment, a computer program can be used to carry out the procedure.

[0018] Further developments are defined in the dependent claims. Exemplary embodiments of the present invention are explained below with reference to the accompanying drawings. These show: Fig. 1a a schematic block diagram of a computing unit with an optimizer according to a basic embodiment; Fig. 1a a schematic flowchart of the procedure for the calculation and acquisition trajectory according to the basic embodiment; Fig. 1c a schematic block diagram of an extended computing unit; Fig. 2 some combinations of test task, test object and corresponding acquisition trajectory according to an embodiment; and Fig. 3 a CT system according to a further embodiment.

[0019] Before exemplary embodiments of the present invention are explained below with reference to the accompanying drawings, it should be noted that elements and structures with the same effect are provided with the same reference numerals, so that their descriptions are applicable to each other or interchangeable.

[0020] Fig. 1 Figure 10 shows a calculation unit for calculating an acquisition trajectory of a CT system. The calculation unit comprises the perimeter interface 12, the optimizer 20, and a control unit 14. Furthermore, in this embodiment, the calculation unit has an interface 16 for receiving a current acquisition status (e.g., position / orientation of the object).

[0021] Measurement and / or simulation data relating to the object to be recorded are obtained via interface 12. In this case, the simulation data comes from an X-ray simulation of the object 30, whereby the X-ray simulation can, for example, be based on CAD data for the object 30. In the procedure 100 carried out, as described in Fig. 1b As shown, this reception of the measurement and / or simulation data corresponds to step 110. This data is then analyzed by the optimizer and processed in combination with the corresponding case (see reference numbers F1 to F3), which is obtained via user interface 16, in such a way that the acquisition trajectory 32 is determined. This step corresponds to step 120 in procedure 100.

[0022] For example, in the case of the cube-shaped object 30 shown here and in the exemplary test task F1 "determining a surface structure", the number and positions on the recording trajectory 32 can be reduced in such a way that, for example, only four or slightly more recordings need to be made along the side faces of the square cube 30 in order to determine good gradient images that indicate the edges.

[0023] These corresponding positions on the acquisition trajectory, or more generally the calculated acquisition trajectory 32, are then output via the control unit 14, either a simple interface for outputting the corresponding acquisition trajectory data or a control interface. This corresponds to step 130 in relation to procedure 100.

[0024] However, according to the alternative or additive test task F3 "Detection of defects", the recording trajectory 32' could also be determined in such a way that a large number of positions along the recording trajectory 32' are recorded in order to be able to determine a sufficiently detailed voxel model of the cube-shaped object 30 in which irregularities in the density distribution can be detected.

[0025] The alternative or additive test task F2 "determining a volume data set" (with the aim of material recognition) specified according to a further embodiment would not require such a detailed data basis as, for example, test task F3, but would still require more detail than test task F1, starting from the cube-shaped test specimen 30.

[0026] It should be noted at this point that the influence of the geometry or, more generally, of the test specimen 30 is considered with regard to Fig. 2 will be explained in detail.

[0027] As a result, the device 10 makes it possible to reduce or eliminate the shortcomings prevalent in the prior art with regard to determining the acquisition trajectory, in order to achieve a good test result or the required image quality. Here, "image" is understood to mean a set (or part thereof) of spatially resolved information that exists about the test object (CT volume, defect list, edge image, surface mesh, etc.).

[0028] Referring to Fig. 1c Another embodiment of a device 10' with an optimizer 20' is explained.

[0029] Although strictly speaking not part of the computation device 10', in the present embodiment we assume that a measurement and simulation unit 40' and an image processing unit 42' are provided, which are typically part of the CT system. The measurement and simulation unit 40' receives the acquisition trajectory 32* and controls the CT system based on this. The block 42, which is downstream of the measurement and simulation, receives the corresponding intermediate images 37* from the measurement and simulation for reconstruction. From this, either a raw data set (voxel model, see reference numeral 37*) or an optimized image 38* can be output. It should be noted again that the optimized image can be understood to include not only the surface geometry and the volume model, but also a list of defect locations or other information.

[0030] It should be noted that the optimized image 38* is output in particular to the output interface of the CT system, but can also be output to the optimizer 20', which calculates its trajectory 32* from this.

[0031] In the following discussion, it is assumed that the trajectory 32*1 belongs to test task F1, while the trajectory 32*2 belongs to test task F2 and the trajectory 32*3 belongs to test task F3.

[0032] The optimizer '20' may, under certain circumstances, refer to a predefined or measurement-generated, optimal or non-optimal model 30' (prior knowledge, CAD, surface model) of the test object and / or the setup and manipulators of the test system in order to optimize the acquisition trajectory 32* with respect to information such as object geometry, accessibility, material and structure.

[0033] Optimizer 20' can generate different trajectories 32*1, 32*2, 32*3 depending on the type of image quality to be achieved. These types of image quality can include, for example: Test task F1: An optimal image 38* with regard to the description of the structure and surfaces of the test object (high-frequency features, gradients in the image; application, for example, in metrology). Test task F2: A physically correct representation of the object in the form of material properties (attenuation coefficients, material, and density) that can be determined as accurately as possible across the entire measurement volume with a given level of detail (resolution and contrast). Test task F3: A sufficiently reliable representation of unknown or expected deviations in the object from the optimal or non-optimal model. Depending on the problem, the optimizer can directly determine the optimal trajectory or successively modify it during the measurement until optimality is achieved.

[0034] Trajectory 32*1 - 32*3 refers here to the sequence of positions and orientations of one or more X-ray tubes, one or more X-ray detectors, and one or more objects via a common or multiple independent positioning systems. The trajectory can include the parameters of the X-ray source and the detector (voltage, filtering, current, exposure time).

[0035] Optimizer 20' optimizes and defines the trajectory 32*1 - 32*3 with respect to the optimality criterion used (1), 2), or 3), iteratively if necessary. For this purpose, the optimizer uses one or more pieces of information: the optimal or non-optimal model 30', the result or intermediate result of the iterative optimization ("optimized image 38*"), the result or intermediate result of a single X-ray image (see 37*), and the result or intermediate result of image processing (see 39*). The result or intermediate result of an X-ray image may also have been generated by an X-ray simulation rather than by measurement. The relationships are described in Fig. 1c clarifies.

[0036] Although the above example only ever referred to test tasks F1 to F3, it should be noted here that there may also be further test tasks or variations of test tasks F1 to F3.

[0037] Since the object being examined also plays a role in addition to the test tasks F1 to F3, reference is made below to the table from Fig. 2 explains the extent to which the test object influences the recording trajectory.

[0038] The table from Fig. 2 The diagram shows a total of four columns with four test objects and four corresponding test tasks. In the first column, a triangular test object is to be identified according to test task F1 "Surface structure recognition". In columns 2 and 3, a rectangular object is to be examined once with test task F1 "Surface structure recognition" (column 2) and once with test task F3 "Defect detection" (column 3). In the fourth column, a flat element, such as a disk, is to be examined based on test task F2 "Solid model recognition". In column five, a body with undercuts, e.g., a heat sink, is to be scanned (F1).

[0039] In the case of column 1, it becomes clear that it is most advantageous to examine at least three angles in space, namely those parallel to the legs of the triangular object. The corresponding X-ray parameters (X-ray voltage, etc.) must also be selected so that the edges and the resulting gradients at the edges are easily detectable.

[0040] For the test task from the second column, it should essentially suffice if the trajectory is chosen such that angles in space offset by approximately 90° or 92° (generally angles in the range between 85° and 100°, preferably greater than 90°) are approached to enable surface detection. For added security, the two additional angles in space shown with dashed lines can also be approached to allow for verification.

[0041] Assuming that intermediate images are taken into account when determining the trajectory, one could imagine a case where the first transmission occurs at an oblique angle (see dotted line), so that variations (starting from this first transmission angle, e.g., by 90° each time) would lead to a measurement error. However, no sharp gradient is discernible in this oblique transmission, so that for the second angle in space, a corresponding angle is sought that offers a sufficiently high gradient, thus arriving at the two or four optimal angles in space.

[0042] The embodiment shown in the third column involves the same object as in the second column, but for a different testing task. Here, it is advisable to adjust the radiographic parameters to achieve good spatial resolution in the attenuation images, which allow conclusions to be drawn about the material density. This can be accomplished, for example, by taking a large number of parallel images and a further large number of parallel images offset by 90°, or by irradiating the cube from a large number of different angles around its 360° axis.

[0043] The example shown in column 4 involves a surface where the density is to be determined across the entire object volume. The radiation path parallel to the surface alone makes it clear that a very large volume needs to be irradiated, which, to achieve sufficient accuracy, requires considerably long exposure times. Conversely, with transverse radiation (through the thin plane), it is not necessary to use such long exposure times, so the exposure time can be reduced accordingly. Otherwise, however, it may be necessary to take several parallel images, as the beam path between the radiation source and the X-ray detector may not allow the entire surface to be irradiated. This is indicated by the additional dotted arrow in the row "possible angles in space".When determining the angles to be approached in space, the orientation of the object in space also plays a crucial role, since, for example, the heat sink is easy to scan from the side (i.e., perpendicular to the cooling fins), but difficult to scan from above or below.

[0044] In the case of column 1, essentially the same angles in space are approached as in column 2; however, due to the undercuts or recesses, additional parallel (offset) measurements are taken from the surface. For "true" undercuts that cannot be illuminated from any side, the sampling rate would need to be increased so that these undercuts would be recognizable in a volume model (see example from column 3).

[0045] Further possible variations for trajectories or influencing parameters on the trajectory are explained below.

[0046] According to one embodiment, unusual trajectories, e.g., when zooming in as in the example from column 3, can also be used. The claimed classes of X-ray imaging trajectories include, for example, successively improving imaging trajectories (e.g., Fibonacci grids on the sphere's surface), scans of constant magnification with trajectories on spherical surfaces, trajectories in which the X-ray source, detector, and object do not lie on a straight line or the X-ray source is not on the normal of the X-ray detector at its image center, and in particular, trajectories for scanning the object with variable magnification, that is, with variable distances between the X-ray source and detector and the object. The variable distance serves to achieve a variable magnification M of a section of the reproduced object by utilizing the following principles: M = QDD / QOD , where M is the geometric magnification, QDD is the source-detector distance, and QOD is the source-object distance.

[0047] According to a further embodiment, the trajectories can be optimized with respect to different boundary conditions of the actually implemented recording or manipulation system. According to the invention, this includes avoiding collisions, in particular with the object, with the manipulation system itself, with the room, or with persons in the workspace. It can also include taking into account reachable, unreachable, or partially reachable positions in the workspace of the manipulation system, as well as different levels of precision achievable by the manipulation system at different locations in the workspace, at different traverse speeds, different directions of movement, or with different changes in the direction or speed of movement. This means, in particular, that trajectories are generated which minimize the total time required for a measurement.

[0048] According to another embodiment, the exposure time, current, voltage, and filtering parameters can also be optimized in parallel with the trajectories as part of a trajectory optimization process. This allows the tuple of these parameters to be optimally determined for the entire measurement or for each individual X-ray image. According to yet another embodiment, it would also be conceivable to determine the bright images for an optimized trajectory without having to measure the bright images during a trajectory run without the trajectory.

[0049] Bright images are detector images necessary for correcting inhomogeneous detector properties and illumination, produced without any object in the beam path. If the relative position and orientation of the X-ray source to the detector, or the X-ray parameters such as exposure time, current, voltage, and filtering, are not constant over a measurement, then these correction images are also not constant and can only be determined by measurement with considerable effort. The variable part of these bright images is either calculated from the previously recorded radiation field of the X-ray tube or estimated by means of X-ray simulation of the recording geometry and parameters.

[0050] The following refers to Fig. 3 a computed tomography system 80 based on robots 72 and 74 is explained.

[0051] Fig. 3a shows a possible computed tomography system 80 in which the X-ray source 70s is arranged on a first robot 72, while the radiation detector 70d is provided on a second robot 74. These two robots 72 and 74 allow the X-ray source 70s and the radiation detector 70d to be arbitrarily varied in space. For example, the two elements 70s and 70d can be moved along the trajectory 76, here a circular path, so that the corresponding images are displayed along this circular path.

[0052] Angles in space can be traversed. The radius of the circular path can be reduced, as shown here by the dashed line 76', to achieve, for example, a higher spatial resolution. It should be noted that the different positions do not have to lie on one and the same circular path, i.e., not in a common plane, but can be arbitrarily distributed in space around the object (which is precisely what the CT system 80 with its two robots 72 and 74 advantageously allows). In other words, the trajectories are defined by any positions that can be approached by the robots 72 and 74.

[0053] According to another embodiment, the computed tomography system 80 is created with the computing unit 10", the robots 72 and 74 as well as the modules of the X-ray unit 70s and 70d.

[0054] According to another embodiment, the object to be examined can be held by another robot, whose freedom of movement can be used to realize a trajectory. Therefore, it is also conceivable that the CT system comprises more than two robots.

[0055] The above-described examples of implementation make it possible to achieve superior image quality compared to conventional computed tomography by means of unconventional, optimized trajectories with optionally variable recording parameters.

[0056] Further embodiments enable automatic path planning for complex recording systems, so that in addition to optimal image quality, limitations imposed by the recording system such as accessibility, precision or speed are also taken into account.

[0057] The method according to the above embodiments is particularly relevant for the computed tomography of large and / or complexly shaped components, for example from automotive engineering, aerospace and aviation, using new imaging systems with a high number of degrees of freedom in a large working space, such as robot-based computed tomography.

[0058] The method can help accelerate fast, production-integrated inline computed tomography, thereby increasing the cycle time and efficiency of the inspection.

[0059] Similarly, this method can generally contribute to improved image quality in computed tomography, leading to more reliable visualization of finer structures. Transferring this method to medical applications is possible, potentially contributing to improved image quality and reduced radiation exposure for the patient.

[0060] Although some aspects have been described in connection with a device, it is understood that these aspects also constitute a description of the corresponding process, such that a block or component of a device can also be understood as a corresponding process step or as a feature of a process step. Similarly, aspects described in connection with or as a process step also constitute a description of a corresponding block, detail, or feature of a corresponding device. Some or all of the process steps can be performed by (or using) a hardware apparatus, such as a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, some or more of the key process steps can be performed by such an apparatus.

[0061] Depending on specific implementation requirements, embodiments of the invention can be implemented in hardware or in software. The implementation can be carried out using a digital storage medium, for example, a floppy disk, DVD, Blu-ray disc, CD, ROM, PROM, EPROM, EEPROM, FLASH memory, hard disk, or other magnetic or optical storage medium, on which electronically readable control signals are stored. These control signals can interact with, or interact with, a programmable computer system in such a way as to execute the respective method. Therefore, the digital storage medium can be computer-readable.

[0062] Some embodiments according to the invention therefore include a data carrier which has electronically readable control signals which are able to interact with a programmable computer system in such a way that one of the methods described herein is carried out.

[0063] In general, embodiments of the present invention can be implemented as a computer program product with a program code, wherein the program code is effective in carrying out one of the methods when the computer program product runs on a computer.

[0064] The program code can also be stored on a machine-readable medium, for example.

[0065] Other embodiments include the computer program for carrying out one of the methods described herein, wherein the computer program is stored on a machine-readable medium.

[0066] In other words, an embodiment of the method according to the invention is thus a computer program that includes program code for carrying out one of the methods described herein when the computer program runs on a computer.

[0067] Another embodiment of the methods according to the invention is therefore a data carrier (or a digital storage medium or a computer-readable medium) on which the computer program for carrying out one of the methods described herein is recorded.

[0068] Another embodiment of the method according to the invention is thus a data stream or a sequence of signals that represents the computer program for carrying out one of the methods described herein. The data stream or sequence of signals can be configured, for example, to be transferred via a data communication connection, such as the Internet.

[0069] Another embodiment comprises a processing device, for example a computer or a programmable logic device, which is configured or adapted to perform one of the methods described herein.

[0070] Another embodiment comprises a computer on which the computer program for performing one of the procedures described herein is installed.

[0071] Another embodiment of the invention comprises a device or system designed to transmit a computer program for carrying out at least one of the methods described herein to a receiver. The transmission can be, for example, electronic or optical. The receiver can be, for example, a computer, a mobile device, a storage device, or a similar device. The device or system can, for example, include a file server for transmitting the computer program to the receiver.

[0072] In some embodiments, a programmable logic device (for example, a field-programmable gate array, an FPGA) can be used to perform some or all of the functionalities of the methods described herein. In some embodiments, a field-programmable gate array can interact with a microprocessor to perform one of the methods described herein. Generally, in some embodiments, the methods are performed by any hardware device. This can be general-purpose hardware such as a computer processor (CPU) or method-specific hardware such as an ASIC.

[0073] The embodiments described above merely illustrate the principles of the present invention. It is understood that modifications and variations of the arrangements and details described herein will be obvious to other people skilled in the art. Therefore, it is intended that the invention be limited only by the scope of protection set forth in the following claims and not by the specific details presented herein by way of description and explanation of the embodiments.

Claims

1. Calculating unit (10, 10', 10") for calculating a recording trajectory of a CT system based on two robots (72, 74), wherein an X-ray radiation source (70s) is arranged on a first robot (72), whereas the radiation detector (70d) is arranged on a second robot (74), comprising: a receive interface (12) for receiving measurement and / or simulation data relative to the object to be recorded; an optimizer (20, 20') configured to determine the recording trajectory based on known degrees of freedom of the CT system with its robots (72, 74), based on the measurement and / or simulation data and based on a test task from a group comprising a plurality of test tasks of determining the recording trajectory; and a control unit (14) configured to output data in correspondence with the calculated recording trajectory for controlling a manipulation unit of the CT system and thereby control the CT system in correspondence with the calculated recording trajectory; wherein the positions of the recording trajectory are selected such that a collision between the recording unit and the surroundings of the object to be recorded is prevented, wherein based on the measurement or simulation data, the free space, in particular the free space in the surroundings, is known.

2. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein the measurement and / or simulation data comprise an optimum model generated during a measurement or a non-optimum model generated during a measurement, a geometry model and / or a volume model.

3. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein the measurement and / or simulation data are taken when recording the object to be recorded using the CT system.

4. Calculating unit (10, 10', 10") in accordance with claim 3, wherein the measurement and / or simulation data comprise intermediate images and / or interim data output by image processing (42").

5. Calculating unit (10, 10', 10") in accordance with claim 3, wherein the measurement and / or simulation data comprise a data object calculated by the image processing (42") in correspondence with the test task.

6. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein the calculating unit comprises a user interface (16) using which the test task is selected.

7. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein the group comprising a plurality of test tasks includes the test task of establishing a geometrical structure and / or geometrical surface structure.

8. Calculating unit (10, 10', 10") in accordance with claim 7, wherein the positions on the recording trajectory are selected such that high-frequency features of the object to be recorded and / or gradients are detectable when recording the object to be recorded.

9. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein the group of test tasks comprises detecting characteristics of the materials using the volume of the object to be recorded.

10. Calculating unit (10, 10', 10") in accordance with claim 9, wherein the positions of the recording trajectories are selected such that attenuation coefficients, the density and / or the type of the materials in the volume of the object to be recorded are detectable in the recordings.

11. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein the group of test tasks includes establishing deviations in the volume of the object to be recorded.

12. Calculating unit (10, 10', 10") in accordance with claim 11, wherein the positions on the recording trajectory are selected such that local variations in attenuation coefficients are detectable in the recordings.

13. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein every further position on the recording trajectory is selected such that a successive improvement of the im-age to be recorded is achieved; and / or wherein the positions on the recording trajectory are selected with a variable magnification and / or decreasing distance between the object to be recorded and the image detector.

14. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein the optimizer (20, 20') is configured to select the positions on the recording trajectory such that a collision between the recording unit and the object to be recorded is prevented.

15. Calculating unit (10, 10', 10") in accordance with any one of the preceding claims, wherein the optimizer (20, 20') establishes correction data from a set of stored correction data for each position on the recording trajectory or calculates corrected correction data based on the stored correction data for neighboring positions; and / or wherein the optimizer (20, 20') establishes correction data from a memory for the recording parameters selected or calculates corrected correction data based on the correction data stored for similar parameters.

16. Computer tomography system (80) comprising a calculating unit (10, 10', 10") in accordance with any one of the preceding claims.

17. Method (100) for calculating a recording trajectory of a computer tomography system based on two robots (72, 74), wherein an X-ray radiation source (70s) is arranged on a first robot (72), whereas the radiation detector (70d) is arranged on a second robot (74), comprising: receiving (110) measurement and / or simulation data relative to the object to be recorded; determining (120) the recording trajectory based on known degrees of freedom of the CT system, based on the measurement and / or simulation data and based on a test task from a group comprising a plurality of test tasks, and outputting (130) data in correspondence with the calculated recording trajectory for controlling a manipulation unit of the computer tomography system in order to control the CT system in correspondence with the calculated recording trajectory; wherein the positions of the recording trajectory are selected such that a collision between the recording unit and the surroundings of the object to be recorded is prevented, wherein based on the measurement or simulation data, the free space, in particular the free space in the surroundings, is known.

18. Computer program including a program code, wherein the program code is operable to perform the method in accordance with claim 17 when the computer program runs on a computer for a CT system.