Estimation of radiation exposure signature for imaging system with radiation source

Abstract

In order to estimate a radiation exposure characteristic quantity for an imaging system (1) having a radiation source (4), a basic model is obtained which determines the radiation exposure characteristic quantity from a set of input quantities comprising at least one radiation source operating parameter. Measured measurement data is obtained for the imaging system (1), said measurement data comprising a radiation exposure characteristic quantity for a plurality of different values of at least one radiation source operating parameter. And adjusting the basic model according to the measurement data to generate a refined model. Determining a current value for the set of input variables for the imaging system (1); and determining, by a refinement model, an estimated value for the radiation exposure feature quantity from a current value for the set of input quantities.

Description

Technical Field

[0001] The invention relates to a method for estimating a characteristic quantity of radiation exposure for an imaging system with a radiation source, a corresponding data processing device, an imaging system with a radiation source for generating ionizing radiation and such a data processing device, and a corresponding computer program product. Background Art

[0002] In imaging systems with radiation sources for generating ionizing radiation, it is desirable, for example for regulatory reasons, to record the radiation exposure of patients and medical personnel as accurately as possible. For this purpose, corresponding radiation exposure characteristics can be measured during operation of the imaging system.

[0003] In X-ray based imaging systems, such as X-ray based angiography systems, the dose area product (DAP) can be measured as a characteristic quantity of radiation exposure in the radiation path of the imaging system, for example, by a measuring device called a DAP chamber. In addition to the above-mentioned regulatory requirements, the measured DAP can also be used to control the radiation source.

[0004] Such a DAP cavity or a corresponding measuring device for measuring a characteristic quantity of radiation exposure leads to increased scattering of ionizing radiation because it is arranged in the radiation path. This can increase the radiation exposure of medical personnel in the imaging system environment on the one hand, and can also reduce the image quality due to additional out-of-focus radiation on the other hand.

[0005] In the document "Technical Note: SpekPy v2.0—a software toolkit for modeling x-ray tube spectra" Med. Phys. 48(7), 3630, G. Poludniowski et al. describe a software for modeling X-ray tube spectra and compare the modeled predictions with experimentally determined spectra.

[0006] Document CN106291650A describes a method for determining radiotherapy dose based on Monte Carlo simulation. For this purpose, a virtual source model is used and modified based on the results of a water model experiment. The modified virtual source model is used to calculate the radiation dose in the tumor target area.

[0007] In addition, various possible approaches are known to adjust or generate a statistical patient model based on patient-specific information (e.g., metadata such as body size, age, etc.) and / or image data (e.g., pre-interventional CT reconstruction, etc.) in order to generate a patient-specific body model for a single patient. Such a body model may include skin, bones, blood vessels, and / or other internal organs, depending on the complexity.

[0008] In the paper "PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation" (arXiv: 2211.11734v2) by K. Shetty et al., a method for reconstructing a 3D mesh of the human body from a single 2D image is introduced. The method is based on a linearized representation of the parametric SMPL model (skinned multi-person linear model). In the paper "BOSS: Bones, Organs and Skin Shape Model" (arXiv: 2303.04923v1) by K. Shetty et al., a deformable human shape and posture model is introduced, whereby the skin, internal organs and bones can be combined and learned from CT images. The overall representation of the human body is achieved by modeling the statistical variations in the position normalized space using probabilistic principal component analysis and preserving the joint kinematics. Summary of the invention

[0009] The technical problem to be solved by the present invention is to reduce scattered radiation during the operation of an imaging system with a radiation source.

[0010] This technical problem is solved by the content of the independent claim. Other designs and advantageous improvements are the content of the dependent claims.

[0011] The idea of ​​the present invention is to replace the measuring device for measuring the characteristic quantity of radiation exposure in normal operation of the imaging system by a model-based estimation of the characteristic quantity of radiation exposure. Based on the basic model for determining the characteristic quantity of radiation exposure from the input quantity set, a refined model is generated from the values ​​of the characteristic quantity of radiation exposure measured for a specific imaging system. The characteristic quantity of radiation exposure in normal operation is estimated using the refined model.

[0012] According to one aspect of the present invention, a method for estimating radiation exposure characteristic quantities of an imaging system with a radiation source, in particular a radiation source for generating ionizing radiation, is provided. Here, a basic model for determining the radiation exposure characteristic quantity based on an input quantity set is obtained. The input quantity set includes at least one radiation source operating parameter. Measurement data measured for the imaging system is obtained. The measurement data includes radiation exposure characteristic quantities for a plurality of different values ​​of the at least one radiation source operating parameter. A refined model is generated, in particular a refined model for determining the radiation exposure characteristic quantity based on the input quantity set, by adjusting the basic model based on the measurement data. A current value for the input quantity set for the imaging system is determined. An estimated value for the radiation exposure characteristic quantity is determined based on the current value for the input quantity set by the refined model.

[0013] The actual carrying out of the measurements for determining the measurement data is not necessarily part of the method according to the invention. The measurement data are stored, for example, on a data carrier within the scope of the method according to the invention. The method according to the invention can therefore be designed as a purely computer-implemented method. Unless otherwise specified, all steps of the computer-implemented method can be performed by a data processing device having at least one computing unit. In particular, at least one computing unit is configured or adjusted to perform the steps of the computer-implemented method. For this purpose, at least one computing unit can, for example, store one or more computer programs, the execution of which enables at least one computing unit to perform the computer-implemented method.

[0014] In a further embodiment of the method according to the invention, it comprises determining measurement data, i.e. measuring a characteristic quantity of radiation exposure to the imaging system for a plurality of different values ​​of at least one operating parameter of the radiation source. Accordingly, each purely computer-implemented embodiment of the method according to the invention directly leads to a corresponding method for estimating a characteristic quantity of radiation exposure that is not purely computer-implemented, and vice versa.

[0015] In both cases, however, the measurement data are values ​​for characteristic quantities of radiation exposure measured for a specific, unique imaging system or its radiation source. A refined model can therefore be understood as a case-specific model for a specific, unique imaging system. This is usually not the case for a basic model. The basic model can, for example, model an imaging system of a certain type or with a certain type of radiation source. The basic model can also model a certain model of an imaging system or a certain production series. For example, a basic model can be created during a research and development phase, in particular for the development of imaging systems. A refined model can, for example, be generated during or at the end of production of a specific imaging system.

[0016] The basic model can obtain the corresponding values ​​of an input quantity set as input data and calculate the radiation exposure characteristic quantity based on or according to this. The basic model can be understood as a function or mapping of the input quantity set to the radiation exposure characteristic quantity. This is also similarly applicable to the refined model.

[0017] The radiation exposure characteristic quantity is the value of the radiation exposure to which an object or a person is exposed outside the radiation source, especially in a defined position or a defined area. The radiation exposure characteristic quantity especially relates to the dose or energy of ionizing radiation within a defined measurement range outside the radiation source. The radiation exposure characteristic quantity can be equivalent to, for example, energy per unit area and / or per unit mass, etc. The radiation exposure characteristic quantity can be equivalent to, for example, kerma or dose area product (DAP).

[0018] In order to generate the measurement data, the value of at least one radiation source operating parameter is changed and the radiation exposure characteristic quantity is measured accordingly. In addition to the change of at least one radiation source operating parameter, other parameters or quantities of the input quantity set, in particular all quantities of the input quantity set, can also be changed in order to respectively measure the radiation exposure characteristic quantity accordingly.

[0019] The at least one radiation source operating parameter corresponds in particular to at least one parameter which is set or provided for the radiation source in order to generate ionizing radiation.

[0020] If the radiation source is, for example, an X-ray radiation source, i.e., in particular an X-ray tube, the at least one radiation source operating parameter may in particular include a kilovolt peak value kVp (English: peak kilovoltage), i.e., a maximum tube voltage applied to the X-ray tube when generating X-rays as ionizing radiation. The at least one radiation source operating parameter may also include, for example, a tube current and / or a focal spot size (English: focal spot size) of the X-ray tube.

[0021] Generating a refined model by adjusting the basic model according to the measured data can also be understood, for example, to generate a refined model based on the measured data taking into account the basic model, in particular taking into account the basic model as a starting point or initial state. The basic model can be determined, for example, by a large number of model parameters and the adjustment of the basic model can be carried out according to the measured data, so that the model parameters are adjusted in such a way that the refined model can correctly reproduce the measured data or reduce the error when reproducing the measured data compared to the basic model. If the basic model contains a neural network, the model parameters can also be weighting factors or bias factors of the neural network. However, it is not necessary that the basic model is based on a neural network or other machine-trainable algorithms. The model parameters can correspondingly be polynomial coefficients of a polynomial model or other curve parameters of the above-mentioned function or mapping.

[0022] By using the refined model, hardware for determining radiation exposure characteristics in the radiation path, such as a DAP chamber, can be substantially omitted during normal operation of the imaging system, such as in scientific or clinical operation, thereby reducing the amount of ionizing radiation to be controlled.

[0023] As mentioned at the outset, the radiation exposure characteristic quantity can be used to record the radiation exposure of patients or medical personnel. However, the radiation exposure characteristic quantity can also be used, for example, to control a radiation source or for other purposes.

[0024] Since the refined model is generated based on the measurement data for the specific imaging system, the estimation of the characteristic quantity of radiation exposure can also be improved, in particular relative to an estimation based only on a physical model of an ideal radiation source or an idealized imaging system. Compared to a high-precision physical simulation for calculating the characteristic quantity of radiation exposure, for example using a Monte Carlo method, computing resources, in particular computing time, can be saved to a considerable extent by the method according to the invention.

[0025] According to at least one embodiment, after the measurement of the measurement data and after the expiration of the update time period, additional measured data for the imaging system are obtained, for example measured. The additional measured data include radiation exposure characteristic quantities for additional multiple different values ​​of at least one radiation source operating parameter. An updated model is generated by adjusting the refined model according to the additional measured data. Additional values ​​of the input quantity set for the imaging system are determined and additional estimated values ​​for the radiation exposure characteristic quantity are determined by the updated model according to the additional values ​​of the input quantity set.

[0026] Whether and when an update time period expires may generally be determined based on predetermined time intervals and / or based on surveillance measurements of the imaging system.

[0027] The interpretation of the measurement data can be transferred similarly to the further measurement data. The updated model can be generated based on the refinement model in a similar manner to the previous generation of the refinement model based on the basic model. The corresponding interpretation can also be transferred similarly.

[0028] The radiation exposure characteristic quantity depends on the value of the input quantity set and in particular the value of at least one radiation source operating parameter. However, due to aging effects, in particular of the radiation source, for example, aging effects of the anode in the case of an X-ray tube and / or other components of the imaging system, for example, components for filtering ionizing radiation and / or beam shaping, etc., this correlation may change. By generating an updated model in a corresponding embodiment of the method according to the invention, deviations and inaccuracies of the refined model, which may occur over time due to the above-mentioned aging effects, can thus be at least partially compensated.

[0029] According to at least one embodiment, measured reference measurement data is obtained for at least one reference imaging system. The reference measurement data includes radiation exposure characteristic quantities for a plurality of different values ​​of at least one radiation source operating parameter. A basic model is generated based on the reference measurement data.

[0030] The interpretation of the measurement data may similarly be transferred to the reference measurement data, wherein the reference measurement data may come from measurements for a plurality of reference imaging systems and / or may be repeated at different points in time, for example in order to take into account aging effects and / or temperature influences.

[0031] The reference imaging system may be of the same type or model as the imaging system or of a similar or comparable model. For example, the type of radiation source, such as an X-ray source, used for the reference imaging system and the imaging system may be the same. However, a single reference imaging system is not necessarily the same as the imaging system. The base model is a more general model than a refined model specifically tuned for a unique imaging system. Therefore, the base model can be used for different imaging systems to generate corresponding different case-specific refined models as described above.

[0032] According to at least one embodiment, a physical initial model for determining a radiation exposure characteristic quantity based on an input quantity set is obtained, and the physical initial model is adjusted based on reference measurement data to generate a basic model.

[0033] The physical initial model is in particular a model generated according to physical relationships, in particular physical relationships when ionizing radiation is generated by a radiation source. The physical initial model may also contain approximations. In particular, the physical initial model is not an algorithm trained completely or substantially by machine learning.

[0034] For example, the physical initial model can be defined or determined by a model parameter set. Then, in particular, the basic model is generated by adjusting the model parameter set according to reference measurement data. Similarly, the model parameter set can also be readjusted according to the measurement data to generate a refined model.

[0035] According to at least one embodiment, the basic model is generated by an algorithm that is trainable by machine learning based on reference measurement data. In particular, the basic model is an algorithm that is trained by machine learning.

[0036] For example, the trainable algorithm may include one or more artificial neural networks and / or one or more support vector machines (SVMs).

[0037] The training of the trainable algorithm based on the reference measurement data can be performed according to known training methods, for example methods for neural networks or SVMs.

[0038] In this embodiment, a refined model can also be generated by retraining the algorithm trained based on the reference measurement data or further training it based on the measurement data. However, a basic model can also be given by a trained algorithm, and then a refined model can be generated based on the physical model, or vice versa.

[0039] Since trainable algorithms, in particular artificial neural networks, can be understood as universal approximations to functions, a basic model can be generated with high accuracy in these embodiments regardless of whether the physical conditions that generate ionizing radiation, in particular the influence of aging effects etc., are known.

[0040] According to at least one embodiment, the input quantity set comprises at least one state and / or material parameter for a radiation filter device of the imaging system. The measurement data comprises radiation exposure characteristic quantities for a plurality of different values ​​of the at least one state and / or material parameter.

[0041] In various embodiments, this applies analogously to the reference measurement data and / or the further measurement data.

[0042] For example, the radiation filtering device may involve one or more filters which are used to introduce the radiation path, in particular between the exit area of ​​the ionizing radiation from the radiation source and the object to be examined or imaged. In the case of an X-ray-based imaging system and an X-ray tube as a radiation source, the at least one filter may, for example, include one or more homogeneous metal filters for beam hardening, i.e. filtering out low-energy components, and / or one or more wedge filters, etc.

[0043] The at least one state and / or material parameter for the radiation filter device may relate, for example, to the filter layer thickness, the material of the respective filter, the position of the filter (particularly in the case of wedge-shaped filters), or the like.

[0044] The detailed model can therefore also accurately take into account aging effects of the radiation filter, for example due to material changes in the filter material caused by X-rays or the like.

[0045] According to at least one embodiment, the set of input quantities comprises at least one state and / or material parameter for a beam shaping device of the imaging system. The measurement data comprises radiation exposure characteristic quantities for a plurality of different values ​​of the at least one state and / or material parameter.

[0046] In various embodiments, this applies analogously to the reference measurement data and / or the further measurement data.

[0047] The beam shaping device can be, for example, one or more collimators for introducing a radiation path, in particular between the radiation filter device and the object.

[0048] For example, the at least one state and / or material parameter for the beam shaping device may be the thickness or geometry of the collimator, the material of the respective collimator, the size of the opening of the collimator or the like.

[0049] The refined model can therefore also accurately take into account aging effects of the beam shaping device, for example due to material changes of the collimator caused by X-rays or the like.

[0050] According to at least one embodiment, the set of input quantities comprises an ambient temperature of the radiation source, and the measurement data comprises radiation exposure characteristic quantities for a plurality of different values ​​of the ambient temperature.

[0051] Temperature-dependent effects that can influence the radiation exposure characteristic variable can thereby also be taken into account. The ambient temperature can correspond in particular to the air temperature in the room in which the radiation source is located or the air temperature in a housing in which the radiation source is located.

[0052] According to at least one embodiment, the input quantity set comprises the temperature of at least one component of the imaging system, for example a component located in the radiation source environment, and the measurement data comprises radiation exposure characteristic quantities for a plurality of different values ​​of the temperature of the at least one component.

[0053] Temperature-dependent effects which can influence the radiation exposure characteristic variable can thereby also be taken into account. In particular, at least one component can influence the value of the radiation exposure characteristic variable by heating up.

[0054] According to at least one embodiment, the set of input quantities comprises at least one exposure parameter and the measurement data comprises radiation exposure characteristic quantities for a plurality of different values ​​of the at least one exposure parameter.

[0055] This applies analogously, if appropriate, to the reference measured data and / or the further measured data.

[0056] Exposure parameters are referred to here and below as such parameters, although ionizing radiation is not necessarily light in the true sense, but can also be, for example, X-rays. However, the understanding of exposure can also be similarly transferred to other electromagnetic radiation, such as X-rays. At least one exposure parameter can, for example, include exposure duration or aperture during exposure or pulse width of ionizing radiation pulses during exposure.

[0057] In this way, a detailed model can be generated exactly for different exposure settings.

[0058] According to at least one embodiment, the radiation exposure characteristic quantity is a dose area product or a kerma or a quantity that depends on the dose area product and / or the kerma.

[0059] For example, the radiation source in this example is an X-ray tube, but similar quantities can be obtained for other types of ionizing radiation.

[0060] According to at least one embodiment, image data generated by an imaging system are obtained, which represent an object to be imaged, in particular a patient's body. In addition, a volume model of the object, in particular a volume model of the patient's body, is obtained. A consistency check is performed based on the image data, the volume model, the values ​​for the input quantity set used to generate the image data, and the refined model. Based on the result of the consistency check, it is determined whether an update time period has expired.

[0061] In particular, the consistency level is determined based on the result of the consistency check and it is decided based on the consistency level whether the update time period is due.

[0062] Therefore, in this case, the updating time period is not a fixedly prescribed time interval, but is determined according to the current specific characteristics of the imaging system, in particular the image data.

[0063] The consistency check includes, in particular, a check of whether the image data, the volume model, the values ​​for the input volume set used to generate the image data, and the refinement model are consistent with one another.

[0064] For example, if it is determined that they are consistent and the update period has not expired, the refined model can be retained. On the other hand, if it is determined that they are inconsistent or insufficiently consistent, the update period has expired, and an updated model can be generated based on the refined model, as described above.

[0065] Acquiring the volume model may be understood in particular to mean providing it in a computer-readable form, in particular storing it. However, the method according to the invention may also comprise generating or partially generating the volume model, for example by adapting a predetermined statistical volume model.

[0066] For example, the volume model corresponds to a three-dimensionally resolved distribution of the respective ionizing radiation attenuation coefficient, for example constructed in the form of a voxel model.

[0067] The consistency check can be performed in a variety of ways. According to at least one embodiment, simulated image data are generated based on the volume model, the values ​​for the input volume set used to generate the image data, and the refined model, and the simulated image data are compared with the image data for the consistency check.

[0068] The result of the consistency check includes in particular the deviation of the image data from the simulated image data. If the deviation is greater than a specified limit value, it can be determined that the update time period has expired.

[0069] According to at least one embodiment, a comparison model is generated from the image data, the volume model and values ​​for an input volume set for generating the image data, and the comparison model is compared with the refined model for a consistency check.

[0070] The results of the consistency check include in particular deviations of the comparison model from the refined model, for example deviations of the corresponding model parameters. If the deviation is greater than a specified limit value, it can be determined that the update time period has expired.

[0071] According to at least one embodiment, a comparison volume model is generated from the image data, values ​​for a set of input volumes used to generate the image data, and a refined model, and the comparison volume model is compared with the volume model for a consistency check.

[0072] The result of the consistency check includes, in particular, a comparison of the deviations between the body models and the body models. If the deviations are greater than a specified limit value, it can be determined that the update time period has expired.

[0073] According to at least one embodiment, the consistency check comprises applying a further algorithm trained by machine learning to the input data which depends on the image data, the volume model, the values ​​for the input volume set used to generate the image data, and the refined model.

[0074] In addition, the output of the trained algorithm is equivalent to the result of the consistency check, that is, the degree of consistency of the input data.

[0075] According to at least one embodiment, the imaging system is an X-ray based imaging system and the radiation source is an X-ray radiation source, in particular an X-ray tube.

[0076] According to a further aspect of the invention, a data processing device is provided which has at least one computing unit which is configured to carry out the method according to the invention, in particular a computer-implemented method according to the invention.

[0077] A computing unit is particularly understood to be a data processing device comprising a processing circuit. A computing unit can therefore process data in particular to perform computing operations. This may also include operations for indexing access to data structures, such as look-up tables (English: "look-up tables").

[0078] The computing unit may in particular comprise 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 (English: “application-specific integrated circuit”), one or more field-programmable gate arrays, FPGAs, and / or one or more single-chip systems, SoCs (English: “system on a chip”). The computing unit may also comprise one or more processors, such as one or more microprocessors, one or more central processing units, CPUs (English: “central processing unit”), one or more graphics processing units, GPUs (English: “graphics processing unit”), and / or one or more signal processors, in particular one or more digital signal processors, DSPs. The computing unit may also comprise a physical or virtual combination of computers or other mentioned units.

[0079] In various embodiments, the computing unit comprises one or more hardware and / or software interfaces and / or one or more storage units.

[0080] The memory cell can be designed as a volatile data memory, such as a dynamic random access memory, DRAM (English: dynamic random access memory) or a static random access memory, SRAM (English: static random access memory), or a non-volatile data memory, such as a read-only memory, ROM (English: read-only memory), a programmable read-only memory, PROM (English: programmable read-only memory), an erasable programmable read-only memory, EPROM (English: erasable programmable read-only memory), an electrically erasable programmable read-only memory, EEPROM (English: electrically erasable programmable read-only memory), a flash memory or a flash EEPROM, a ferroelectric random access memory, FRAM (English: ferroelectric random access memory), a magnetoresistive random access memory, MRAM (English: magnetoresistive random access memory) or a phase change random access memory, PCRAM (English: phase-change random access memory).

[0081] According to another aspect of the invention, an imaging system is provided which has a data processing device according to the invention, a radiation source for generating ionizing radiation and a radiation detector for detecting part of the ionizing radiation, in particular part of the ionizing radiation that has passed through an object to be imaged.

[0082] Further embodiments of the imaging system according to the invention are directly derived from the various embodiments of the method according to the invention, and vice versa. In particular, individual features and corresponding explanations and advantages related to the different embodiments of the method according to the invention are similarly transferred to the corresponding embodiments of the imaging system according to the invention.

[0083] According to another aspect of the invention, a computer program is provided with commands which, when executed by at least a data processing device, in particular a data processing device according to the invention, cause the data processing device to carry out a method according to the invention.

[0084] For example, the command can exist as a program code. For example, the program code can be provided in the form of binary code or an assembler and / or a programming language (such as C language) source code and / or a program script, such as Python.

[0085] According to another aspect of the present invention, a computer-readable storage medium storing a computer program according to the present invention is provided.

[0086] The computer program and the computer-readable storage medium are respectively a computer program product with instructions.

[0087] Other features and feature combinations of the present invention are obtained from the drawings and their description as well as the claims. In particular, other embodiments of the present invention do not necessarily include all the features of the claims. Other embodiments of the present invention may have features or feature combinations not mentioned in the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The present invention is further described below based on specific embodiments and related schematic diagrams. The same or functionally identical elements in the drawings are provided with the same reference numerals. The description of the same or functionally identical elements may not necessarily be repeated in different drawings.

[0089] In the attached figure:

[0090] Figure 1 A schematic diagram showing an exemplary embodiment of an imaging system according to the present invention;

[0091] Figure 2 A schematic flow chart showing an exemplary embodiment of a method for estimating a characteristic quantity of radiation exposure according to the present invention;

[0092] Figure 3 A schematic flow chart showing a further exemplary embodiment of the method for estimating a characteristic quantity of radiation exposure according to the present invention;

[0093] Figure 4 A schematic diagram showing measuring a characteristic quantity of radiation exposure according to a further exemplary embodiment of a method for estimating a characteristic quantity of radiation exposure according to the present invention. DETAILED DESCRIPTION

[0094] Figure 1 Schematically, an exemplary embodiment of an imaging system 1 according to the invention is shown in FIG. The imaging system 1 has at least one computing unit 2 and an imaging modality having a radiation source 4 for generating ionizing radiation 7 and a radiation detector 3 for detecting part of the ionizing radiation 7. For example, the imaging modality is designed as an X-ray-based imaging modality, such as an X-ray-based angiography system, and has an X-ray source as the radiation source 4 and an X-ray detector as the radiation detector 3. Furthermore, a patient 5 is represented as an object to be imaged.

[0095] At least one computing unit 2 is configured to execute the method according to the invention for estimating a characteristic quantity of radiation exposure, such as kerma or DAP, for the imaging system 1 . Figure 2A schematic flow chart of an exemplary embodiment of such a method is shown in , wherein step 260 is optional.

[0096] Kerma, in particular air kerma, corresponds to the energy released per unit mass, in particular air, measured in Gy. It can be determined as the product of the energy, the photon flux rate and the energy absorption rate of the radiation 7 in the air. In a homogeneous medium such as air, the kerma is approximately equal to the dose. In Gym 2 The DAP, measured in units of , is defined as the kerma multiplied by the corresponding area exposed to the radiation. Apart from the medium, especially air, with which the radiation 7 interacts, kerma and DAP depend primarily on the energy spectrum of the radiation. Both quantities convey the same information. If either kerma or DAP is known, the other quantity can be derived.

[0097] Figure 4 Schematically shows the radiation path from the radiation source 4 to the radiation detector 3 for another exemplary embodiment of the imaging system 1. Here, a radiation filter 8 (in the case of an X-ray tube, for example, a filter and / or a wedge filter for beam hardening, also referred to as a wedge filter) and / or a beam shaping device 9 (for example, a collimator) is arranged directly after the radiation source 4. A measuring device 6, for example a DAP chamber, can be introduced between the patient 5 and the radiation source 4, the radiation filter 8 and the beam shaping device 9 for measuring radiation exposure characteristics.

[0098] In step 200, a basic model for determining a characteristic quantity of radiation exposure from a set of input quantities is obtained, wherein the input quantity comprises at least one radiation source operating parameter. In step 220, measured measurement data for the imaging system 1 are obtained, in particular by a measuring device 6, which comprises characteristic quantities of radiation exposure for a plurality of different values ​​of the at least one radiation source operating parameter. The basic model is adjusted according to the measurement data to generate a refined model.

[0099] In step 240 , a current value for the input variable set is determined for the imaging system 1 and an estimated value for the radiation exposure characteristic variable is determined by means of a refined model as a function of the current value for the input variable set.

[0100] In optional step 260, in some embodiments, additional measurement data measured for the imaging system 1 is obtained, which includes radiation exposure characteristic quantities for a plurality of additional different values ​​of at least one radiation source operating parameter. An updated model is generated by adjusting the refined model according to the additional measurement data. Additional values ​​for the set of input quantities for the imaging system 1 are determined and additional estimates for the radiation exposure characteristic quantities are determined by the updated model according to the additional values ​​for the set of input quantities.

[0101] Since the radiation exposure characteristic quantity, in particular the DAP or the kerma, depends on the energy spectrum of the radiation 7, it can in principle be estimated with the aid of information about the radiation source 4, in particular the radiation source operating parameters (such as kV peak value, tube current, focal spot size, etc. in the case of an X-ray tube), if necessary with the aid of information about the radiation filter 8, such as filter material thickness, wedge filter position, etc., or with the aid of information about the beam shaper 9, such as the extent of the collimation field, and with the aid of information about the exposure, such as exposure duration, pulse width, etc., since this information is usually available at each recording. However, the energy spectrum of the radiation source 4 may change over time, for example due to aging effects, and so may the radiation filter 8 and the beam shaper 9. Such changes are implicitly taken into account by the refined model and the accuracy of the refined model is therefore better than that of the basic model.

[0102] The basic model itself can be provided in different ways. For example, during the development phase, reference measurement data measured for at least one reference imaging system can be obtained, which contain radiation exposure characteristics for a plurality of different values ​​of at least one radiation source operating parameter, and the basic model is generated based on the reference measurement data. The reference measurement data can be very extensive here. For example, a physical initial model can be adjusted based on the reference measurement data in order to generate a basic model, which is used to determine the radiation exposure characteristics based on the input quantity set. The basic model can also be generated by training based on the reference measurement data by an algorithm that can be trained by machine learning. The basic model can also be recreated occasionally in order to take into account changes in the system.

[0103] The measurement data used for generating the refined model may be detected, for example, during production of the specific imaging system 1 , in particular the radiation source 4 , wherein the measurement data may be smaller than the range of the reference measurement data.

[0104] For example, the basic model can be a physical model that calculates the ideal energy spectrum and derives the radiation exposure characteristic quantity from it. The basic model can also be an approximation of such a physical model, for example, obtained by polynomial or spline curve fitting. The basic model can also be a model based on machine learning training.

[0105] For example, the refined model can be a model trained based on machine learning for estimating effective parameters for aged components of the imaging system 1. For example, these effective parameters can be changed in the refined model compared to the basic model. In the case of an X-ray imaging system, for example, an aged metal filter made of a first material, such as copper, can be approximated by an effective metal filter made of another material and / or with another material thickness. The refined model can be implemented in such a way that it obtains the basic model and corrects the output of the basic model, for example by multiplying by a scaling correction factor.

[0106] Figure 3A schematic flow chart of another exemplary embodiment of the method according to the present invention is shown in FIG. 1 , wherein steps 200, 220, 240 and 260 are Figure 2 The steps of the method correspond.

[0107] In accordance with Figure 3 In the method, a consistency check is performed after step 240 and before step 260. Step 260 is performed only when it is determined that there is inconsistency according to the result of the consistency check, otherwise the refined model continues to be used.

[0108] For the consistency check, image data representing the patient 5 generated by the imaging system 1 are acquired and a volume model of the body of the patient 5 is obtained. The consistency check is performed as a cross validation of the image data, the volume model, the values ​​for the input volume set used to generate the image data and the refined model.

[0109] For example, simulated image data can be generated based on the volume model, the values ​​for the input volume set for generating the image data, and the refined model, and the simulated image data can be compared with the image data for consistency checking. For example, the simulated image data can be generated using rendering methods and / or physical simulations. If the simulated image data deviate too much from the image data according to predetermined limits, an inconsistency can be determined and the refined model can be updated accordingly.

[0110] Additionally or alternatively, a comparison model can also be generated based on the image data, the volume model and the values ​​for the input volume set for generating the image data, and compared with the refined model for consistency checking. The comparison model can be determined, for example, by an inverse rendering method and / or a physical simulation. If the comparison model deviates too much from the refined model according to a predetermined limit, an inconsistency can be determined and the refined model can be updated accordingly.

[0111] Additionally or alternatively, a comparison volume model can be generated based on the image data, the values ​​for the input quantity set used to generate the image data, and the refined model and compared with the volume model for consistency checking. The comparison volume model can be determined, for example, by an inverse rendering method and / or a physical simulation. If the comparison volume model deviates too much from the volume model according to a predetermined limit, it can be concluded that there is an inconsistency and the refined model is updated accordingly.

[0112] The consistency check can be used to find differences or deviations between the corresponding predictions and the actual input data. The consistency check can also be performed as a solution to an optimization problem by inverse rendering (also called differentiable rendering) and / or physical simulation. The corresponding comparison of data can also be performed by a suitably trained algorithm, which for example receives image data and simulated image data as input data and predicts a consistency value as output. Similarly, comparing a model with a refined model or comparing a volume model with a volume model can also be used as input data.

[0113] Cross-validation also allows the identification of interfering factors in the entire method chain in order to ensure that the correct action is recommended. For example, if the bulk model is the main cause of the inconsistency, then there is no need to update the detailed model if necessary.

[0114] As an alternative to the cross validation described above, an algorithm suitably trained by machine learning may be used to predict whether the refined model needs to be updated. One possible implementation is an algorithm that takes as input the parameters for defining the refined model, the parameters for defining the volume model, the parameters for defining the system geometry, and the values ​​used to generate the image data, and predicts a consistency value as output.

[0115] By being able to determine flexibly and in accordance with the current situation whether an updated detailed model is necessary, unnecessary measures and thus time and costs can be saved. In addition, the need for an update can be identified quickly and responded to appropriately.

Claims

1. A method for estimating a characteristic quantity of radiation exposure of an imaging system (1) with a radiation source (4), wherein: - obtaining a basic model for determining a characteristic quantity of radiation exposure from a set of input quantities, the set of input quantities comprising at least one radiation source operating parameter; - obtaining measured measurement data for the imaging system (1), the measurement data comprising radiation exposure characteristic quantities for a plurality of different values ​​of at least one radiation source operating parameter; -Adjust the basic model according to the measurement data to produce a refined model; - determining a current value for a set of input quantities for the imaging system (1); and - determining an estimated value for the characteristic quantity of radiation exposure based on the current value for the set of input quantities by refining the model.

2. The method according to claim 1, wherein: - obtaining measured reference measurement data for at least one reference imaging system, the reference measurement data comprising radiation exposure characteristic quantities for a plurality of different values ​​of at least one radiation source operating parameter; and - Generate a basic model based on reference measurement data.

3. The method according to claim 2, wherein: - obtaining a physical initial model for determining radiation exposure characteristic quantities from a set of input quantities; and - Generate a basic model by adjusting the physical initial model according to the reference measurement data.

4. The method according to claim 2, wherein: The basic model is generated by training a machine learning algorithm based on reference measurement data.

5. The method according to any one of the preceding claims, wherein: The input quantity set comprises at least one state and / or material parameter for the radiation filtering device (8) and / or the beam shaping device (9); and The measurement data comprise radiation exposure characteristic quantities for a plurality of different values ​​of at least one state and / or material parameter.

6. The method according to any one of the preceding claims, wherein: - the set of input quantities comprises at least one exposure parameter; and The measurement data comprises radiation exposure characteristic quantities for a plurality of different values ​​of at least one exposure parameter.

7. The method according to any one of the preceding claims, wherein: The radiation exposure characteristic quantity is the dose area product or the kerma or a quantity that depends on the dose area product and / or the kerma.

8. The method according to any one of the preceding claims, wherein: After expiration of the update time period after measuring said measurement data: - obtaining measured further measurement data for the imaging system (1), the further measurement data comprising radiation exposure characteristic quantities for a further plurality of different values ​​of at least one radiation source operating parameter; - generating an updated model by adapting the refined model according to the further measurement data; - determining a further value for the imaging system (1) for the set of input quantities; and - determining an estimated value for the radiation exposure characteristic variable based on further values ​​for the set of input variables by updating the model.

9. The method according to claim 8, wherein - obtaining image data generated by the imaging system (1), the image data representing the object to be imaged (5); - obtaining a volume model for the object (5); - performing a consistency check based on the image data, the volume model, the values ​​used to generate the image data for the set of input volumes and the refined model; and -Determine whether the update time period has expired based on the results of the consistency check.

10. The method according to claim 9, wherein - generating simulated image data based on the volume model, the values ​​for the input volume set for generating image data and the refined model, and comparing the simulated image data with the image data for a consistency check; and / or - generating a comparison model based on the image data, the volume model and the values ​​for the set of input volumes used to generate the image data, and comparing the comparison model with the refined model for a consistency check; and / or - generating a comparison volume model from the image data, the values ​​for the input volume set used to generate the image data and the refinement model and comparing the comparison volume model with the volume model for a consistency check.

11. The method according to claim 9, wherein: The consistency check comprises applying a further algorithm trained by machine learning to the input data which depends on the image data, the volume model, the values ​​for the set of input volumes used to generate the image data, and the refined model.

12. The method according to any one of the preceding claims, wherein: The imaging system (1) is an X-ray based imaging system (1), and the radiation source (4) is an X-ray radiation source (4).

13. A data processing device (2) having at least one computing unit which is configured to carry out the method according to one of the preceding claims.

14. An imaging system (1) having a radiation source (4) for generating ionizing radiation (7) and a radiation detector (3) for detecting a portion of the ionizing radiation (7), and a data processing device (2) according to claim 13.

15. A computer program product having commands which, when executed by a data processing device (2), cause the data processing device (2) to carry out the method according to one of claims 1 to 12.

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