Identifying artifacts in synthetic medical recordings

Abstract

The invention is concerned with the technical area of generating synthetic medical recordings and relates to a method, a computer system, and a computer-readable storage medium comprising a computer program for identifying artifacts in synthetic medical recordings.

Description

[0001]BHC231015 FC Detecting Artifacts in Synthetic Medical Images COPYRIGHT NOTICE Part of the disclosure of this patent specification contains material that is subject to copyright protection. The copyright holder does not object to facsimile reproduction of the patent specification as it appears in a patent file or in the files of the Patent Office, but otherwise reserves all copyright and other rights of any kind. © 2023 Bayer AG TECHNICAL FIELD The present disclosure relates to the technical field of generating synthetic medical images. Subjects of the present disclosure are a method, a computer system, and a computer-readable storage medium comprising a computer program for detecting artifacts in synthetic medical images. INTRODUCTION Artificial intelligence is increasingly finding its way into medicine. Machine learning models are not only usedto identify signs of disease in medical images of the human or animal body (see, for example, WO2018202541A1, WO2020229152A1). They are also increasingly being used to generate synthetic (artificial) medical images. WO2021052896A1 and WO2021069338A1, for example, describe methods for generating an artificial medical image that shows an examination area of ​​an examination subject in a first time period (phase). The artificial medical image is generated using a trained machine learning model based on medical images that show the examination area in a second time period (phase). Using the method, for example, radiological examinations can be accelerated; instead of measuring radiological images over a longer period of time,Measurements are taken only within a portion of the time period, and one or more radiological images are predicted for the remaining portion of the time period using the trained model. WO2019 / 074938A1 and WO2022184297A1, for example, describe methods for generating an artificial radiological image showing an examination area of ​​an examination subject after the application of a standard amount of contrast agent.although only a smaller amount of contrast agent than the standard amount has been applied. The standard amount is the amount recommended by the manufacturer and / or distributor of the contrast agent and / or the amount approved by a regulatory authority and / or the amount listed in a package insert for the contrast agent. The methods described in WO2019 / 074938A1 and WO2022184297A can therefore be used to reduce the amount of contrast agent. The medical images generated by the trained machine learning models may contain errors (see, for example, K. Schwarz et al.: On the Frequency Bias of Generative Models, https: / / doi.org / 10.48550 / arXiv.2111.02447). Such errors can be problematic because a physician could make a diagnosis and / or initiate therapy based on the artificial medical images. When a physician examines artificial medical images, the physician must knowwhether features in the artificial medical images can be traced back to real features of the examination subject, or whether they are artifacts that are due to prediction errors by the trained machine learning model. SUMMARY These and other problems are addressed by the subject matter of the present disclosure. A first subject matter of the present disclosure is a computer-implemented method for generating at least one confidence value for a synthetic image, comprising the steps of: - receiving at least one image of an examination region of an examination subject, - generating a plurality of partial images based on the at least one received image, wherein each partial image represents a partial region of the examination region of the examination subject, wherein partial regions represented by different partial images,partially overlap but not completely overlap, - generating a plurality of synthetic partial images at least partially based on the generated partial images using a generative model, - determining color values ​​of corresponding image elements of synthetic partial images, wherein corresponding image elements represent the same partial area of ​​the examination region, - determining a scatter measure of the color values ​​of corresponding image elements, - determining a confidence value based on the scatter measure, - outputting the confidence value. A further subject of the present disclosure is a computer system comprising a processor and a memory that stores an application program configured to perform an operation when executed by the processor, wherein the operation comprises: - receiving at least one image of an examination region of an examination object,- Generating a plurality of partial images based on the at least one received image, wherein each partial image represents a partial area of ​​the examination area of ​​the examination subject, wherein partial areas represented by different partial images partially but not completely overlap; - Generating a plurality of synthetic partial images at least partially based on the generated partial images using a generative model; - Determining color values ​​of corresponding image elements of synthetic partial images, wherein corresponding image elements represent the same partial area of ​​the examination area; - Determining a scatter measure of the color values ​​of corresponding image elements; - Determining a confidence value based on the scatter measure; - Outputting the confidence value. A further subject matter of the present disclosure is a computer-readable storage medium comprising a computer program,which can be loaded into a working memory of a computer system and causes the computer system to carry out the following steps: - receiving at least one image of an examination area of ​​an examination object, - generating a plurality of partial images based on the at least one received image, wherein each partial image represents a partial area of ​​the examination area of ​​the examination object, wherein partial areas represented by different partial images partially but not completely overlap, - generating a plurality of synthetic partial images at least partially based on the generated partial images by means of a generative model, - determining color values ​​of corresponding image elements of synthetic partial images, wherein corresponding image elements represent the same partial area of ​​the examination area, - determining a measure of dispersion of the color values ​​of corresponding image elements,- Determining a confidence value based on the measure of dispersion, - Outputting the confidence value. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 shows, by way of example and schematically, the generation of partial images based on a received image of an examination region of an examination object. Fig. 2 shows, by way of example and schematically, the generation of synthetic partial images based on partial images using a generative model and the merging of the synthetic partial images into a synthetic image. Fig. 3 shows, by way of example and schematically, the combining of synthetic images into a combined synthetic image. Fig. 4 shows, by way of example and schematically, the combining of synthetic images into a combined synthetic image. Fig. 5a shows, by way of example and schematically,how partial images are generated from each image of a plurality of received images and how synthetic partial images are generated based on the generated partial images. Fig. 5b shows, by way of example and schematically,how synthetic partial images can be combined to form synthetic images and how the synthetic images can be combined to form a unified synthetic image. Fig. 6 shows, by way of example and schematically, the determination of the at least one confidence value and the generation of a confidence representation. Fig. 7 shows an embodiment of the method of the present disclosure in the form of a flow chart. Fig. 8 shows, by way of example and schematically, a method for training a generative machine learning model. Fig. 9 shows, by way of example and schematically, a computer system according to the present disclosure. Fig. 10 shows, by way of example and schematically, a further embodiment of the computer system of the present disclosure. DETAILED DESCRIPTION The invention is explained in more detail below, without distinguishing between the subject matters of the present disclosure (method, computer system,computer-readable storage medium). Rather, the following statements are intended to apply mutatis mutandis to all subject matter of the invention, regardless of the context in which they are described (method, computer system, computer-readable storage medium). If steps are specified in a sequence in the present description or in the claims, this does not necessarily mean that the invention is limited to the specified sequence. Rather, it is conceivable that the steps can also be carried out in a different sequence or in parallel to one another, unless one step builds on another, which absolutely requiresthat the constructive step is subsequently carried out (this will become clear in each individual case). The sequences mentioned are therefore preferred embodiments of the present disclosure. The invention is explained in more detail at some points with reference to drawings. The drawings show specific embodiments with specific features and combinations of features, which primarily serve for illustration purposes; the invention should not be understood as being limited to the features and combinations of features shown in the drawings. Furthermore, statements made in the description of the drawings with regard to features and combinations of features are intended to apply generally.This means that it is also transferable to other embodiments and not limited to the embodiments shown. The present disclosure describes means for assessing the trustworthiness of a synthetic image of an examination area of ​​an examination subject. The term "trustworthiness" is understood to mean that a person examining the synthetic image can trust that structures and / or morphologies and / or textures depicted in the synthetic image can be traced back to real structures and / or real morphologies and / or real textures of the examination area of ​​the examination subject and are not artifacts. The term "synthetic" means that the synthetic image is not the direct result of a measurement on a real examination subject,but was artificially generated (calculated). However, a synthetic image can be based on image recordings of a real examination object, i.e., one or more image recordings of a real examination object can be used to generate the synthetic image. Examples of synthetic images are described in the introduction and in the further description of the present disclosure. According to the present disclosure, a synthetic image is generated by a machine learning model. The generation of a synthetic image with the aid of a machine learning model is also referred to as "prediction" in this description. The terms "synthetic" and "predicted" are used synonymously in this disclosure. In other words, a synthetic image is an image generated by a (trained) machine learning model based on input data,wherein the input data may comprise one or more metrologically generated images. The "examination object" is preferably a human or an animal, preferably a mammal, most preferably a human. The "examination region" is a part of the examination object, for example an organ of a human or animal such as the liver, brain, heart, kidney, lung, stomach, intestine, pancreas, thyroid, prostate, breast, or a part of said organs, or multiple organs, or another part of the examination object. The examination region may also comprise multiple organs and / or parts of multiple organs. In one embodiment, the examination region comprises a liver or a part of a liver, or the examination region is a liver or a part of a liver of a mammal.preferably of a human. In a further embodiment, the examination region comprises a brain or part of a brain, or the examination region is a brain or part of a brain of a mammal, preferably of a human. In a further embodiment, the examination region comprises a heart or part of a heart, or the examination region is a heart or part of a heart of a mammal, preferably of a human. In a further embodiment, the examination region comprises a thorax or part of a thorax, or the examination region is a thorax or part of a thorax of a mammal, preferably of a human. In a further embodiment, the examination region comprises a stomach or part of a stomach, or the examination region is a stomach or part of a stomach of a mammal,preferably of a human. In a further embodiment, the examination region comprises a pancreas or part of a pancreas, or the examination region is a pancreas or part of a pancreas of a mammal, preferably of a human. In a further embodiment, the examination region comprises a kidney or part of a kidney, or the examination region is a kidney or part of a kidney of a mammal, preferably of a human. In a further embodiment, the examination region comprises one or both lungs or part of a lung of a mammal, preferably of a human. In a further embodiment, the examination region comprises a breast or part of a breast, or the examination region is a breast or part of a breast of a female mammal,preferably of a female human. In a further embodiment, the examination region comprises a prostate or part of a prostate, or the examination region is a prostate or part of a prostate of a male mammal, preferably of a male human. The examination region, also called field of view (FOV), represents in particular a volume that is depicted in radiological images. The examination region is typically defined by a radiologist, for example, on an overview image. Of course, the examination region can alternatively or additionally be defined automatically, for example, based on a selected protocol. The term "image" refers to a data structure that represents a spatial distribution of a physical signal. The spatial distribution can have any dimension, e.g., 2D, 3D,4D or a higher dimension. The spatial distribution can have any shape, e.g., forming a grid and thereby defining pixels or voxels, where the grid can be irregular or regular. The physical signal can be any signal, e.g., proton density, echogenicity, transmittance, absorbance, relaxivity, information about rotating hydrogen nuclei in a magnetic field, color, grayscale, depth, surface or volume occupancy. The term "image" is preferably understood to mean a two-, three-, or higher-dimensional visually perceivable representation of the examination area of ​​the object under investigation. The received image is usually a digital image. The term "digital" means that the image was generated by a machine, usually a computer system.can be processed. "Processing" refers to the well-known methods of electronic data processing (EDP). A digital image can be processed, edited, and reproduced using computer systems and software, as well as converted into standardized data formats, such as JPEG (Joint Photographic Experts Group graphics format), PNG (Portable Network Graphics), or SVG (Scalable Vector Graphics). Digital images can be visualized using suitable display devices, such as computer monitors, projectors, and / or printers. In a digital image, image content is usually represented and stored as integers. In most cases, these are two- or three-dimensional images that can be binary-encoded and, if necessary, compressed. Digital images are usually raster graphics,in which the image information is stored in a uniform raster. Raster graphics consist of a grid-like arrangement of so-called image points (pixels) in the case of two-dimensional representations or volume elements (voxels) in the case of three-dimensional representations. In four-dimensional representations, the term doxel (dynamic voxel) is often used for the image elements. For higher-dimensional representations or in general, the term "n-xel" is sometimes used, where n indicates the respective dimension. In this disclosure, the term image element is generally used. A image element can therefore be an image point (pixel) in the case of a two-dimensional representation, a volume element (voxel) in the case of a three-dimensional representation,a dynamic voxel (doxel) in the case of a four-dimensional representation or a higher-dimensional image element in the case of a higher-dimensional representation. Each image element of an image is usually assigned at least one color value. The color value specifies how (e.g., in which color) the image element should be visually displayed (e.g., on a monitor or printer). The simplest case is a binary image, in which an image element is displayed as either white or black. Typically, the color value "0" stands for "black" and the color value "1" for "white." In a grayscale image, each image element is assigned a gray level, ranging from black through a defined number of gray shades to white. The gray levels are also referred to as gray values. The number of gradations can, for example, range from 0 to 255 (i.e., encompass 256 gray levels / gray values).Here, too, the value "0" usually stands for "black" and the highest gray value (in this example, the value 255) stands for "white." In a color image, the color coding used for an image element is defined, among other things, by the color space and the color depth. In an image whose color is defined by the so-called RGB color space (RGB stands for the primary colors red, green, and blue), each pixel is assigned three color values: one color value for the color red, one color value for the color green, and one color value for the color blue. The color of an image element results from the superposition (additive mixing) of the three color values. The individual color value can, for example, be discretized into 256 distinguishable levels, called tonal values, which usually range from 0 to 255. The tonal value "0" of each color channel is usually the darkest color nuance. If all three color channels have the tonal value 0,the corresponding image element appears black; if all three color channels have a tonal value of 255, the corresponding image element appears white. Regardless of whether it is a binary image, a grayscale image, or a color image, the term "color value" is used in this disclosure for the information in which color (including the "colors" "black" and "white" as well as all shades of gray) an image element is to be displayed. A color value can therefore be a tonal value of a color channel, a shade of gray, or stand for "black" or "white." A color value in an image (especially a medical image) usually represents the strength of a physical signal (see above). It should be noted that the "color value" can also be a value for the physical signal itself. There are a multitude of possible digital image formats and color codings. For the sake of simplicity, this description assumesthat the images in question are raster graphics with a specific number of image elements. However, this assumption should not be understood as limiting in any way. Those skilled in image processing will know how to apply the teachings of this description to image files that are in other image formats and / or in which the color values ​​are coded differently. An "image" in the sense of the present disclosure may also be one or more sections from a video sequence. In a first step, at least one image of an examination region of an examination object is received. The term "receiving" encompasses both the retrieval of images and the receipt of images that are transmitted, for example, to the computer system of the present disclosure. The at least one image may be from a computer tomography scanner, a magnetic resonance imaging scanner, an ultrasound scanner,received by a camera and / or another device for generating images. The at least one image can be read from a data storage device and / or transmitted from a separate computer system. Preferably, the at least one received image is a two-dimensional or three-dimensional representation of an examination area of ​​an examination subject. In one embodiment of the present disclosure, the at least one received image is a medical image. A “medical image” is a visual representation of an examination area of ​​a human or animal that can be used for diagnostic and / or therapeutic purposes. There are a variety of techniques with which medical images can be generated; examples of such techniques are X-ray, computed tomography (CT), fluoroscopy, magnetic resonance imaging (MRI), ultrasound (sonography), endoscopy,Elastography, tactile imaging, thermography, microscopy, positron emission tomography, optical coherence tomography (OCT), fundus photography, and others. Examples of medical images include CT scans, X-ray images, MRI scans, fluorescein angiography images, OCT scans, histological images, ultrasound images, fundus images, and / or others. The at least one received image may be a CT scan, MRI scan, ultrasound image, OCT scan, and / or another representation of an examination area of ​​an examination subject. The at least one received image may also include representations of different modalities, e.g., a CT scan and an MRI scan. Preferably, the at least one received image is the result of a radiological examination. "Radiology" is the branch of medicinewhich deals with the application of electromagnetic radiation and (including, for example, ultrasound diagnostics) mechanical waves for diagnostic, therapeutic, and / or scientific purposes. In addition to X-rays, other ionizing radiation such as gamma radiation or electrons are also used. Since a key application is imaging, other imaging techniques such as sonography and magnetic resonance imaging (MRI) are also considered radiology, even though these techniques do not involve the use of ionizing radiation. The term "radiology" within the meaning of this disclosure thus includes, in particular, the following examination methods: computed tomography, magnetic resonance imaging,Sonography. In one embodiment of the present disclosure, the radiological examination is a magnetic resonance imaging examination. In another embodiment, the radiological examination is a computed tomography examination. In another embodiment, the radiological examination is an ultrasound examination. In radiological examinations, contrast agents are often used to enhance the contrast. “Contrast agents” are substances or mixtures of substances that improve the representation of structures and functions of the body in radiological examinations. In computed tomography, iodine-containing solutions are usually used as contrast agents. In magnetic resonance imaging (MRI), superparamagnetic substances (e.g., iron oxide nanoparticles,Superparamagnetic iron-platinum particles (SIPPs) or paramagnetic substances (e.g., gadolinium chelates, manganese chelates, hafnium chelates) are used as contrast agents. In the case of sonography, fluids containing gas-filled microbubbles are usually administered intravenously. Examples of contrast agents can be found in the literature (see e.g. ASL Jascinth et al.: Contrast Agents in computed tomography: A Review, Journal of Applied Dental and Medical Sciences, 2016, Vol. 2, Issue 2, 143 – 149; H. Lusic et al.: X-ray- Computed Tomography Contrast Agents, Chem. Rev. 2013, 113, 3, 1641-1666; https: / / www.radiology.wisc.edu / wp-content / uploads / 2017 / 10 / contrast-agents-tutorial.pdf, MR Nough et al.: Radiographic and magnetic resonances contrast agents: Essentials and tips for safe practices,World J Radiol. 2017 Sep 28; 9(9): 339–349; LC Abonyi et al.: Intravascular Contrast Media in Radiography: Historical Development & Review of Risk Factors for Adverse Reactions, South American Journal of Clinical Research, 2016, Vol. 3, Issue 1, 1-10; ACR Manual on Contrast Media, 2020, ISBN: 978-1-55903-012-0; A. Ignee et al.: Ultrasound contrast agents, Endosc Ultrasound.2016 Nov-Dec; 5(6): 355–362). MRI contrast agents exert their effect in an MRI examination by altering the relaxation times of the structures that absorb contrast agents. Two groups of substances can be distinguished: paramagnetic and superparamagnetic substances. Both groups of substances possess unpaired electrons that induce a magnetic field around the individual atoms or molecules. Superparamagnetic contrast agents lead to a predominant T2 shortening, while paramagnetic contrast agents essentially lead to a T1 shortening. The effect of these contrast agents is indirect,because the contrast agent itself does not emit a signal, but only influences the signal intensity in its surroundings. An example of a superparamagnetic contrast agent is iron oxide nanoparticles (SPIO). Examples of paramagnetic contrast agents are gadolinium chelates such as gadopentetate dimeglumine (trade name: Magnevist, ® among others), gadoteric acid (Dotarem ® , Dotagita ® , Cyclolux ® ), gadodiamide (Omniscan ® ), Gadoteridol (ProHance ® ), Gadobutrol (Gadovist ® ), gadopiclenol (Elucirem, Vueway) and gadoxetic acid (Primovist ® / Eovist ®). In one embodiment, the radiological examination is an MRI examination using an MRI contrast agent. In another embodiment, the radiological examination is a CT examination using a CT contrast agent. In another embodiment, the radiological examination is a CT examination using an MRI contrast agent. In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium(III) 2-[4,7,10-tris(carboxymethyl)-1,4,7,10-tetrazacyclododec-1-yl]acetic acid (also referred to as gadolinium DOTA or gadoteric acid).In a further embodiment, the contrast agent is an agent comprising gadolinium(III) ethoxybenzyldiethylenetriaminepentaacetic acid (Gd-EOB-DTPA); preferably, the contrast agent comprises the disodium salt of gadolinium(III) ethoxybenzyldiethylenetriaminepentaacetic acid (also referred to as gadoxetic acid). In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium(III) 2-[3,9-bis[1-carboxylato-4-(2,3-dihydroxypropylamino)-4-oxobutyl]-3,6,9,15-tetrazabicyclo[9.3.1]pentadeca-1(15),11,13-trien-6-yl]-5-(2,3-dihydroxypropylamino)-5-oxopentanoate (also referred to as gadopiclenol) (see, for example, WO2007 / 042504 and WO2020 / 030618 and / or WO2022 / 013454).In one embodiment of the present disclosure, the contrast agent is an agent comprising dihydrogen[(±)-4-carboxy-5,8,11-tris(carboxymethyl)-1-phenyl-2-oxa-5,8,11-triazatridecane-13-oato(5-)]gadolinate(2-) (also referred to as gadobenic acid). In one embodiment of the present disclosure, the contrast agent is an agent comprising tetragadolinium [4,10-bis(carboxylatomethyl)-7-{3,6,12,15-tetraoxo-16-[4,7,10-tris-(carboxylatomethyl)-1,4,7,10-tetraazacyclododecan-1-yl]-9,9-bis({[({2-[4,7,10-tris-(carboxylatomethyl)-1,4,7,10-tetraazacyclododecan-1-yl]propanoyl}amino)acetyl]-amino}methyl)- 4,7,11,14-tetraazahepta-decan-2-yl}-1,4,7,10-tetraazacyclododecan-1-yl]acetate (also referred to as Gadoquatrane) (see, e.g., J. Lohrke et al.: Preclinical Profile of Gadoquatrane: A Novel Tetrameric, Macrocyclic High Relaxivity Gadolinium-Based Contrast Agent. Invest Radiol., 2022, 1, 57(10): 629-638; WO2016193190).In one embodiment of the present disclosure, the contrast agent is an agent comprising a Gd. 3+ -Complex of a compound of formula (I) (I) , wherein Ar is a group selected from where # represents the link to X, X represents a group consisting of CH2, (CH2)2, (CH2)3, (CH2)4 and *-(CH2)2-O-CH2- # is selected, where * represents the connection to Ar and # represents the link to the acetic acid residue, R 1 , R 2 and R 3 independently represent a hydrogen atom or a group selected from C1-C3 alkyl, -CH2OH, -(CH2)2OH and -CH2OCH3, R 4 a group selected from C2-C4-alkoxy, (H3C-CH2)-O-(CH2)2-O-, (H3C-CH2)-O-(CH2)2-O- (CH2)2-O- and (H3C-CH2)-O-(CH2)2-O-(CH2)2-O-(CH2)2-O-, R 5 represents a hydrogen atom, and R 6represents a hydrogen atom, or a stereoisomer, tautomer, hydrate, solvate or salt thereof, or a mixture thereof. In one embodiment of the present disclosure, the contrast agent is an agent comprising a Gd 3+ -Complex of a compound of formula (II) Are a group selected from represents, where # represents the linkage to X, X represents a group consisting of CH2, (CH2)2, (CH2)3, (CH2)4and *-(CH2)2-O-CH2- # is selected, where * represents the connection to Ar and # represents the link to the acetic acid residue, R 7 represents a hydrogen atom or a group selected from C1-C3 alkyl, -CH2OH, -(CH2)2OH and -CH2OCH3; R 8 a group selected from C2-C4 alkoxy, (H3C-CH2O)-(CH2)2-O-, (H3C-CH2O)-(CH2)2-O-(CH2)2-O- and (H3C-CH2O)-(CH2)2-O-(CH2)2-O-(CH2)2-O-; R 9 and R 10independently represent a hydrogen atom; or a stereoisomer, tautomer, hydrate, solvate, or salt thereof, or a mixture thereof. The term "C1-C3 alkyl" means a linear or branched, saturated, monovalent hydrocarbon group having 1, 2, or 3 carbon atoms, e.g., methyl, ethyl, n-propyl, and isopropyl. The term "C2-C4 alkyl" means a linear or branched, saturated, monovalent hydrocarbon group having 2, 3, or 4 carbon atoms. The term "C2-C4 alkoxy" means a linear or branched, saturated, monovalent group of the formula (C2-C4 alkyl)-O-, in which the term "C2-C4 alkyl" is as defined above, e.g., a methoxy, ethoxy, n-propoxy, or isopropoxy group. In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium 2,2',2''-(10-{1-carboxy-2-[2-(4-ethoxyphenyl)ethoxy]ethyl}-1,4,7,10-tetraazacyclododecane-1,4,7-triyl)triacetate (see, e.g.,WO2022 / 194777, Example 1). In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium 2,2',2''-{10-[1-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}triacetate (see, e.g., WO2022 / 194777, Example 2). In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium 2,2',2''-{10-[(1R)-1-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}triacetate (see, e.g., WO2022 / 194777, Example 4). In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium (2S,2'S,2''S)-2,2',2''-{10-[(1S)-1-carboxy-4-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}butyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}tris(3-hydroxypropanoate) (see, e.g., WO2022 / 194777, Example 15).In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium 2,2',2''-{10-[(1S)-4-(4-butoxyphenyl)-1-carboxybutyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}triacetate (see, e.g., WO2022 / 194777, Example 31). In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium 2,2',2''-{(2S)-10-(carboxymethyl)-2-[4-(2-ethoxyethoxy)benzyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}triacetate. In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium 2,2',2''-[10-(carboxymethyl)-2-(4-ethoxybenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7-triyl]triacetate.In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium(III) 5,8-bis(carboxylatomethyl)-2-[2-(methylamino)-2-oxoethyl]-10-oxo-2,5,8,11-tetraazadodecane-1-carboxylate hydrate (also referred to as gadodiamide). In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium(III) 2-[4-(2-hydroxypropyl)-7,10-bis(2-oxido-2-oxoethyl)-1,4,7,10-tetrazacyclododec-1-yl]acetate (also referred to as gadoteridol). In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium(III) 2,2',2''-(10-((2R,3S)-1,3,4-trihydroxybutan-2-yl)-1,4,7,10-tetraazacyclododecane-1,4,7-triyl)triacetate (also referred to as gadobutrol or Gd-DO3A-butrol). The at least one received image may also include representations of the examination region generated under different measurement conditions, e.g.a T1-weighted MRI image and / or a T2-weighted MRI image and / or a diffusion-weighted MRI image and / or another MRI image and / or one or more dual-energy CT images and / or one or more spectral CT images. The at least one received image can also include multiple radiological images generated after applying different amounts of a contrast agent and / or after applying different contrast agents, such as a native radiological image and / or a radiological image after applying a first amount of a contrast agent and / or one or more radiological images after applying a second contrast agent and / or a virtual non-contrast representation (VNC representation).The at least one received image can also comprise a plurality of radiological images that were generated at different times before and / or after the application of one or more contrast agents and / or that represent the examination region in different phases and / or states. Each received image comprises a plurality of image elements. Each image element of the plurality of image elements represents a partial region of the examination region of the examination object. The term “plurality of image elements” means at least 1,000, preferably at least 10,000, and even more preferably more than 100,000. It is conceivable for a received image to comprise one or more image elements that do not represent the examination region of the examination object, but rather another region, such as an adjacent and / or surrounding region. In a first step, a plurality of partial images are generated based on the at least one image.The term "multiplicity of partial images" means at least two, preferably at least ten, and even more preferably more than twenty partial images. Each partial image represents a sub-area of ​​the examination region of the examination object. Sub-areas represented by different partial images partially but not completely overlap. In other words: there are at least two partial images, each representing a sub-area of ​​the examination region, wherein the sub-areas partially but not completely overlap. In other words: there are sub-areas of the examination region that are represented by several (at least two) partial images, wherein the sub-images representing the same sub-area represent different further sub-areas.Preferably, each sub-region of the examination zone is represented by several (at least two) partial images in different constellations with other partial regions. A partial region of the examination zone is represented by one or more image elements of a partial image. There are therefore at least two partial images that have at least one image element in common, but differ in at least one image element. Here, “a common image element” is an image element that represents the same sub-region, and “different image elements” are image elements that represent different sub-regions. Preferably, for each partial image, there is at least one other partial image with at least one common image element and at least one different image element. Even more preferably, for each partial image, there are several other partial images with at least one common image element and at least one different image element.Preferably, the number of identical (common) image elements and / or different image elements is greater than 10, even more preferably greater than 100. Preferably, for each image element of the at least one received image, there are a plurality of partial images that also comprise this image element, wherein each partial image of the plurality of partial images differs from every other partial image of the plurality of partial images by at least one different image element. The partial images can be generated, for example, by dividing the at least one received image into partial images by sections, wherein the section lines (in the case of 2D images) or section surfaces (in the case of 3D images) run at different angles through the at least one received image. An embodiment for generating partial images is explained in more detail below with reference to Fig. 1, without wishing to limit the invention to the embodiment shown in Fig. 1.Statements made regarding the embodiments illustrated in the drawings of the present disclosure shall apply analogously to all other embodiments. Fig. 1 shows a received image I. i of an examination area of ​​an object under examination. The received image I i comprises a plurality of image elements, three image elements IE1, IE2, and IE3 are represented as points. From the received image I iA plurality of copies I1, I2, I3, and I4 are generated. One of the copies I1, I2, I3, or I4 can be the received image Ii itself. The number of copies generated for each received image (per image) typically corresponds to the number of synthetic images that can be generated and then combined into a unified synthetic image. Each copy I1, I2, I3, and I4 is divided into a plurality of partial images. In the present example, this is achieved by dividing each copy by sections along section planes. The section planes run at different angles through the copies I1, I2, I3, and I4. In the case of copy I2, for example, the section planes run parallel to the xz-plane; the partial images PI are created. 21 , PI 22 , PI 23 , PI 24 , PI 25 and PI 26 . In the case of copy I4, for example, the cutting planes run parallel to the yz-plane; the partial images PI are created 41 , PI42 , PI 43 , PI 44 , PI 45 , PI 46 and PI 47The division of the copies I1 and I3 is only indicated in Fig. 1; cutting planes are shown; the partial images resulting from the corresponding cuts along the cutting planes are not explicitly shown in Fig. 1. In the present example, the copies are divided into partial images by flat surfaces. This is a preferred embodiment of the present invention. However, it is also possible to divide the copies into partial images by curved surfaces or by other cuts. In the present example, the cutting planes in the individual copies are the same distance from one another. This is a preferred embodiment of the present invention. However, it is also possible for the distance between the cutting planes (or generally the cutting surfaces) to vary. In the case of copies I2 and I4, all partial images have the same size (they have the same number of image elements).In other words, the sub-area of ​​the examination region that they each represent is the same size for all sub-images. In the case of copies I1 and I3, only some of the sub-images have the same size. It is possible to make all sub-images the same size by padding areas in sub-images of smaller size with zeros (or another value). The number of sub-images generated from the individual copies can be the same or different. In the present example, the number of sub-images generated from the individual copies varies. In the case of copy I1, there are six, in the case of copy I2, there are six, in the case of copy I3, there are nine, and in the case of copy I4, there are seven. Preferably, the copies are divided into sub-images such that all resulting sub-images have the same size (i.e., number of image elements); this can be achieved in individual cases by padding.This has the advantage that sub-images of the same size can always be fed to the generative model. The generation of sub-images shown in Fig. 1 fulfills the above-mentioned requirements: - There are at least two sub-images that represent the same sub-area of ​​the object's examination area, but each additionally represents a different sub-area: The sub-area represented by image element IE1 is represented by both sub-image PI and sub-image PI. 25 as well as by the drawing PI 41 represents; each of the partial images PI 25 and PI 41 However, it also represents other parts of the study area that are not represented by the other sub-image. For example, the sub-image PI 25 For example, with the image element IE3 a sub-area defined by the sub-image PI 41 is not represented. The drawing part P 47represents with the image element IE3 a subarea, which is also represented by the subimage PI 25 is represented; however, it represents with the image element IE2 a sub-area which is defined by the partial image PI 25 not represented. - The drawings PI 25 and PI 47 have the picture element IE3 in common, but differ in at least one other picture element: Part PI 25 For example, includes the picture element IE1, which contains the partial picture PI 47 not included and the drawing file PI 47comprises the image element IE2, which the partial image PI25 does not comprise. The generation of partial images shown in Fig. 1 further satisfies a further condition of an above-mentioned embodiment: - For each partial image, there is at least one other partial image with at least one common image element and at least one different image element. In the present example, for each partial image, there are at least three partial images with at least one common image element and at least one different image element. Here, “a common image element” is an image element that represents the same sub-area, and “different image elements” are image elements that represent different sub-areas. In a next step, synthetic partial images are generated on the basis of the partial images. The synthetic partial images are generated using a model, which is referred to as a generative model in this disclosure.The generative model can be a trained machine learning model. A "machine learning model" can be understood as a computer-implemented data processing architecture. Such a model can receive input data and produce output data based on this input data and model parameters. Such a model can learn a relationship between the input data and the output data through training. During training, model parameters can be adjusted to produce a desired output for a specific input. When training such a model, the model is presented with training data from which it can learn. The trained machine learning model is the result of the training process. The training data includes input data and the correct output data (target data) that the model is to generate based on the input data. During training, patterns are recognized that map the input data to the target data.During the training process, the input data of the training data is fed into the model, and the model generates output data. The output data is compared with the target data. Model parameters are changed so that the deviations between the output data and the target data are reduced to a (defined) minimum. An optimization method such as a gradient descent method can be used to modify the model parameters to reduce the deviations. The deviations can be quantified using an error function (loss function). Such an error function can be used to calculate an error (loss) for a given pair of output data and target data. The goal of the training process can be to change (adjust) the parameters of the machine learning model so that the error is reduced to a (defined) minimum for all pairs in the training dataset.For example, if the output and target data are numbers, the error function can be the absolute difference between these numbers. In this case, a high absolute error may mean that one or more model parameters need to be changed significantly. For output data in the form of vectors, for example, difference metrics between vectors such as the mean square error, a cosine distance, a norm of the difference vector such as a Euclidean distance, a Chebyshev distance, an Lp norm of a difference vector, a weighted norm, or another type of difference metric of two vectors can be chosen as the error function. For higher-dimensional outputs, such as two-dimensional, three-dimensional, or higher-dimensional outputs, an element-wise difference metric can be used. Alternatively or additionally, the output data can be transformed before calculating an error value, e.g.into a one-dimensional vector. Figure 8 schematically shows an example of training a machine learning model and is described in more detail below. The generative model may include one or more algorithms that specify how a synthetic sub-image can be generated based on one or more sub-images. Typically, one or more sub-images are fed to the generative model, and the model generates a synthetic sub-image based on the one or more sub-images, model parameters, and optionally other input data. The generative model can be a machine learning model, for example as described in one of the following publications: WO2019 / 074938A1, WO2022 / 253687A1, WO2022 / 207443A1, WO2022 / 223383A1, WO20227184297A1, WO2022 / 179896A2, WO2021 / 069338A1, EP 22209510.1, EP23159288.2, PCT / EP2023 / 053324, PCT / EP2023 / 050207, CN110852993A, CN110853738A, US2021150671A1, arXiv:2303.15938v1, doi:10.1093 / jrr / rrz030. The generative model can be configured, for example, to generate a synthetic radiological image after the application of a second amount of contrast agent based on at least one received radiological image of the examination region before and / or after an application of a first amount of contrast agent, wherein the second amount is preferably greater than the first amount (as described, for example, in WO2019 / 074938A1 or WO2022184297A1). The at least one received radiological image can be, for example, an MRI image, and the synthetic radiological image can be a synthetic MRI image.The at least one received radiological image may also comprise a CT image before and / or after the application of a first amount of an MRI contrast agent, and the synthetic radiological image may be a synthetic CT image after the application of a second amount of an MRI contrast agent, wherein the second amount is preferably greater than the first amount and preferably greater than the standard amount of the MRI contrast agent for MRI examinations (as described, for example, in PCT / EP2023 / 053324). The "standard amount" is typically the amount recommended by the manufacturer and / or distributor of the contrast agent and / or the amount approved by a regulatory authority and / or the amount listed in a package insert for the contrast agent.The generative model can, for example, be configured to generate a synthetic radiological image based on at least one radiological image of an examination region of an examination subject, which represents the examination region in a first time period before and / or after the application of a contrast agent, which synthetic image represents the examination region in a second time period after the application of the contrast agent (as described, for example, in WO2021052896A1). In contrast to the methods described in the publications cited above, in the case of the present disclosure, however, not only one synthetic image is generated, but rather a plurality of synthetic images are generated, which can be combined in a subsequent step to form a single synthetic image—referred to in this description as a combined synthetic image.The term "plurality of synthetic images" means at least two, preferably at least five, even more preferably at least ten synthetic images. The synthetic images of the plurality of synthetic images differ from one another in that they are generated at least partially based on different partial images of the at least one received image. Partial images differ in the constellation of the image elements of which they are composed; different partial images can have the same image elements, but they have such identical image elements in different constellations with other image elements. In other words, each synthetic image of the plurality of synthetic images is generated based on partial images that have a different constellation of image elements.Partially different input data is fed to the generative model; with the help of the generative model, the majority of synthetic images are generated based on the partially different input data. From the differences between the synthetic images of the plurality of synthetic images, a confidence value can then be determined, which indicates how trustworthy a combined synthetic image is that can be obtained by combining the plurality of synthetic images. Details for determining the confidence value are described further down in the description. Fig. 2 shows an example and schematically the generation of synthetic partial images based on partial images with the help of a generative model and the combining of the synthetic partial images to form a synthetic image. The partial images are the partial images PI shown in Fig. 1. 21 , PI 22 , PI 23 , PI 24 , PI25 and PI 26 . Although Fig.2 may give the impression that the partial images PI 21 , PI 22 , PI 23 , PI 24 , PI 25 and PI 26 from Fig.1 are fed together to the generative model GM, the partial images PI 21 , PI 22 , PI 23 , PI 24 , PI 25 and PI 26 fed to the generative model GM separately (e.g., one after the other). The generative model GM is configured in the example shown in Fig. 2, based on a partial image PI 2m , a synthetic partial image PS 2m where m is an index which in this example runs through the integers 1 to 6. In this example, based on the partial image PI 21 the synthetic partial image PS 21 generated based on the drawing file PI 22 the synthetic partial image PS 22 generated based on the drawing file PI 23the synthetic partial image PS 23 generated based on the drawing file PI 24 the synthetic partial image PS 24 generated based on the drawing file PI 25 the synthetic partial image PS 25 generated and based on the drawing file PI 26 the synthetic partial image PS 26 Each synthetic partial image preferably represents the same part of the examination area as the partial image on the basis of which it was generated. Fig. 2 further shows that the synthetic partial images PS 21 , PS 22 , PS 23 , PS 24 , PS 25 and PS 26 in a further step can be combined to form a synthetic image S2. The synthetic image S2 preferably represents the entire examination area (as does the at least one received image I i in Fig.1). The procedure shown in Fig. 2 using the example of the partial images PS 21 , PS 22 , PS23 , PS 24 , PS 25 and PS 26of copy I2 from Fig. 1 is carried out in an analogous manner for the partial images of the remaining copies I1, I3 and I4. This results in a synthetic image S1, S2, S3 and S4 for each copy I1, I2, I3 and I4. These are shown in Fig. 3. As shown schematically in Fig. 3, the synthetic images S1, S2, S3 and S4 can be combined in a further step to form a combined synthetic image S. The synthetic images S1, S2, S3 and S4 represent the same examination region and preferably comprise the same number of image elements. Each partial region of the examination region is therefore represented four times by the synthetic images S1, S2, S3 and S4. The combination of the synthetic images S1, S2, S3 and S4 to form a combined synthetic image is carried out on the basis of the color values ​​of corresponding image elements.Image elements that represent the same sub-area of ​​the examination zone are referred to in this disclosure as "mutually corresponding image elements" or "corresponding image elements" for short. Corresponding image elements can, for example, be those image elements that have the same coordinates if the respective synthetic image is a raster graphic. The color values ​​are determined for each k-tuple of corresponding image elements of the generated synthetic images. Here, k indicates the number of mutually corresponding image elements. In the example shown in Fig. 3, four image elements of the synthetic images S1, S2, S3, and S4 always correspond to one another. A mean value (e.g., arithmetic mean, geometric mean, root mean square, or another mean value) can be calculated from the color values.The mean of the color value for a tuple of corresponding image elements can be set as the color value of the corresponding image element of the combined synthetic image. In the case of multiple color values ​​(e.g., three color values ​​as in the RGB color model), an average can be calculated for each color channel. The respective average can then be set as the color value of the corresponding color channel of the corresponding image element of the combined synthetic image. It is also conceivable to determine not a mean but a maximum or minimum color value for corresponding image elements, and to assemble the combined synthetic image from the image elements with the respective maximum or minimum color values. Instead of the maxima / minima, other statistical quantities can also be determined and used to generate the combined synthetic image. Fig.Figure 4 shows, by way of example and schematically, the combining of synthetic images into a unified synthetic image. Three synthetic images S1, S2, and S3 are shown. The synthetic images S1, S2, and S3 shown in Figure 4 are 2D raster graphics. Each of the synthetic images S1, S2, and S3 represents the same examination area of ​​an examination object. Each of the three synthetic images S1, S2, and S3 has 10^10 = 100 image elements. The image elements are arranged in a grid; each row and column is assigned a number, so that each image element can be uniquely specified by its coordinates (row value, column value). The synthetic images S1, S2, and S3 are binary images, i.e., each image element is assigned either the color value "white" or the color value "black." The combined synthetic image S is generated by combining the synthetic images S1, S2, and S3.The combination is based on corresponding image elements. Corresponding image elements each represent the same sub-area of ​​the examination area of ​​the object under examination. In the present example, the coordinates of corresponding image elements match. For example, the image element with coordinates (1,1) of the synthetic image S1 corresponds to the image element with coordinates (1,1) of the synthetic image S2 and to the image element with coordinates (1,1) of the synthetic image S3. The image elements with coordinates (1,1) of the synthetic images S1, S2, and S3 form a tuple of corresponding image elements. For each tuple of corresponding image elements, the color values ​​are determined, and the color value of the corresponding image element of the combined synthetic image is determined based on the determined color values.In this example, the synthetic images are combined into the combined synthetic image according to the following rule: the color value of each pixel of the combined synthetic image S corresponds to the color value of the majority of the color values ​​of the corresponding pixels of the synthetic images S1, S2, and S3. For example, the color value for the pixel with coordinates (1,1) of the synthetic image S1 is "white." The color value for the corresponding pixel with coordinates (1,1) of the synthetic image S2 is also "white." The color value for the corresponding pixel with coordinates (1,1) of the synthetic image S3 is also "white." The majority of the corresponding pixels (namely, all pixels) have the color value "white." Accordingly, the color value of the pixel with coordinates (1,1) of the combined synthetic image is also set to "white."For example, the color value for the pixel with coordinates (1, 4) of the synthetic image S1 is "white." The color value for the corresponding pixel with coordinates (1, 4) of the synthetic image S2 is "black." The color value for the corresponding pixel with coordinates (1, 4) of the synthetic image S3 is "white." The majority of the corresponding pixels have the color value "white." Accordingly, the color value of the pixel with coordinates (1, 4) of the combined synthetic image is set to "white." For example, the color value for the pixel with coordinates (7, 10) of the synthetic image S1 is "black." The color value for the corresponding pixel with coordinates (7, 10) of the synthetic image S2 is also "black." The color value for the corresponding pixel with coordinates (7, 10) of the synthetic image S3 is "white." The majority of the corresponding image elements have the color value “black”.Accordingly, the color value of the image element with the coordinates (7, 10) of the combined synthetic image is set to “black.” Other possibilities for combining the individual synthetic images into a combined synthetic image are conceivable. For example, a machine learning model (e.g. an artificial neural network) can be trained to generate the combined synthetic image from the synthetic images of the plurality of synthetic images according to predetermined criteria. If training data is available which, in addition to synthetic images as input data, also includes images that can be used as target data, the machine learning model can be trained in a supervised learning process to combine synthetic images of a plurality of synthetic images into a combined synthetic image. For example, attention mechanisms (see e.g. arXiv:2203) can be used for this purpose.14263) can be used, in which, for example, different weights are assigned to the individual synthetic images of the plurality of synthetic images when combined to form the combined synthetic image. It is also possible that the method for generating a combined synthetic image is not the same for each tuple of corresponding image elements. It is possible that different methods for generating the combined synthetic image are used for different sub-regions of the object under examination. For example, it is possible that a different method for combining the color values ​​of the image elements is used for image elements that represent a specific tissue and / or organ and / or lesion than for image elements that represent a different tissue and / or organ and / or sub-region.Subregions for which different methods for combining corresponding image elements apply can be identified, for example, using segmentation. The term "segmentation" refers to the process of dividing an image into several segments, which are also called image segments, image regions, or image objects. Segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. In a segmented image, the located objects can be separated from the background, visually highlighted (e.g., colored), measured, counted, or otherwise quantified. During segmentation, each image element of an image is assigned a label (e.g., a number) so that image elements with the same label share certain common characteristics, e.g., the same tissue (e.g., bone tissue or fatty tissue, or healthy tissue or diseased tissue (e.g.,Tumor tissue) or muscle tissue and / or the like) and / or represent the same organ. For corresponding image elements with a specific characteristic, a specific calculation rule can then be used to combine their color values ​​to generate a combined synthetic image; for corresponding image elements with a different (specific) characteristic, another (specific) calculation rule can be used to combine their color values. It is also possible for more than one combined synthetic image to be generated, e.g., two or three or four or more than four. For example, a first combined synthetic image can be generated with the respective maxima of the color values ​​and a second combined synthetic image with the respective minima of the color values. A third combined synthetic image can also be generated with mean values ​​of the color values. The combined synthetic image can be output (e.g.,displayed on a monitor and / or output on a printer) and / or stored in a data storage device and / or transmitted to a separate computer system, e.g. via a network connection. On the basis of the output combined synthetic image, a doctor can, for example, make a diagnosis and / or initiate therapy. It is also possible that no combined synthetic image is generated and / or output. It is possible that the analysis of corresponding image elements of synthetic images described below shows that a combined synthetic image has low trustworthiness. For example, a determined trust value that correlates positively with trustworthiness may be lower than a predefined threshold.It may be that the determined confidence value and thus the trustworthiness is so low that no diagnosis should be made and / or therapeutic measures initiated on the basis of the combined synthetic image. In such a case, a combined synthetic image may be worthless or even misleading and thus dangerous. The generation and / or output of such a combined synthetic image with a low trustworthiness can then be dispensed with. A warning can be issued informing a user that a synthetic image with a low trustworthiness has been generated on the basis of the received images using the generative model. In the example shown in Fig. 1, Fig. 2 and Fig. 3, based on a single received image I. iexactly one combined synthetic image S is generated. However, it is possible to generate exactly one combined synthetic image S based on two or more received images. In such a case, sub-images are generated from each received image as described in this disclosure. Sub-images generated from different received images are then fed together to the generative model, and the generative model generates a synthetic sub-image based on the fed sub-images. The sub-images fed to the generative model preferably represent the same sub-area of ​​the examination area. The synthetic sub-images can then be combined to form a plurality of synthetic images, and the plurality of synthetic images can be combined to form a combined synthetic image. This is schematically illustrated in Fig. 5a and Fig. 5b using an example with two received images. In Fig.Figure 5a schematically shows how partial images are generated from each of a plurality of received images and how synthetic partial images are generated based on the generated partial images. Figure 5b schematically shows how the synthetic partial images are combined to form synthetic images and how the synthetic images are combined to form a unified synthetic image. The starting point of the method shown in Figure 5a is two received images, a first image I1 and a second image I2. Each image preferably represents the same examination region of the same examination object. A plurality of copies are generated from each received image. In the example shown in Figure 5a, three copies are generated from each received image; copies I are made from the first image I1. 11 , I 12 and I 13 created, from the second image I2 the copies I 21 , I 22 and I 23It is possible that one of the copies I 11 , I 12 or I 13 the first image I1 itself; it is also possible that one of the copies I 21 , I 22 or I 23 the second image I2 itself. Each copy is divided into sub-images; each sub-image represents a sub-area of ​​the examination area of ​​the object under investigation. From copy I 11 the drawing files PI 111 , PI 112 , PI 113 , PI 114 , PI 115 and PI 116 generated; from copy I 12 the drawing files PI 121 , PI 122 , PI 123 , PI 124 , PI 125 and PI 126 generated; from copy I 13 the drawing files PI 131 , PI 132 , PI 133 , PI 134 , PI 135 and PI 136 generated; from copy I 21 the drawing files PI 211 , PI 212 , PI 213 , PI 214 , PI215 and PI 216 generated; from copy I 22 the drawing files PI 221 , PI 222 , PI 223 , PI 224 , PI 225 and PI 226 generated; from copy I 23 the drawing files PI 231 , PI 232 , PI 233 , PI 234 , PI 235 and PI 236 generated. Sub-areas represented by different drawing elements overlap partially but not completely. For example, drawing element PI 111 shown: the partial image PI 111 partially but not completely overlaps with the PI drawings 121 , PI 131 , PI 132 , PI 133 , PI 134 , PI 135 and PI 136Corresponding partial images originating from different received images are combined into a generative model GM. “Corresponding partial images” are those that represent the same sub-area of ​​the examination area. The generative model GM is shown three times in Fig. 5a for better understanding; however, it is always the same generative model. In the example shown in Fig. 5a, the partial image PI corresponds 111 with the drawing file PI 211 , the drawing part PI 112 with the drawing file PI 212 , the drawing part PI 113 with the drawing file PI 213 , the drawing part PI 114 with the drawing file PI 214 , the drawing part PI 115 with the drawing file PI 215 , the drawing part PI 116 with the drawing file PI 216 , the drawing part PI 121 with the drawing file PI 221 , the drawing part PI 122 with the drawing file PI 222 , the drawing part PI 123with the drawing file PI 223 , the drawing part PI 124 with the drawing file PI 224 , the drawing part PI 125 with the drawing file PI 225 , the drawing part PI 126 with the drawing file PI 226 , the drawing part PI 131 with the drawing file PI 231 , the drawing part PI 132 with the drawing file PI 232 , the drawing part PI 133 with the drawing file PI 233 , the drawing part PI 134 with the drawing file PI 234 , the drawing part PI 135 with the drawing file PI 235 and the drawing part PI 136 with the drawing file PI 236. The corresponding partial images are fed together to the generative model GM, and the generative model generates a synthetic partial image based on the supplied partial images. Each synthetic partial image corresponds to the partial images on the basis of which it was generated, ie, it represents the same sub-area of ​​the examination area as the partial images on the basis of which it was generated. In the example shown in Fig. 5a, the generative model GM generates a synthetic partial image based on the partial images PI. 111 and PI 211 the synthetic partial image PS 11 , based on the drawings PI 112 and PI 212 the synthetic partial image PS 12 , based on the drawings PI 113 and PI 213 the synthetic partial image PS 13 , based on the drawings PI 114 and PI 214 the synthetic partial image PS 14 , based on the drawings PI 115 and PI 215the synthetic partial image PS 15 , based on the drawings PI 116 and PI 216 the synthetic partial image PS 16 , based on the drawings PI 121 and PI 221 the synthetic partial image PS 21 , based on the drawings PI 122 and PI 222 the synthetic partial image PS 22 , based on the drawings PI 123 and PI 223 the synthetic partial image PS 23 , based on the drawings PI 124 and PI 224 the synthetic partial image PS 24 , based on the drawings PI 125 and PI 225 the synthetic partial image PS 25 , based on the drawings PI 126 and PI 226 the synthetic partial image PS 26 , based on the drawings PI 131 and PI 231 the synthetic partial image PS 31 , based on the drawings PI 132 and PI 232 the synthetic partial image PS 32 , based on the drawings PI 133and PI 233 the synthetic partial image PS 33 , based on the drawings PI 134 and PI 234 the synthetic partial image PS 34 , based on the drawings PI 135 and PI 235 the synthetic partial image PS 35 and based on the drawings PI 136 and PI 236 the synthetic partial image PS 36 . The synthetic subimages, which were generated based on the same subdivisions of the copies, are combined into synthetic images in a next step. In the example shown in Fig. 5b, the subimages PS 11 , PS 12 , PS 13 , PS 14 , PS 15 and PS 16 combined to form the synthetic image S1; the partial images PS 21 , PS 22 , PS 23 , PS 24 , PS 25 and PS 26are combined to form the synthetic image S2, and the partial images PS31, PS32, PS33, PS34, PS35, and PS36 are combined to form the synthetic image S3. The synthetic images S1, S2, and S3 are combined in a further step to form a combined synthetic image S. This process corresponds to the process shown in Fig. 3 and Fig. 4. The combined synthetic image S can be output (e.g., displayed on a monitor and / or output to a printer) and / or stored in a data storage device and / or transmitted to a separate computer system, e.g., via a network connection. As already described, more than two images can also be received, and a combined synthetic image can be generated based on these received images. The number of received images can be m, where m is a positive integer. From each of the m images I1, I2, ... I mp copies can be created, where p is a positive integer. In total, this results in m^p copies: I 11 , I 12 , …, I 1p , I 21 , I 22 , … I 2p , … I m1 , I m2 , …, I mp Each copy is divided into subimages. The number of subimages into which each copy is divided can be, for example, q, where q is a positive integer. As described above, the number of subimages into which a copy is divided can, however, be different for individual copies. If it is the same for all copies, then m^p^q subimages result: PI 111 , PI 112 , …, PI 11q , PI 121 , PI 122 , …, PI 12q , …, P 1p1 , P 1p2 , …, P 1pq , …, P 211 , P 212 , …, P 21q , P 221 , P 222 , …, P 22q , …, P 2p1 , P 2p2 , …, P 2pq , …, P m11 , P m12 , …, Pm1q , P m21 , P m22 , …, P m2q , …, P mp1 , P mp2 , …, P mpq . The copies originating from the same received image are divided into sub-images in different ways, so that for each sub-image of a copy, there is at least one other sub-image of another copy with which the sub-image overlaps completely but not entirely. If sub-images partially overlap, the overlapping area represents the same sub-area of ​​the examination area. In other words, the copies I 11 , I 12 , …, I 1p are divided into sub-images in different ways, so that for each sub-image there is at least one other sub-image, which partially but not completely overlaps with the sub-image. Likewise, the copies I 21 , I 22 , … I 2pdivided into sub-images such that for each sub-image there is at least one other sub-image that partially but not completely overlaps with the sub-image. In general, the copies I j1 , I j2 , …, I jp divided into partial images such that for each partial image there is at least one other partial image that partially but not completely overlaps with the partial image, where j is an integer that can assume a value in the range from 1 to m. The way in which copies of a received image are divided into partial images is preferably the same for different received images. This creates corresponding partial images. "Corresponding partial images" are those that represent the same sub-area of ​​the examination area. The copy I 11 is divided into sub-images in the same way as copy I 21 , like the copy I 31 , … and how the copy I m1. Likewise, copy I 12 divided into sub-images in the same way as copy I 22 , like the copy I 32 , … and how the copy I m2 . In general, the copies I rs divided into subimages in the same way, where r is an index that runs through the integers from 1 to m. As r runs through the numbers 1 to m, the value s remains constant; s is an integer that can take a value in the range from 1 to p. Thus, the subimage PI corresponds 111 with the drawing file PI 211 , the partial image PI 311 , …, and with the drawing file PI m11 . Likewise, the partial images PI correspond 112 , PI 212 , PI 312 , …, PI m12 with each other. Likewise, the partial images PI 113 , PI 213 , PI 313 , …, PI m13 with each other. In general, the drawings PI rstwith each other, where r is an index that runs through the integers from 1 to m. If r runs through the numbers 1 to m, the values ​​s and t remain constant; s is an integer that can take on a value in the range from 1 to p, and t is an integer that can take on a value in the range from 1 to q. Based on the partial images, synthetic partial images are generated using the generative model. Corresponding partial images are fed together to the generative model. The generative model generates a synthetic partial image based on each tuple of corresponding partial images. The generative model generates PI based on the corresponding partial images. 111 , PI 211 , PI 311 , …, PI m11 a synthetic partial image PS 11 The generative model generates PI based on the corresponding partial images 112 , PI 212 , PI 312 , …, PI m12a synthetic partial image PS 12 The generative model generates PI based on the corresponding partial images 113 , PI 213 , PI 313 , …, PI m13 a synthetic partial image PS 13 In general, the generative model generates on the basis of the corresponding partial images PI rst a synthetic partial image PS st , where r is an index that ranges from 1 to m. As r ranges from 1 to m, the values ​​s and t remain constant; s is an integer that can take on a value in the range from 1 to p, and t is an integer that can take on a value in the range from 1 to q. The synthetic partial images can then be combined to form synthetic images. In this process, the synthetic partial images PS 11 , P 12 , …, PS 1q combined into a synthetic image S1, the synthetic partial images PS 21 , P 22 , …, PS2q combined into a synthetic image S2, the synthetic partial images PS 31 , P 32 , …, PS 2q to a synthetic image S3, and so on. In general, the synthetic partial images PS vu to a synthetic image S vcombined, where u is an index that ranges from 1 to q. As u ranges from 1 to q, the value v remains constant; v is an integer that can assume a value in the range from 1 to p. All generated synthetic images can be combined into a single unified synthetic image. The determination of the at least one confidence value is described in more detail below. The at least one confidence value can be a value that indicates the extent to which a synthetic image (e.g., the unified synthetic image) can be trusted. The confidence value can correlate positively with the trustworthiness of the synthetic image, i.e., if the confidence value is low, the trustworthiness is also low, and if the confidence value is high, the trustworthiness is also high. However, it is also possible that the trust value correlates negatively with the trustworthiness; i.e., if the trust value is low,the confidence is high and if the confidence value is high, the confidence is low. In the case of a negative correlation, one can also speak of an uncertainty value instead of the confidence value: if the uncertainty value is high, then the synthetic image has a high degree of uncertainty; it is possible that the synthetic image contains one or more artifacts; it is possible that structures and / or morphologies and / or textures in the synthetic image have no equivalent in reality, i.e., that structures and / or morphologies and / or textures in the synthetic image cannot be traced back to real structures and / or real morphologies and / or real textures in the area under investigation. A low uncertainty value, on the other hand, indicates,that the synthetic image has a low degree of uncertainty; features in the synthetic image have a counterpart in reality; the synthetic image can be trusted; a medical diagnosis can be made based on the synthetic image and / or medical therapy can be initiated based on the synthetic image. A confidence value that correlates positively with trustworthiness can, in principle, also be converted into a confidence value that correlates negatively with trustworthiness (an uncertainty value), for example, by taking the reciprocal (the inverse). Conversely, a confidence value that correlates negatively with trustworthiness (an uncertainty value) can also be converted into a confidence value that correlates positively with trustworthiness.be converted. The at least one confidence value can be determined based on corresponding image elements of synthetic images. For each k-tuple of corresponding image elements of the generated synthetic images, the color values ​​are determined. Here, k indicates the number of corresponding image elements. In the example shown in Fig. 4, three image elements of the synthetic images S1, S2, and S3 correspond to each other. The more the color values ​​of corresponding image elements in the synthetic images differ, the greater the difference on which partial images the generation of a synthetic image is based. However, if it makes a large difference on which partial images a synthetic image is based, then a certain uncertainty emanates from the synthetic image; the greater the differences, the lower the confidence. Therefore, the extent to whichin which the color values ​​of corresponding image elements differ, can be used as a measure of trustworthiness / uncertainty: the more the color values ​​of corresponding image elements differ, the lower the trustworthiness, the higher the uncertainty; the less the color values ​​of corresponding image elements differ, the lower the uncertainty, the higher the trustworthiness. The trustworthiness / uncertainty can therefore be determined for each tuple of corresponding image elements of synthetic images of the plurality of synthetic images and then represents the trustworthiness / uncertainty of (i) each synthetic image of the plurality of synthetic images, (ii) the totality of the synthetic images of the plurality of synthetic images, and (iii) the combined synthetic image. In other words, a trust value can be determined for each individual image element of the combined synthetic image.which indicates how much one can trust the color value of the image element. Such a confidence value can, for example, be the spread of the color values ​​of the tuple of corresponding image elements. The spread is defined as the difference between the largest and smallest value of a variable. Thus, for each tuple of corresponding image elements, a maximum color value and a minimum color value can be determined, and the difference between the maximum and minimum color values ​​can be calculated. The result is the spread of the color values ​​of the tuple of corresponding image elements, which can be used as a confidence value. If there is more than one color value (for example, three color values, as in the case of images whose color values ​​are specified according to the RGB color model),For each color channel, a maximum and a minimum color value can be determined, and the difference for each color channel can be calculated. This results in three spreads. For each color channel, the respective spread can be used as a separate confidence value; however, it is also possible to combine the spreads of the color channels into a single value; it is possible to use the maximum spread as the confidence value; it is possible to use an average value (e.g., the arithmetic mean, the geometric mean, the root mean square, or another average value) of the spreads as the confidence value; it is possible to specify the length of the vector that the spreads represent in a three-dimensional space (or a higher-dimensional space when using more than three color channels).as a confidence value; other possibilities are conceivable. A confidence value for a tuple of corresponding image elements can also be the variance and / or the standard deviation of the color values ​​of the corresponding image elements. The variance is defined as the mean square deviation of a variable from its expected value; the standard deviation is defined as the square root of the variance. A confidence value can also be another measure of dispersion, such as the sum of the squares of the deviations, the coefficient of variation, the mean absolute deviation, a quantile distance, an interquantile distance, the mean absolute distance from the median, the median of the absolute deviations, and / or the geometric standard deviation. It is also possible that there is more than one confidence value for a tuple of corresponding image elements. It is also possible,that the method for calculating a confidence value is not the same for each tuple of corresponding image elements. It is possible that different methods for calculating a confidence value are used for different sub-areas of the object under examination. For example, it is possible that a different method for calculating a confidence value is used for image elements representing a specific tissue and / or organ and / or lesion than for image elements representing a different tissue and / or organ and / or sub-area. Sub-areas for which different calculation rules for the confidence values ​​apply can be identified, for example, using segmentation. During segmentation, each image element of an image can be assigned an identifier (e.g., a number) so that image elements with the same identifier have certain common features.e.g., represent the same tissue (e.g., bone tissue, fatty tissue, healthy tissue, diseased tissue (e.g., tumor tissue), muscle tissue, and / or the like) and / or the same organ. For corresponding image elements with a specific characteristic, a specific calculation rule can then be used to calculate a confidence value; for corresponding image elements with a different (specific) characteristic, a different (specific) calculation rule can be used to calculate a confidence value. The confidence values ​​determined for tuples of corresponding image elements can be output (e.g., displayed on a monitor or printed out on a printer).stored in a data storage device and / or transmitted to a separate computer system, e.g., via a network. The trust values ​​determined for tuples of corresponding image elements can also be displayed graphically. In addition to the combined synthetic image, a further representation of the examination area can be output (e.g., displayed on a monitor), indicating the trustworthiness of each image element. Such a representation is also referred to in this description as a trust representation. The trust representation preferably has the same dimension and size as the combined synthetic image; each image element of the combined synthetic image is preferably assigned an image element in the trust representation. Using such a trust representation, a user (e.g., a doctor) can recognize for each individual image element,how much they can trust the color value of the image element. It is possible to display the trust representation completely or partially overlaid with the combined synthetic image and / or with a received image. It is possible to design the overlaid representation so that the user can show and hide it. The user can display the combined synthetic image and / or a received image, for example, layer by layer, as is common for computed tomography, magnetic resonance imaging, and other three- or higher-dimensional representations. For each layer, the user can display the corresponding layer of the trust representation to check whether image elements in the layer that show structures, morphologies, and / or textures in the combined synthetic image are trustworthy or unreliable. In this way, the user can determine how high the risk is that the structures,Morphologies and / or textures are real properties of the examination area or artifacts. Image elements with a low confidence level (with a high degree of uncertainty) can, for example, be displayed brightly and / or with a signal color (e.g., red, orange, or yellow), while image elements with a high confidence level (with a low degree of uncertainty) can be displayed darkly or with a more inconspicuous or calming color (e.g., green or blue). It is also possible to display only those image elements in an overlay for which the confidence value exceeds or falls below a predefined threshold. If the confidence value correlates positively with the confidence level, for example, only those image elements of the confidence representation can be displayed.whose confidence value lies below a predefined threshold; in such a case, a user (e.g., a doctor) is only shown those image elements that they should not trust. It is also possible to determine confidence values ​​for sub-areas (partial images) of the combined synthetic image (e.g., layers within the combined synthetic image) and / or for the entire combined synthetic image. Such confidence values ​​for sub-areas or entire images can be determined based on the confidence values ​​of the image elements from which they are composed. To determine a confidence value for a layer, for example, all confidence values ​​of the image elements that lie in this layer can be taken into account. However, it is also possiblealso consider neighboring image elements (e.g., image elements of the layer above and / or below a layer under consideration). A confidence value for a sub-area or the entire area can be determined, for example, by averaging (e.g., arithmetic mean, geometric mean, root mean square, or another mean). It is also possible to determine the maximum value (e.g., for a confidence value that correlates negatively with trustworthiness) or the minimum value (e.g., for a confidence value that correlates negatively with trustworthiness) of the confidence values ​​of the image elements of a sub-area or the entire area and use this as the confidence value of the sub-area or the entire area. Other options for determining a confidence value for a sub-area or the entire area based on the confidence values ​​of individual image elements include:are conceivable. Such a confidence value for a sub-area or the entire range can also be output (e.g., displayed on a monitor or printed out), stored in a data storage device, and / or transmitted to a separate computer system. It can also be represented graphically (e.g., in color), as described for the individual confidence values. If a confidence value for a sub-area or the entire range that correlates positively with the confidence is lower than a predefined threshold, then it is possible that the corresponding sub-area or the entire range should not be trusted. It is possible that such a sub-area or the corresponding entire range is not output at all (e.g., not displayed at all), as described above, or that it is displayed with a warning that a user should be cautious when interpreting the displayed data.because the displayed data is uncertain. It is also possible for the user of the computer system / computer program of the present disclosure to be given the option, via a user interface, to navigate to sub-areas in the combined synthetic image that have a low trustworthiness. For example, the sub-areas with the lowest trustworthiness can be displayed to the user in a list (e.g., in the form of a list with a number l of sub-areas that have the lowest trust value positively correlated with the trustworthiness, where l is a positive integer). By clicking on a list entry, the user can be shown the corresponding sub-area in the form of a combined synthetic image,a trust representation and / or a received image and / or a section thereof. Fig. 6 shows an example and schematically the determination of the at least one trust value. The determination of the at least one trust value is carried out on the basis of corresponding image elements of the synthetic images S1, S2, and S3 already shown in Fig. 4. A trust value is determined for each tuple of corresponding image elements. In a first step, the color values ​​of all image elements are determined. In the present example, the color "black" is assigned the color value "0" and the color "white" is assigned the color value "1" as is generally customary. The spread of the color values ​​is calculated as the trust value for each tuple of corresponding image elements of the synthetic images S1, S2, and S3. The color value for the image element with the coordinates (1,1) of the synthetic image S1 is, for example, "1" (white). The color value for the corresponding image element with the coordinates (1,1) of the synthetic image S2 is also "1" (white). The color value for the corresponding image element with the coordinates (1,1) of the synthetic image S3 is also "1" (white). The spread for the tuple of corresponding image elements is therefore 1 - 1 = 0. The color value for the image element with the coordinates (1,4) of the synthetic image S1 is, for example, "1" (white). The color value for the corresponding image element with the coordinates (1,4) of the synthetic image S2 is "0" (black). The color value for the corresponding image element with the coordinates (1,4) of the synthetic image S3 is "1" (white). The range for the tuple of corresponding image elements is therefore 1 – 0 = 1. The color value for the image element with the coordinates (7,10) of the synthetic image S1 is, for example, "0" (black). The color value for the corresponding image element with the coordinates (7,10) of the synthetic image S2 is also "0" (black). The color value for the corresponding image element with the coordinates (7,10) of the synthetic image S3 is "1" (white). The spread for the tuple of corresponding image elements is therefore 1 - 0 = 1. The confidence values ​​are listed in table CV. The confidence values ​​determined in this way correlate negatively with the trustworthiness. A trust representation can be determined based on the confidence values. In the example shown in Fig. 6, the color value of each image element in the trust representation SR is set to the corresponding trust value of the tuple of corresponding image elements. For example, the image element with the coordinates (1,1) in the trust representation is assigned the color black, while the image elements with the coordinates (1,4) and (7, 10) are given the color white. Based on the confidence representation SR, a user (e.g., a doctor) can immediately recognize which image elements are safe (black) and which are unsafe (white). The user should have less confidence in areas in which many white image elements appear in the confidence representation SR. Fig. 7 shows an embodiment of the method of the present disclosure in the form of a flowchart. The method (100) comprises the steps: (110) receiving at least one image of an examination region of an examination object, (120) generating a plurality of partial images based on the at least one received image, wherein each partial image represents a partial area of ​​the examination region of the examination object, wherein partial areas represented by different partial images partially but not completely overlap,(130) Generating a plurality of synthetic partial images at least partially based on the generated partial images, (140) Determining color values ​​of corresponding image elements of synthetic partial images, wherein corresponding image elements represent the same partial area of ​​the examination area, (150) Determining a measure of dispersion of the color values ​​of corresponding image elements, (160) Determining a confidence value based on the measure of dispersion, (170) Outputting the confidence value. As described, the generative model described in this description can be a trained machine learning model. Fig. 8 shows an exemplary and schematic method for training such a machine learning model. The training of the generative model GM is carried out using training data TD. The training data TD comprises, for each reference object of a plurality of reference objects,(i) at least one reference image of the reference area of ​​the reference object in at least a first state as input data, and a reference image of the reference object in at least one state deviating from the first state. The term "multiplicity of reference objects" preferably means more than 10, even more preferably more than 100 reference objects. The term "reference" is used here to distinguish the training phase from the phase of using the trained model to generate synthetic images. A "reference image" is an image used to train the model. The "reference object" is an object from which the reference image originates. The reference object, like the object under investigation, is usually an animal or a human.preferably a human. The reference area is a part of the reference object. Preferably, the reference area is the same part as the examination area of ​​the examination object. The term "reference" has no other limiting meaning. Statements made in this description regarding the at least one received image apply analogously to each reference image; statements made in this description regarding the examination object apply analogously to each reference object; statements made in this description regarding the examination area,apply analogously to the reference area. In the example shown in Fig. 8, only one set of training data TD of a reference object is shown; typically, the training data TD comprises a plurality of these data sets for a plurality of reference objects. In the example shown in Fig. 8, the training data TD comprises a first reference image RI1, a second reference image RI2, and a third reference image RI3. The first reference image RI1 represents the reference area of ​​the reference object in a first state; the second reference image RI2 represents the reference area of ​​the reference object in a second state; and the third reference image RI3 represents the reference area of ​​the reference object in a third state. The first state, the second state, and the third state typically differ from one another. For example, the state can represent an amount of contrast agent,which is or has been introduced into the reference area. For example, the state can represent a point in time before and / or after an application of a contrast agent. For example, the first reference image RI1 can represent the reference area without or after application of a first amount of contrast agent, the second reference image RI2 can represent the reference area after application of a second amount of contrast agent, and the third reference image RI3 can represent the reference area after application of a third amount of contrast agent. The first amount can be smaller than the second amount and the second amount can be smaller than the third amount (see, for example, WO2019 / 074938A1, WO2022184297A1). For example, the first reference image RI1 can represent the reference area before or in a first time period after application of a contrast agent,the second reference image RI2 represents the reference area in a second time period after application of the contrast agent, and the third reference image RI3 represents the reference area in a third time period after application of the contrast agent (see, for example, WO2021052896A1, WO2021069338A1). The first reference image RI1 and the second reference image RI2 serve as input data in the example shown in Fig. 8; they are fed to the generative model GM. The generative model GM is configured to generate a synthetic image S based on the first reference image RI1 and the second reference image RI2 and on the basis of model parameters MP. The synthetic image S should be as close as possible to the third reference image RI3. This means that the third reference image RI3 functions as target data (ground truth) in the example shown in Fig. 8. The synthetic image S generated by the generative model GM is compared with the third reference image RI3. An error function LF is used,to quantify deviations between the synthetic image S and the third reference image RI3. For each pair of a synthetic image and a third reference image, an error value can be calculated using the error function LF. In an optimization procedure, the error value and thus the deviations between the synthetic image S generated by the generative model and the third reference image RI3 can be reduced by modifying model parameters MP. The process is repeated for a large number of reference objects. If the error values ​​reach a predefined minimum or if the error values ​​cannot be further reduced by modifying model parameters, training can be terminated. The trained model can be saved,transmitted to a separate computer system and / or used to generate synthetic images for (new) objects (examination objects). Fig. 9 shows an exemplary and schematic representation of a computer system according to the present disclosure. A "computer system" is a system for electronic data processing that processes data using programmable computing instructions. Such a system typically comprises a "computer," the unit that includes a processor for performing logical operations, as well as peripherals. In computer technology, "peripherals" refer to all devices connected to the computer and used to control the computer and / or as input and output devices. Examples include monitors (screens), printers, scanners, mice, keyboards, drives, cameras, microphones,Speakers, etc. Internal connections and expansion cards are also considered peripherals in computer technology. The computer system (10) shown in Fig. 9 comprises a receiving unit (11), a control and computing unit (12), and an output unit (13). The control and computing unit (12) serves to control the computer system (10), coordinate the data flows between the units of the computer system (10), and perform calculations. The control and computing unit (12) is configured to: - cause the receiving unit (11) to receive at least one image of an examination region of an examination subject, - generate a plurality of partial images based on the at least one received image, wherein each partial image represents a partial area of ​​the examination region of the examination subject, wherein partial areas represented by different partial images partially but not completely overlap,- to generate a plurality of synthetic partial images at least partially based on the generated partial images, - to determine color values ​​of corresponding image elements of synthetic partial images, wherein corresponding image elements represent the same partial area of ​​the examination area, - to determine a degree of dispersion of the color values ​​of corresponding image elements, - to determine a confidence value based on the degree of dispersion, - to cause the output unit to output the confidence value. Fig. 10 shows an exemplary and schematic illustration of a further embodiment of the computer system. The computer system (10) comprises a processing unit (21) connected to a memory (22). The processing unit (21) and the memory (22) form a control and computing unit.as shown in Fig. 9. The processing unit (21) may comprise one or more processors alone or in combination with one or more memories. The processing unit (21) may be conventional computer hardware capable of processing information such as digital images, computer programs, and / or other digital information. The processing unit (21) typically consists of an arrangement of electronic circuits, some of which may be implemented as an integrated circuit or as multiple interconnected integrated circuits (an integrated circuit is sometimes referred to as a "chip"). The processing unit (21) may be configured to execute computer programs,which may be stored in a working memory of the processing unit (21) or in the memory (22) of the same or another computer system. The memory (22) may be conventional computer hardware capable of storing information such as digital images (e.g. representations of the examination area), data, computer programs, and / or other digital information either temporarily and / or permanently. The memory (22) may comprise volatile and / or non-volatile memory and may be permanently installed or removable. Examples of suitable memories are RAM (Random Access Memory), ROM (Read-Only Memory), a hard disk, a flash memory, a removable computer diskette, an optical disc, a magnetic tape, or a combination of the above. Optical discs may include compact discs with read-only memory (CD-ROM), compact discs with read / write function (CD-R / W), DVDs,Blu-ray discs and the like. In addition to the memory (22), the processing unit (21) can also be connected to one or more interfaces (11, 12, 31, 32, 33) to display, transmit and / or receive information. The interfaces can comprise one or more communication interfaces (11, 32, 33) and / or one or more user interfaces (12, 31). The one or more communication interfaces can be configured to send and / or receive information, e.g., to and / or from an MRI scanner, a CT scanner, an ultrasound camera, other computer systems, networks, data storage devices, or the like. The one or more communication interfaces can be configuredthat they transmit and / or receive information via physical (wired) and / or wireless communication connections. The one or more communication interfaces may include one or more interfaces for connecting to a network, e.g., using technologies such as cellular phone, Wi-Fi, satellite, cable, DSL, fiber optic, and / or the like. In some examples, the one or more communication interfaces may include one or more short-range communication interfaces configured to connect devices using short-range communication technologies such as NFC, RFID, Bluetooth, Bluetooth LE, ZigBee, infrared (e.g., IrDA), or the like. The user interfaces may include a display (31). A display (31) may be configured to display information to a user. Suitable examples include a liquid crystal display (LCD), a light-emitting diode display (LED),a plasma display panel (PDP) or similar. The user input interface(s) (11, 12) may be wired or wireless and may be configured to receive information from a user into the computer system (1), e.g., for processing, storage, and / or display. Suitable examples of user input interfaces include a microphone, an image or video capture device (e.g., a camera), a keyboard or keypad, a joystick, a touch-sensitive surface (separate from or integrated with a touchscreen), or similar. In some examples, the user interfaces may include automatic identification and data capture (AIDC) technology for machine-readable information. This may include barcodes, radio frequency identification (RFID), magnetic stripes, optical character recognition (OCR),Integrated circuit cards (ICCs) and the like. The user interfaces may further include one or more interfaces for communication with peripheral devices such as printers and the like. One or more computer programs (40) may be stored in the memory (22) and executed by the processing unit (21), which is thereby programmed to perform the functions described in this description. The retrieval, loading, and execution of instructions of the computer program (40) may occur sequentially, such that one command at a time is retrieved, loaded, and executed. However, the retrieval, loading, and / or execution may also occur in parallel. The computer system of the present disclosure may be embodied as a laptop, notebook, netbook, and / or tablet PC; it may also be a component of an MRI scanner, a CT scanner, or an ultrasound diagnostic device.

Claims

Claims 1. Computer-implemented method comprising: - receiving at least one image (I i ) of an examination area of ​​an object under examination, - generating a large number of partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26 ) based on the at least one received image (I i ), each partial image representing a partial area of ​​the examination area of ​​the object under examination, whereby partial areas represented by different partial images partially but not completely overlap, - generating a plurality of synthetic partial images (PS 21 , PS 22 , PS 23 , PS 24 , PS 25 , PS 26 ) at least partially based on the generated partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26), - Determination of color values ​​of corresponding image elements of synthetic partial images (PS 21 , PS 22 , PS 23 , PS 24 , PS 25 , PS 26 ), wherein corresponding image elements represent the same sub-area of ​​the examination area, - determining a measure of dispersion of the color values ​​of corresponding image elements, - determining a confidence value based on the measure of dispersion, - outputting the confidence value.

2. Method according to claim 1, wherein for each tuple of corresponding image elements of the synthetic partial images (PS 21 , PS 22 , PS 23 , PS 24 , PS 25 , PS 26 ) a dispersion measure of the color values ​​is determined, whereby for each tuple of corresponding image elements of the synthetic partial images (PS 21 , PS 22 , PS 23 , PS 24 , PS 25 , PS 26) a confidence value is determined on the basis of the respective dispersion measure.

3. Method according to one of claims 1 or 2, wherein the dispersion measure is a dispersion range, a standard deviation, a variance, a sum of squared deviations, a coefficient of variation, a mean absolute deviation, a quantile distance, an interquantile distance, a mean absolute distance from a median, a median of absolute deviations and / or a geometric standard deviation of the color values ​​of corresponding image elements or is derived therefrom.

4. Method according to one of claims 1 to 3, wherein for each partial image there is at least one other partial image with at least one common image element and at least one different image element.

5. Method according to one of claims 1 to 4, wherein for each image element of the at least one received image (I i) there are a plurality of partial images which also comprise this picture element, wherein each partial image of the plurality of partial images differs from every other partial image of the plurality of partial images by at least one other picture element.

6. The method according to one of claims 1 to 5, wherein the generation of the plurality of partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26 ) comprises: - generating a plurality of copies (I1, I2, I3, I4) of the at least one received image (I i ), - Dividing each copy (I1, I2, I3, I4) into partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26 ), wherein all partial images of all copies differ from each other.

7. Method according to one of claims 1 to 6, wherein the generation of the plurality of partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26) comprises: - generating a plurality of copies (I1, I2, I3, I4) of the at least one received image (I i ), - Dividing each copy (I1, I2, I3, I4) into partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26 ) by cuts, wherein the cuts run differently through the copies for different copies.

8. Method according to one of claims 1 to 7, further comprising: - combining synthetic partial images (PS 21 , PS 22 , PS 23 , PS 24 , PS 25 , PS 26) to synthetic images (S1, S2, S3, S4), - generating a combined synthetic image (S) based on the synthetic images (S1, S2, S3, S4), wherein generating the combined synthetic image (S) comprises combining color values ​​of corresponding image elements of the synthetic images (S1, S2, S3, S4).

9. The method according to claim 8, wherein generating the combined synthetic image (S) comprises: - for each tuple of corresponding image elements of the synthetic images (S1, S2, S3, S4): determining an average color value by averaging the color values ​​of the corresponding image elements and setting the average color value as the color value of the corresponding image element of the combined synthetic image (S).

10. The method according to any one of claims 1 to 9, further comprising: - outputting the combined synthetic image (S) and / or transmitting the combined synthetic image (S) to a separate computer system. 11.Method according to one of claims 1 to 10, further comprising: - generating a confidence representation (SR), wherein the confidence representation (SR) comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination region, wherein each image element has a color value, wherein the color value correlates with the confidence value of the respective tuple of corresponding image elements of the synthetic images, - outputting the confidence representation (SR), preferably in a superimposed representation with the combined synthetic image (S), and / or transmitting the confidence representation (SR) to a separate computer system.

12. Method according to one of claims 8 to 11, further comprising: - determining a confidence value for one or more partial areas of the combined synthetic image (S), and / or for the entire combined synthetic image (S), - outputting the confidence value.

13. The method according to any one of claims 1 to 12, wherein the examination subject is a human or animal, preferably a mammal, most preferably a human.

14. The method according to any one of claims 1 to 13, wherein the at least one received image (I i ) is at least one medical image and each synthetic image (S1, S2, S3, S4) and / or the combined synthetic image (S) is a synthetic medical image.

15. The method according to one of claims 1 to 14, wherein the at least one received image (I i) comprises a first radiological image and a second radiological image, wherein the first radiological image represents the examination region of the examination object without contrast agent or after application of a first amount of a contrast agent, and the second radiological image represents the examination region of the examination object after application of a second amount of the contrast agent, wherein each synthetic image (S1, S2, S3, S4) and / or the combined synthetic image (S) is a synthetic radiological image, wherein each synthetic image (S1, S2, S3, S4) and / or the combined synthetic image (S) represents the examination region of the examination object after application of a third amount of the contrast agent, wherein the second amount is different from, preferably greater than, the first amount, and the third amount is different from, preferably greater than, the first amount and the second amount. 16.Method according to one of claims 1 to 15, wherein the at least one received image (I. i) comprises a first radiological image and a second radiological image, wherein the first radiological image represents the examination region of the examination object in a first time period before or after application of a contrast agent, and the second radiological image represents the examination region of the examination object in a second time period after application of the contrast agent, wherein each synthetic image (S1, S2, S3, S4) and / or the combined synthetic image (S) is a synthetic radiological image, wherein each synthetic image (S1, S2, S3, S4) and / or the combined synthetic image (S) represents the examination region of the examination object in a third time period after application of the contrast agent, wherein the second time period preferably follows the first time period and the third time period preferably follows the second time period. 17.Computer system (10) comprising - a receiving unit (11), - a control and computing unit (12) and - an output unit (13), wherein the control and computing unit (12) is configured - to cause the receiving unit (11) to output at least one image (I. i ) of an examination area of ​​an object under investigation, - a variety of partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26 ) based on the at least one received image (I i ), each partial image representing a partial area of ​​the examination area of ​​the object under examination, whereby partial areas represented by different partial images partially but not completely overlap, - a plurality of synthetic partial images (PS 21 , PS 22 , PS 23 , PS 24 , PS 25 , PS 26 ) at least partially based on the generated partial images (PI21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26 ), - to determine color values ​​of corresponding image elements of synthetic partial images, wherein corresponding image elements represent the same sub-area of ​​the examination area, - to determine a degree of dispersion of the color values ​​of corresponding image elements, - to determine a confidence value based on the degree of dispersion, - to cause the output unit (13) to output the confidence value.

18. A computer-readable storage medium comprising a computer program (40) which, when loaded into a working memory (22) of a computer system (10), causes the computer system (10) to carry out the following steps: - receiving at least one image (I i ) of an examination area of ​​an object under examination, - generating a large number of partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26) based on the at least one received image (I i ), each partial image representing a partial area of ​​the examination area of ​​the object under examination, whereby partial areas represented by different partial images partially but not completely overlap, - generating a plurality of synthetic partial images (PS 21 , PS 22 , PS 23 , PS 24 , PS 25 , PS 26 ) at least partially based on the generated partial images (PI 21 , PI 22 , PI 23 , PI 24 , PI 25 , PI 26 ), - determining color values ​​of corresponding image elements of synthetic partial images, whereby corresponding image elements represent the same sub-area of ​​the examination area, - determining a dispersion measure of the color values ​​of corresponding image elements, - determining a confidence value based on the dispersion measure, - outputting the confidence value.

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