Generating synthetic radiological images

Abstract

The present disclosure relates to the technical field of radiology, in particular to the support of radiologists in radiological examinations using methods of artificial intelligence. The present disclosure is concerned with training a machine learning model and using the trained model to predict representations of an examination region following applications of different amounts of a contrast agent.

Description

[0001] BYP250119 FC

[0002] Generating synthetic radiological images TECHNICAL AREA

[0003] This disclosure relates to the technical field of radiology, in particular the support of radiologists in radiological examinations using artificial intelligence methods. The present disclosure deals with training a machine learning model and using the trained model to predict representations of an examination area after the application of different amounts of a contrast agent. INTRODUCTION WO2019 / 074938A1 discloses a method for reducing the amount of contrast agent used in the production of radiological images using an artificial neural network.

[0004] In a first step, a training dataset is generated. The training dataset includes, for each person in a large number of individuals, i) a native radiological image (zero-contrast image), ii) a radiological image after the administration of a small amount of contrast agent (low-contrast image), and iii) a radiological image after the administration of a standard amount of contrast agent (full-contrast image).

[0005] In a second step, an artificial neural network is trained to predict, for each individual in the training dataset, an artificial radiological image showing the image area after the application of the standard amount of contrast agent, based on the native image and the image after administration of a small amount of contrast agent. The measured radiological image after the application of a standard amount of contrast agent serves as the reference (ground truth) during training.

[0006] In a third step, the trained artificial neural network can be used to generate a synthetic radiological image for a new person based on a native image and a radiological image after the application of a small amount of contrast agent, which shows the recorded area as it would look if a standard amount of contrast agent had been applied.

[0007] It has been shown that synthetic radiological images generated in this way can exhibit artifacts. Fine structures may not be accurately represented, or structures may appear in areas of the synthetic radiological image that are not present in the represented tissue (see, e.g., K. Schwarz et al., 'On the Frequency Bias of Generative Models', https: / / doi.org / 10.48550 / arXiv.2111.02447).

[0008] SUMMARY

[0009] This and other problems are addressed in the present revelation.

[0010] The first subject of the present disclosure comprises a computer-implemented method:

[0011] Providing a first representation, wherein the first representation represents an investigation area of ​​an object under investigation after application of a first amount of a contrast agent in the frequency space,

[0012] Providing a second representation, wherein the second representation represents the investigation area of ​​the object under investigation after application of a second amount of the contrast agent in the frequency space,

[0013] Generating a first difference representation based on the first representation and the second representation, where the first difference representation represents a difference between the first representation and the second representation; inputting the first difference representation into a trained machine learning model; receiving a second difference representation as output from the trained machine learning model.

[0014] Generating a third representation, wherein generating the third representation involves adding the second difference representation to the second representation or to the first representation,

[0015] Transforming the third representation into a third representation in spatial space, outputting and / or storing the third representation in spatial space and / or transmitting the third representation in spatial space to a separate computer system,

[0016] wherein the trained machine learning model was trained in a supervised learning process based on training data, wherein the training data for each reference object of a plurality of reference objects comprise (i) a first reference difference representation as input data and (ii) a second reference difference representation as target data, wherein the first reference difference representation represents a difference between a first reference representation and a second reference representation, and the second reference difference representation represents a difference between the second reference representation and a third reference representation or between the first reference representation and the third reference representation, wherein the first reference representation represents a reference range of the reference object after application of the first amount of the contrast agent in the frequency domain,where the second reference representation represents the reference range of the reference object after application of the second amount of contrast agent in the frequency domain, and the third reference representation represents the reference range of the reference object after application of a third amount of contrast agent in the frequency domain.

[0017] where the first set is different from the second set and the third set, and the second set is different from the third set.

[0018] Another subject of the present disclosure is a computer system comprising

[0019] a receiving unit,

[0020] a control and computing unit and

[0021] an output unit,

[0022] where the control and computing unit is configured,

[0023] to provide an initial representation and / or to cause the receiving unit to receive the initial representation, wherein the initial representation represents an area of ​​investigation of an object of investigation after application of an initial quantity of a contrast agent in the frequency space,

[0024] to provide a second representation and / or to cause the receiving unit to receive the second representation, wherein the second representation represents the area under investigation of the object after application of a second quantity of the contrast agent in the frequency domain,

[0025] to generate a first difference representation based on the first representation and the second representation, where the first difference representation represents a difference between the first representation and the second representation,

[0026] to input the first difference representation into a trained machine learning model, and to receive a second difference representation as output from the machine learning model.

[0027] to generate a third representation, wherein generating the third representation involves adding the second difference representation to the second representation or to the first representation,

[0028] to transform the third representation into a third spatial representation, to cause the output unit to output and / or store the third spatial representation and / or to transmit it to a separate computer system,

[0029] wherein the trained machine learning model was trained in a supervised learning process based on training data, wherein the training data for each reference object of a plurality of reference objects comprise (i) a first reference difference representation as input data and (ii) a second reference difference representation as target data, wherein the first reference difference representation represents a difference between a first reference representation and a second reference representation, and the second reference difference representation represents a difference between the second reference representation and a third reference representation or between the first reference representation and the third reference representation, wherein the first reference representation represents a reference range of the reference object after application of the first amount of the contrast agent in the frequency domain,where the second reference representation represents the reference range of the reference object after application of the second amount of contrast agent in the frequency domain, and the third reference representation represents the reference range of the reference object after application of a third amount of contrast agent in the frequency domain.

[0030] where the first set is different from the second set and the third set, and the second set is different from the third set.

[0031] Another subject of the present disclosure is a computer program product comprising a computer program that can be loaded into the main memory of a computer system and causes the computer system to perform the following steps:

[0032] Providing a first representation, wherein the first representation represents an investigation area of ​​an object under investigation after application of a first amount of a contrast agent in the frequency space,

[0033] Providing a second representation, wherein the second representation represents the investigation area of ​​the object under investigation after application of a second amount of the contrast agent in the frequency space,

[0034] Generating a first difference representation based on the first representation and the second representation, where the first difference representation represents a difference between the first representation and the second representation.

[0035] Inputting the first difference representation into a trained machine learning model, receiving a second difference representation as output from the machine learning model,

[0036] Generating a third representation, wherein generating the third representation involves adding the second difference representation to the second representation or to the first representation,

[0037] Transforming the third representation into a third representation in spatial space, outputting and / or storing the third representation in spatial space and / or transmitting the third representation in spatial space to a separate computer system,

[0038] wherein the trained machine learning model was trained in a supervised learning process based on training data, wherein the training data for each reference object of a plurality of reference objects comprise (i) a first reference difference representation as input data and (ii) a second reference difference representation as target data, wherein the first reference difference representation represents a difference between a first reference representation and a second reference representation, and the second reference difference representation represents a difference between the second reference representation and a third reference representation or between the first reference representation and the third reference representation, wherein the first reference representation represents a reference range of the reference object after application of the first amount of the contrast agent in the frequency domain.where the second reference representation represents the reference range of the reference object after application of the second amount of the contrast agent in the frequency domain, and the third reference representation represents the reference range of the reference object after application of a third amount of the contrast agent in the frequency domain.

[0039] where the first set is different from the second set and the third set, and the second set is different from the third set.

[0040] Another subject of the present disclosure is a kit comprising a contrast agent and a computer program that can be loaded into the working memory of a computer system and causes the computer system to perform the following steps:

[0041] Providing a first representation, wherein the first representation represents an investigation area of ​​an object under investigation after application of a first amount of a contrast agent in the frequency space,

[0042] Providing a second representation, wherein the second representation represents the investigation area of ​​the object under investigation after application of a second amount of the contrast agent in the frequency space,

[0043] Generating a first difference representation based on the first representation and the second representation, where the first difference representation represents a difference between the first representation and the second representation.

[0044] Inputting the first difference representation into a trained machine learning model, receiving a second difference representation as output from the machine learning model,

[0045] Generating a third representation, wherein generating the third representation involves adding the second difference representation to the second representation or to the first representation,

[0046] Transforming the third representation into a third representation in spatial space, outputting and / or storing the third representation in spatial space and / or transmitting the third representation in spatial space to a separate computer system,

[0047] wherein the trained machine learning model was trained in a supervised learning process based on training data, wherein the training data for each reference object of a plurality of reference objects comprise (i) a first reference difference representation as input data and (ii) a second reference difference representation as target data, wherein the first reference difference representation represents a difference between a first reference representation and a second reference representation, and the second reference difference representation represents a difference between the second reference representation and a third reference representation or between the first reference representation and the third reference representation, wherein the first reference representation represents a reference range of the reference object after application of the first amount of the contrast agent in the frequency domain,where the second reference representation represents the reference range of the reference object after application of the second amount of contrast agent in the frequency domain, and the third reference representation represents the reference range of the reference object after application of a third amount of contrast agent in the frequency domain.

[0048] where the first set is different from the second set and the third set, and the second set is different from the third set.

[0049] Further items and embodiments can be found in the following description, the patent claims and the drawings.

[0050] BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Fig. 1 shows an implementation of the computer-implemented method of the present disclosure in the form of a flowchart.

[0052] Fig. 2 shows, by way of example and schematically, a method for training the machine learning model of the present disclosure.

[0053] Fig. 3 shows, by way of example and schematically, the use of the trained machine learning model for prediction.

[0054] Fig. 4 shows, by way of example and schematically, a computer system according to the present disclosure, which can be used to train the machine learning model and / or to use the trained machine learning model for prediction.

[0055] Fig. 5 shows, by way of example and schematically, another embodiment of the computer system of the present disclosure.

[0056] DETAILED DESCRIPTION

[0057] The subject matter of this disclosure is explained in more detail below, without distinguishing between the subject matter (method, computer system, computer program, kit). Rather, the following explanations are intended to apply analogously to all subject matter (method, computer system, computer program, kit), regardless of the context in which they are made. If steps are specified in a sequence in this description or in the claims, this does not mean that the present disclosure is limited to the specified sequence. Rather, it is conceivable that the steps can also be carried out in a different sequence or even in parallel with one another, unless, for example, one step builds upon another, which requires that the building step be carried out subsequently (this will become clear in the specific case). The sequences mentioned are thus exemplary embodiments of the present disclosure.

[0058] The subject matter of this disclosure is further explained in several places with reference to drawings. These drawings depict specific embodiments with specific features and combinations of features, primarily for illustrative purposes; the drawings should not be interpreted as limiting the present disclosure 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 be generally applicable, that is, transferable to other embodiments and not limited to the embodiments shown. With the aid of this disclosure, representations of an area of ​​investigation of an object of investigation can be predicted. Such a predicted representation is also referred to in this disclosure as a synthetic representation.

[0059] The term "synthetic" means that the representation is not the (direct) result of a physical measurement on a real-world object (e.g., a person), but rather that the synthetic representation is generated using a machine learning model. A synonym for "synthetic" is "artificial." However, the synthetic representation is based on one or more measured representations; that is, the machine learning model can be configured and trained to generate a synthetic representation based on one or more measured representations (and / or optionally, additional data).

[0060] The “object of study” is usually a living being, for example a mammal, for example a human.

[0061] The "area of ​​investigation" is a part of the object of investigation, for example an organ or part of an organ or several organs or another part of the object of investigation.

[0062] The area of ​​examination can be, for example, a liver, a kidney, a heart, a lung, a brain, a stomach, a bladder, a prostate gland, an intestine, a female breast, a uterus, a thyroid gland, a pancreas, a spleen or part thereof, or any other part of the body of a mammal (e.g., a human).

[0063] In one embodiment, the area under investigation comprises a liver or part of a liver, or the area under investigation is a liver or part of a liver of a mammal, for example a human.

[0064] In another embodiment, the area of ​​investigation comprises a brain or part of a brain, or the area of ​​investigation is a brain or part of a brain of a mammal, for example a human.

[0065] In another embodiment, the examination area comprises a heart or part of a heart, or the examination area is a heart or part of a heart of a mammal, for example a human.

[0066] In another embodiment, the examination area comprises a thorax or part of a thorax, or the examination area is a thorax or part of a thorax of a mammal, for example a human.

[0067] In another embodiment, the examination area comprises a stomach or part of a stomach, or the examination area is a stomach or part of a stomach of a mammal, for example a human.

[0068] In another embodiment, the area under investigation comprises a thyroid gland or part of a thyroid gland, or the area under investigation is a thyroid gland or part of a thyroid gland of a mammal, for example a human.

[0069] In another embodiment, the examination area comprises a uterus or part of a uterus, or the examination area is a uterus or part of a uterus of a female mammal, for example, a human female. In another embodiment, the examination area comprises a pancreas or part of a pancreas, or the examination area is a pancreas or part of a pancreas of a mammal, for example, a human female.

[0070] In another embodiment, the examination area comprises a kidney or part of a kidney, or the examination area is a kidney or part of a kidney of a mammal, for example a human.

[0071] In another embodiment, the examination area comprises one or both lung wings or part of a lung wing of a mammal, for example a human.

[0072] In another embodiment, the examination area comprises a breast or part of a breast, or the examination area is a breast or part of a breast of a female mammal, for example a female human.

[0073] In another embodiment, the examination area comprises a prostate or part of a prostate, or the examination area is a prostate or part of a prostate of a male mammal, for example a male human.

[0074] The field of view (FOV), also known as the examination area, is a volume that is depicted in radiological images. The field of view is typically defined by a radiologist, for example, on a localizer image. Alternatively, or additionally, the field of view can also be defined automatically, for example, based on a selected protocol.

[0075] A “representation of the area under investigation” is usually the result of a radiological examination.

[0076] Radiology is the branch of medicine that deals with the application of primarily 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 rays 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 part of radiology, even though these techniques do not use ionizing radiation. The term "radiology" as used in this disclosure therefore includes, in particular, the following examination methods: computed tomography (CT), magnetic resonance imaging (MRI), sonography, and positron emission tomography (PET).

[0077] In one embodiment of the present disclosure, the radiological examination is a magnetic resonance imaging examination.

[0078] In another embodiment, the radiological examination is a computed tomography examination.

[0079] In one embodiment, the radiological examination is an ultrasound examination.

[0080] In one embodiment, the radiological examination is a PET scan.

[0081] Contrast agents are frequently used in radiological examinations to enhance contrast. "Contrast agents" are substances or mixtures of substances that improve the visualization of bodily structures and functions during radiological examinations. In computed tomography (CT), iodine-containing solutions are most commonly 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) are typically used as contrast agents. In sonography, fluids containing gas-filled microbubbles are usually administered intravenously. In positron emission tomography (PET), radiotracers are commonly used. Radiotracers contain a radioactive isotope and emit positrons during their decay.These positrons interact with electrons in the body, leading to the emission of gamma rays that can be detected by the PET scanner.

[0082] MRI contrast agents exert their effect in an MRI examination by altering the relaxation times of the structures that absorb the contrast agent. Two groups of substances can be distinguished: para- and superparamagnetic agents. Both groups possess unpaired electrons that induce a magnetic field around the individual atoms or molecules. Superparamagnetic agents lead to a predominantly T2 shortening, while paramagnetic contrast agents primarily lead to a T1 shortening. The effect of these contrast agents is indirect, as 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 superparamagnetic iron oxide nanoparticles (SPIO). Examples of paramagnetic contrast agents include gadolinium chelates such as gadopentetate dimeglumine (trade name: Magnevist®, etc.).), Gadoteric acid (Dotarem®, Dotagita®, Cyclolux®), Gadodiamide (Omniscan®), Gadoteridol (ProHance®), Gadobutrol (Gadovist®) and Gadoxetic acid (Primovist® / Eovist®).

[0083] A representation of the area of ​​investigation within the meaning of the present disclosure may be an MRI scan, a CT scan, an ultrasound image, a PET scan or the like, or the representation of the area of ​​investigation may be generated from one or more MRI scans, CT scans, ultrasound images, PET scans or the like.

[0084] A representation of the investigation area within the meaning of the present disclosure can be a representation in spatial space (image space) or a representation in frequency space.

[0085] In a spatial representation, also referred to in this description as a spatial representation, the area under investigation is typically represented by a multitude of image elements (pixels or voxels), which may be arranged in a grid, for example, with each image element representing a portion of the area under investigation. A widely used format in radiology for storing and processing spatial representations is the DICOM format. DICOM (Digital Imaging and Communications in Medicine) is an open standard for storing and exchanging information in medical image data management.

[0086] In a frequency-space representation, also referred to in this description as a frequency-space diagram or frequency-space representation, the area under investigation is represented by a superposition of fundamental oscillations. For example, the area under investigation can be represented by a sum of sine and cosine functions with different amplitudes, frequencies, and phases. The amplitudes and phases can be plotted as a function of the frequencies, for example, in a two- or three-dimensional representation. Typically, the lowest frequency (origin) is placed at the center. The further one moves away from this center, the higher the frequencies. Each frequency can be assigned an amplitude, with which the frequency is represented in the frequency-space diagram, and a phase, which indicates how far the respective oscillation is shifted relative to a sine or cosine oscillation.A representation in spatial space can be transformed into a representation in frequency space, for example, by a Fourier transform. Conversely, a representation in frequency space can be transformed into a representation in spatial space, for example, by an inverse Fourier transform.

[0087] Details about spatial representations and frequency space representations and their respective conversions are described in numerous publications, see e.g.: https: / / see.stanford.edu / materials / lsoftaee261 / book-fall-07.pdf.

[0088] In a first step, a first and a second representation are provided. The first and second representations can be transmitted, for example, by a CT scanner, an MRI scanner, an ultrasound device, or a PET scanner and received by the computer system of this disclosure. The first and second representations can, for example, be read from a data storage device. The data storage device can be a component of the computer system of this disclosure or connected to it, for example, via a network. The first and second representations can be generated based on corresponding spatial representations.

[0089] The first representation represents the area of ​​investigation of a test object after the application of an initial amount of contrast agent in the frequency domain. The initial amount of contrast agent can also be zero, meaning the first representation can represent the area of ​​investigation of the test object without any contrast agent in the frequency domain.

[0090] The second representation represents the same investigation area of ​​the same object after application of a second amount of contrast agent in the frequency domain. The first and second amounts of contrast agent differ from each other.

[0091] In one embodiment, the second quantity is larger than the first quantity (where the first quantity can also be zero, as described). For example, the first quantity can be 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, less than 10%, or any other percentage of the second quantity.

[0092] The phrase "after application of a second amount of contrast agent" should not be interpreted as meaning that the first and second amounts are added together in the area under investigation. Rather, the phrase "the second representation represents the area under investigation after application of a second amount" should mean: "the second representation represents the area under investigation with a second amount" or "the representation represents the area under investigation encompassing a second amount."

[0093] The contrast agent can be administered, for example, as a bolus via intravenous administration.

[0094] In one embodiment, the first quantity of contrast agent is smaller than the standard quantity. The second quantity of contrast agent can be the same as the standard quantity; however, it can also be smaller or larger.

[0095] The standard dose of a contrast agent is usually the dose recommended by the manufacturer and / or distributor of the contrast agent and / or the dose approved by a regulatory authority and / or the dose listed in the package leaflet for the contrast agent. For example, the standard dose of Primovist® is 0.025 mmol Gd-EOB-DTPA disodium per kg body weight.

[0096] The first and second representations can be the result of a radiological examination procedure performed on the object under investigation. It is also possible that the first and second representations are derived from corresponding spatial representations, which in turn can be the result of a radiological examination procedure performed on the object under investigation.

[0097] In one embodiment, the radiological examination method is a magnetic resonance imaging examination method and the contrast agent is an MRI contrast agent.

[0098] In one embodiment, the radiological examination method is a computed tomography examination method and the contrast agent is a CT contrast agent.

[0099] In one implementation form, the radiological examination procedure is a computed tomography examination procedure and the contrast agent is an MRI contrast agent.

[0100] The MRI contrast agent may be an extracellular contrast agent. Extracellular contrast agents are low-molecular-weight, water-soluble compounds that, after intravenous administration, distribute themselves in the blood vessels and interstitial space. They are excreted via the kidneys after a relatively short period of circulation in the bloodstream. Examples of extracellular MRI contrast agents include the gadolinium chelates gadobutrol (Gadovist®), gadoteridol (Prohance®), gadoteric acid (Dotarem®), gadopentetic acid (Magnevist®), and gadodiamide (Omnican®).

[0101] The MRI contrast agent may be an intracellular contrast agent. Intracellular contrast agents are partially absorbed into the cells of tissues and subsequently excreted. Intracellular MRI contrast agents based on gadoxetic acid, for example, are characterized by their partial, specific uptake by boar cells (hepatocytes), accumulation in functional tissue (parenchyma), and enhancement of contrast in healthy boar tissue before being excreted in the feces via bile. Examples of such gadoxetic acid-based contrast agents are described in US 6,039,931A; they are commercially available, for example, under the brand names Primovist® and Eovist®. Another MRI contrast agent with lower uptake into hepatocytes is gadobenate dimeglumine (Multihance®).

[0102] Gadoxetate disodium (GD, Primovist®) belongs to the group of intracellular contrast agents. It is approved for use in MRI of the liver to detect and characterize lesions in patients with known or suspected focal liver disease. GD, with its lipophilic ethoxybenzyl moiety, exhibits a biphasic distribution: first, distribution in the intravascular and interstitial spaces after bolus injection, followed by selective uptake by hepatocytes. GD is excreted unchanged from the body in approximately equal amounts via the kidneys and the hepatobiliary pathway (50:50 dual excretion mechanism). Due to its selective accumulation in healthy liver tissue, GD is also referred to as a hepatobiliary contrast agent.

[0103] GD is approved at a dose of 0.1 ml / kg body weight (BW) (0.025 mmol / kg BW Gd). The recommended administration of GD involves an undiluted intravenous bolus injection at a flow rate of approximately 2 ml / second, followed by flushing the IV cannula with physiological saline. A standard protocol for liver imaging using GD consists of several planning and pre-contrast sequences. Following IV bolus injection of the contrast agent, dynamic images are typically acquired during the arterial (approximately 30 seconds post-injection, pi), portal venous (approximately 60 seconds pi), and transition phases (approximately 2–5 minutes pi). The transition phase typically already shows some increase in liver signal intensity due to the incipient uptake of the agent by hepatocytes.Additional T2-weighted and diffusion-weighted (DWI) images can be acquired after the dynamic phase and before the late hepatobiliary phase. In one embodiment, the contrast agent is gadoxetate disodium.

[0104] 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 known as gadolinium-DOTA or gadoteric acid).

[0105] In another embodiment, the contrast agent comprises gadolinium(III) ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA); preferably, the contrast agent comprises the disodium salt of gadolinium(III)-ethoxybenzyl-diethylenetriaminepentaacetic acid (also known as gadoxetic acid).

[0106] In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium(III) 2-[3,9-bis[l-carboxylato-4-(2,3-dihydroxypropylamino)-4-oxobutyl]-3,6,9,15-tetrazabicyclo[9.3.1]pentadeca-l(15),ll,13-trien-6-yl]-5-(2,3-dihydroxypropylamino)-5-oxopentanoate (also known as gadopiclenol, see e.g. W02007 / 042504 as well as W02020 / 030618 and / or WO2022 / 013454).

[0107] In one embodiment of the present disclosure, the contrast agent is an agent comprising dihydrogen[(±)-4-carboxy-5,8,l l-tris(carboxymethyl)-l-phenyl-2-oxa-5,8,l 1-triazatridecane-13-oato(5-)]gadolinate(2-) (also known as gadobenic acid).

[0108] In one embodiment of the present disclosure, the contrast agent comprises 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 known as gadoquatrans) (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).

[0109] In one embodiment of the present disclosure, the contrast agent is an agent that contains a Gd 3+ -Complex of a compound of formula (I)

[0110]

[0111] (I) ,

[0112] includes, whereby

[0113] Ar a group selected from

[0114]

[0115] represents

[0116] where # represents the link to X, X represents a group consisting of

[0117] CH2, (CH2)2, (CH2)3, (CH2)4 and *-(CH2)2-O-CH2- # is selected

[0118] where * represents the connection to Ar and # which represents the connection to the acetic acid residue,

[0119] 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

[0120] R 4 a group selected from C2-C4-alkoxy, (H3C-CH2)-O-(CH2)2-O-, (FEC-CFy-O- CFE^-O-(CH2)2-O- and (H3C-CH2)-O-(CH2)2-O-(CH2)2-O-(CH2)2-O-

[0121] R 5 represents a hydrogen atom

[0122] and

[0123] R6 represents a hydrogen atom

[0124] or a stereoisomer, tautomer, hydrate, solvate or salt thereof, or a mixture thereof.

[0125] In one embodiment of the present disclosure, the contrast agent is an agent that contains a Gd 3+ -Complex of a compound of formula (II)

[0126]

[0127] Ar a group selected from

[0128]

[0129] represents

[0130] where # represents the link to X,

[0131] X represents a group consisting of CH2, (CH2)2, (CH2)3, (CH2)4 and *-(CH2)2-O-CH2- # is selected, where * represents the link to Ar and # which represents the connection to the acetic acid residue, R 7 a hydrogen atom or group selected from C'i-CA alkyl. -CH2OH, -(CH2)2OH and -CH2OCH3 represents;

[0132] R 8represents 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-;

[0133] R 9 and R 10 represent a hydrogen atom independently of one another; or a stereoisomer, tautomer, hydrate, solvate or salt thereof, or a mixture thereof.

[0134] The term "Ci-Cs-alkyl" refers to a linear or branched, saturated, monovalent hydrocarbon group with 1, 2, or 3 carbon atoms, e.g., methyl, ethyl, n-propyl, and isopropyl. The term "C2-C4-alkyl" refers to a linear or branched, saturated, monovalent hydrocarbon group with 2, 3, or 4 carbon atoms.

[0135] 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 defined as above, e.g. a methoxy, ethoxy, n-propoxy or isopropoxy group.

[0136] In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium 2,2',2"-(10-{l-carboxy-2-[2-(4-ethoxyphenyl)ethoxy]ethyl}-l,4,7,10-tetraazacy clododecan-1, 4, 7-triyl)triacetate (see e.g. WO2022 / 194777, Example 1).

[0137] 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 another 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-[(lS)-l-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).

[0138] In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium 2,2',2"-{10-[(lS)-4-(4-butoxyphenyl)-l-carboxybutyl]-l,4,7,10-tetraazacy clododecan-1, 4, 7-triyl}triacetate (see, e.g., WO2022 / 194777, Example 31).

[0139] 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]-l,4,7,10-tetraazacyclododecane- l,4,7-triyl}triacetate.

[0140] In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium-2,2',2"-[10-(carboxymethyl)-2-(4-ethoxybenzyl)-l,4,7,10-tetraazacyclododecane-l,4,7-triyl]triacetate.

[0141] 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-l-carboxylate hydrate (also known as gadodiamide).

[0142] 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)-l,4,7,10-tetrazacyclododec-l-yl]acetate (also known as gadoteridol).

[0143] In one embodiment of the present disclosure, the contrast agent is an agent comprising gadolinium(III) 2,2',2"-(10-((2R,3S)-l,3,4-trihydroxybutan-2-yl)-l,4,7,10-tetraazacy clododecan-1, 4, 7-triyl)triacetate (also known as gadobutrol or Gd-DO3A-butrol).

[0144] The contrast agent can also consist of multiple contrast agents. In a further step, a first difference representation is generated based on the first and second representations. The first difference representation represents a difference between the first and second representations. In other words, the first difference representation represents differences between the first and second representations. Generating the first difference representation can, for example, involve subtracting the first representation from the second representation.

[0145] In principle, it is also possible to capture differences between the first and second representations not by subtraction, but, for example, by division. For the sake of simplicity, the aspects of this disclosure are described using differences, without, however, limiting the disclosure to this form of execution. Those skilled in image processing are aware that differences between pictorial representations can also be determined and expressed in ways other than subtraction (e.g., by division). This is intended to ensure that other methods and representations that capture and represent differences between two representations also fall within the scope of this disclosure.

[0146] It is possible that the first and / or the second representation and / or the first difference representation are filtered using one or more filters. Examples of such filters are frequency-dependent weights as described, for example, in W02024100234A1 or EP24182725.2. However, other filters are also conceivable.

[0147] The first difference representation is fed into a trained machine learning model. The machine learning model is configured and trained to generate a second difference representation based on the first difference representation and model parameters (and optionally further input data).

[0148] The second difference representation is a difference representation predicted by the trained machine learning model based on the first difference representation (and optionally additional input data). The second difference representation is a synthetic representation. It represents a difference (or differences) between the second representation of the study area and a third representation of the study area, or between the first representation and a third representation of the study area (depending on what was trained). The third representation represents the study area after the application of a third amount of the contrast agent.

[0149] The third set is different from the first and second sets. The third set can also be zero, meaning the third representation can represent the area under investigation without contrast medium.

[0150] In one embodiment of the present disclosure, the first set is zero, the second set is equal to the standard set, and the third set is larger than the standard set.

[0151] In one embodiment of the present disclosure, the first set is zero, the second set is smaller than the standard set, and the third set is equal to or larger than the standard set.

[0152] In one embodiment of the present disclosure, the first quantity is smaller than the standard quantity, the second quantity is smaller than the standard quantity, and the third quantity is equal to or greater than the standard quantity. In another embodiment of the present disclosure, the first quantity is smaller than the standard quantity, the second quantity is equal to the standard quantity, and the third quantity is larger than the standard quantity.

[0153] In one embodiment of the present disclosure, the first set is smaller than the standard set, the second set is equal to the standard set, and the third set is zero.

[0154] In one embodiment of the present disclosure, the first set is equal to the standard set, the second set is smaller or larger than the standard set, and the third set is zero.

[0155] The second difference representation is generated by the trained machine learning model based on the first difference representation and model parameters (and optionally further input data).

[0156] A "machine learning model" can be understood as a computer-implemented data processing architecture. The model can receive input data and deliver output data based on this input data and model parameters. Through training, the model can learn a relationship between the input data and the output data. During training, model parameters can be adjusted to produce a desired output for a given input.

[0157] When training such a model, it is presented with training data from which it can learn. The trained machine learning model is the result of the training process. In addition to input data, the training data includes 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.

[0158] During the training process, the input data for the training data is fed into the model, and the model generates output data. This output data is then compared to the target data. Model parameters are modified to reduce the deviations between the output and target data to a (defined) minimum. To modify the model parameters with a view to reducing these deviations, an optimization method such as gradient descent can be used.

[0159] The deviations can be quantified using a loss function. Such a function can be used to calculate the loss for a given pair of output and target data. The goal of the training process can be to modify (adjust) the parameters of the machine learning model so that the loss is reduced to a (defined) minimum for all pairs in the training dataset.

[0160] If the output and target data are numbers, for example, the error function can be the absolute difference between these numbers. In this case, a large absolute error may mean that one or more model parameters need to be changed significantly.

[0161] For output data in the form of vectors, difference metrics between vectors such as the mean squared 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.

[0162] For higher-dimensional outputs, such as two-dimensional, three-dimensional, or even 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, for example, into a one-dimensional vector.

[0163] In this case, the machine learning model is configured and has been trained on training data to generate a second difference representation based on a first difference representation (and optionally further input data). In this case, the training data for training the machine learning model includes, for each reference object of a plurality of reference objects, (i) a first reference difference representation as input data and (ii) a second reference difference representation as target data.

[0164] The term "much" usually means more than 10, e.g. more than 100 or more than 500.

[0165] The first reference-difference representation represents a difference (differences) between a first reference representation and a second reference representation. The second reference-difference representation represents (i) a difference (differences) between the second reference representation and a third reference representation, or (ii) a difference (differences) between the first reference representation and a third reference representation.

[0166] The first reference representation represents a reference range of the reference object after application of the first amount of contrast agent in the frequency domain.

[0167] The second reference representation represents the reference range of the reference object after application of the second amount of contrast agent in the frequency domain.

[0168] The third reference representation represents the reference range of the reference object after application of the third amount of contrast agent in the frequency domain.

[0169] The first set, the second set, and the third set are different from each other.

[0170] The term “reference” is used in several places in this disclosure to distinguish the data used to train and / or validate the machine learning model from the data used to generate a synthetic representation using the trained machine learning model. However, the term “reference” is not to be understood in any other restrictive sense; this distinction is made solely to avoid an objection of lack of clarity in patent granting proceedings.

[0171] The reference object, like the object of study, is usually a living being, for example a mammal, for example a human.

[0172] The "reference area" is a part of the reference object, for example, an organ, part of an organ, multiple organs, or another part of the reference object. The reference area is usually the same for all reference objects or may comprise a sub-area that is the same for all reference objects. The study area typically corresponds to the reference area. In other words, the machine learning model can be trained on a defined reference area, such as the liver, kidney, heart, lungs, brain, stomach, bladder, prostate gland, intestines, breast, uterus, thyroid gland, pancreas, spleen, or a part thereof, or any other part of a living organism, and then used to predict a synthetic representation of that part of a study object.

[0173] The first reference representation has the same properties as the first representation described in this revelation. The second reference representation has the same properties as the second representation described in this revelation. The first reference-difference representation has the same properties as the first difference representation described in this revelation. During training, the machine learning model generates a predicted second reference-difference representation. The predicted second reference-difference representation has the same properties as the predicted second difference representation described in this revelation.

[0174] In addition to the input data mentioned, further data can be used as input for training the machine learning model (and for prediction after training). This can include data about the reference object, such as age, gender, height, weight, diagnosed diseases, lifestyle habits (e.g., alcohol consumption), and / or other / additional data. It can also include one or more further representations of the reference object's domain of study.

[0175] In one embodiment of the present disclosure, training and prediction are based exclusively on the difference representations; that is, no further radiological images are fed to the machine learning model. However, it is possible to feed the machine learning model, in addition to the difference representations, data on the reference object or the subject of the study, such as age, sex, height, weight, diagnosed diseases, lifestyle habits (e.g., alcohol consumption), and / or other / additional data.

[0176] In another embodiment of the present disclosure, training and prediction are based on the difference representations and one or more further representations of the domain of the object under investigation or the reference domain of the reference object. Training the machine learning model typically comprises the following steps: for each reference object of the plurality of reference objects:

[0177] Inputting the first reference difference representation (and optionally further input data) into the machine learning model,

[0178] Receiving a predicted second reference difference representation as output from the machine learning model,

[0179] Quantifying the deviation between the second reference difference representation and the predicted second reference difference representation,

[0180] Reducing the deviation by modifying model parameters of the machine learning model.

[0181] The modification (adjustment) of the model parameters is usually carried out in an optimization procedure such as a gradient descent method.

[0182] The training of the machine learning model can be terminated when a stop criterion is met. Such a stop criterion could be, for example: a predefined maximum number of training steps / cycles / epochs has been performed; deviations between output data and target data can no longer be reduced by modifying the model parameters; a predefined minimum of the error function is reached; and / or an extreme value (e.g., maximum or minimum) of another performance metric is reached.

[0183] When training a machine learning model, the training dataset is typically divided into several subsets to ensure robust evaluation and generalization. The most common sets include a training set, a validation set, and a test set. The training set is used to train the machine learning model. The validation set can be used to fine-tune hyperparameters and make decisions regarding the model architecture. It can also help evaluate the model's performance during training, allowing for the monitoring of overfitting and the implementation of adjustments to improve model performance. The test set is usually kept separate from the training and validation datasets and is used to evaluate the final performance of the model after training is complete.There are various partitioning strategies for partitioning the data, e.g. random partitioning, k-fold cross-validation, and stratified partitioning.

[0184] The machine learning model can be, or include, an artificial neural network. The artificial neural network can have an autoencoder architecture; for example, the artificial neural network can have an architecture like the U-Net (see, for example, O. Ronneberger et al. U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pages 234-241, Springer, 2015, https: / / doi.org / 10.1007 / 978-3-319-24574-4_28).

[0185] The artificial neural network can be a Generative Adversarial Network (GAN) or include one (see, e.g., M.-Y. Liu et al: Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications, arXiv:2008.02793; J. Henry et al: Pix2Pix GAN for Image-to-Image Translation, DOI: 10.13140 / RG.2.2.32286.66887).

[0186] The artificial neural network can be a Regularized Generative Adversarial Network (see e.g. Q. Li et al: RegGAN: An End-to-End Network for Building Footprint Generation with Boundary Regularization, Remote Sens. 2022, 14, 1835).

[0187] The artificial neural network can be a Conditional Adversarial Network or include one (see e.g. P. Isola et al. : Image-to-Image Translation with Conditional Adversarial Networks , arXiv: 1611.07004 [cs.CV]).

[0188] The artificial neural network can be a transformer network or include one (see e.g. D. Karimi et al. Convolution-Free Medical Image Segmentation using Transformers, arXiv:2102.13645 [eess.IV]).

[0189] Once the machine learning model is trained, it can be used for prediction. It can be used to generate a synthetic representation for a new object (referred to in this disclosure as the object of study). The term "new" means that representations of the object of study have not typically been used in training.

[0190] As described above, the first difference representation (and optionally further input data) is fed into the trained machine learning model. Based on this first difference representation (and optionally the further input data), the trained machine learning model generates a (predicted, synthetic) second difference representation.

[0191] In a further step, a third representation is generated based on the second difference representation produced by the trained machine learning model and the existing second or first representation. Generating the third representation typically involves adding the second difference representation to the second representation or adding the second difference representation to the first representation.

[0192] The third representation is a synthetic representation because it is based on the synthetic second difference representation.

[0193] The third representation represents the investigation area of ​​the object under investigation after application of a third amount of the contrast agent in the frequency space.

[0194] In a further step, a corresponding third spatial representation can be generated based on the third frequency-space representation. The third spatial representation can be generated, for example, from the third frequency-space representation by an inverse Fourier transform. The third spatial representation is a synthetic representation, as it is based on the synthetic second difference representation. The third spatial representation represents the area of ​​investigation of the object after application of the third quantity of the contrast agent in spatial space.

[0195] The third spatial representation can be output (e.g., displayed on a monitor and / or printed), stored in a data storage device, and / or transmitted to a separate computer system. The method described here for generating a synthetic representation of a study area of ​​a study object has the advantage that, in principle, only one representation (the first difference representation) needs to be input into the trained machine learning model to generate the synthetic representation. In the method described in WO2024 / 046831A1, two representations are required. This also speeds up the training process and requires less computational power for training and prediction. Furthermore, the synthetic representations generated with the method described here exhibit fewer artifacts than the representations generated by the method described in WO2019 / 074938A1.

[0196] Further embodiments are disclosed below. These embodiments are not necessarily subject matter that falls under patent protection. As is known to those skilled in the art in patent law, the scope of protection of a patent is defined by the patent claims. The description and the drawings are to be used to interpret the patent claims. The embodiments described below are part of the description and not of the patent claims. The following embodiments are intended to give the reader guidance on how various features described in this disclosure can be combined. They are therefore part of the present technical disclosure and should not be confused with the subject matter of the patent claims.

[0197] Other embodiments include:

[0198] Implementation 1: Computer-implemented method comprising the steps:

[0199] Providing a first representation, wherein the first representation represents an investigation area of ​​an object under investigation after application of a first amount of a contrast agent in the frequency space,

[0200] Providing a second representation, wherein the second representation represents the investigation area of ​​the object under investigation after application of a second amount of the contrast agent in the frequency space,

[0201] Generating a first difference representation based on the first representation and the second representation, where the first difference representation represents a difference between the first representation and the second representation.

[0202] Inputting the first difference representation into a trained machine learning model, receiving a second difference representation as output from the trained machine learning model,

[0203] Generating a third representation, wherein generating the third representation involves adding the second difference representation to the second representation or to the first representation,

[0204] Transforming the third representation into a third spatial representation, outputting and / or storing the third spatial representation and / or transmitting the third spatial representation to a separate computer system,

[0205] wherein the trained machine learning model was trained in a supervised learning process based on training data, wherein the training data for each reference object of a plurality of reference objects comprise (i) a first reference difference representation as input data and (ii) a second reference difference representation as target data, wherein the first reference difference representation represents a difference between a first reference representation and a second reference representation, and the second reference difference representation represents a difference between the second reference representation and a third reference representation or between the first reference representation and the third reference representation, wherein the first reference representation represents a reference range of the reference object after application of the first amount of the contrast agent in the frequency domain,where the second reference representation represents the reference range of the reference object after application of the second amount of contrast agent in the frequency domain, and the third reference representation represents the reference range of the reference object after application of a third amount of contrast agent in the frequency domain.

[0206] where the first set, the second set, and the third set are different from each other. Embodiment 2: Method according to embodiment 1, wherein the trained machine learning model is configured to generate the second difference representation solely on the basis of the first difference representation as the only representation of the investigation domain of the object under investigation.

[0207] Implementation form 3: Procedure according to implementation form 1 or 2, wherein the input data of the training data for each reference object of the plurality of reference objects consists of the first reference difference representation as the sole representation of the reference domain of the reference object. Implementation form 4: Procedure according to implementation form 1, wherein the trained machine learning model is configured to generate the second difference representation based on the first difference representation and on one or more further representations of the object of investigation within the domain of investigation.

[0208] Implementation form 5: Procedure according to implementation form 4, wherein one or more further representations include the first representation and / or the second representation.

[0209] Implementation form 6: Method according to one of embodiments 1, 4 or 5, wherein the training data for each reference object of the plurality of reference objects comprises the first reference difference representation and one or more further representations of the reference domain of the reference object as input data.

[0210] Implementation form 7: Procedure according to implementation form 6, wherein one or more further representations comprise the first reference representation and / or the second reference representation.

[0211] Implementation form 8: Procedure according to implementation form 1, wherein the trained machine learning model is configured to generate the second difference representation exclusively on the basis of the first difference representation and further data on the subject of investigation, wherein the further data are selected from: age, gender, height, weight, diagnosed diseases and / or lifestyle habits.

[0212] Implementation form 9: Method according to one of embodiments 1 or 8, wherein the input data of the training data for each reference object of the plurality of reference objects consists of the first reference difference representation and further data on the reference object, wherein the further data on the reference object are selected from: age, sex, height, weight, diagnosed diseases, lifestyle habits.

[0213] Embodiment 10: Method according to one of embodiments 1 to 9, wherein the first representation is an MRI scan or is based on an MRI scan.

[0214] Embodiment 11: Method according to one of embodiments 1 to 10, wherein the second representation is an MRI scan or is based on an MRI scan.

[0215] Embodiment 12: Method according to one of embodiments 1 to 9, wherein the first representation is a CT scan or is based on a CT scan.

[0216] Embodiment 13: Method according to one of embodiments 1 to 9 or 12, wherein the second representation is a CT scan or is based on a CT scan.

[0217] Embodiment 14: Method according to one of embodiments 1 to 13, wherein the second quantity is larger than the first quantity.

[0218] Embodiment 15: A method according to any one of embodiments 1 to 14, wherein the third quantity is larger than the second quantity. Embodiment 16: A method according to any one of embodiments 1 to 15, wherein the first quantity is less than 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, or 10% of the second quantity.

[0219] Implementation form 17: Procedure according to one of implementation forms 1 to 16, where the first quantity is zero.

[0220] Implementation form 18: Procedure according to one of implementation forms 1 to 17, wherein the first quantity is smaller than the standard quantity.

[0221] Implementation form 19: Procedure according to one of implementation forms 1 to 18, wherein the second quantity is smaller than the standard quantity.

[0222] Implementation form 20: Method according to one of embodiments 1 to 19, wherein the third quantity corresponds to the standard quantity.

[0223] Implementation form 21: Method according to one of embodiments 1 to 19, wherein the third quantity is larger than the standard quantity.

[0224] Embodiment 22: Method according to any of embodiments 1 to 21, wherein the second quantity is less than 90% or 80% or 70% or 60% or 50% or 40% or 30% or 20% or 10% of the third quantity.

[0225] Embodiment 23: Method according to one of embodiments 1 to 14, wherein the first quantity and the second quantity are non-zero and the third quantity is zero.

[0226] Embodiment 24: Method according to one of embodiments 1 to 14, wherein the first quantity is smaller than the standard quantity, the second quantity is equal to the standard quantity and the third quantity is zero.

[0227] Embodiment 25: Method according to any of embodiments 1 to 24, wherein the first quantity is less than 90% or 80% or 70% or 60% or 50% or 40% or 30% or 20% or 10% of the second quantity.

[0228] Embodiment 26: Method according to any one of embodiments 1 to 25, wherein the contrast agent is or comprises an extracellular contrast agent.

[0229] Embodiment 27: Method according to any one of embodiments 1 to 26, wherein the contrast agent is or comprises an intracellular contrast agent.

[0230] Embodiment 28: Method according to one of embodiments 1 to 27, wherein generating the first difference representation comprises subtracting the first representation from the second representation.

[0231] Embodiment 29: Method according to one of embodiments 1 to 28, wherein generating the third representation comprises adding the second difference representation to the second representation, and wherein the second reference difference representation was generated on the basis of the second reference representation and the third reference representation, wherein generating the second reference difference representation comprised subtracting the second reference representation from the third reference representation.

[0232] Embodiment 30: Method according to one of embodiments 1 to 29, wherein the first representation and the second representation are the result of a radiological examination of the object under investigation.

[0233] Embodiment 31: Method according to one of embodiments 1 to 30, further comprising:

[0234] - Generating the first representation from a first spatial representation, where the first spatial representation represents the area of ​​investigation of the object after application of the first amount of contrast agent in spatial space; - Generating the second representation from a second spatial representation, where the second spatial representation represents the area of ​​investigation of the object after application of the second amount of contrast agent in spatial space.

[0235] where the first spatial representation and the second spatial representation are the result of a radiological examination of the object under investigation.

[0236] Implementation form 32: Procedure according to one of implementation forms 1 to 31, wherein the first reference representation and the second reference representation of each reference object are the result of a radiological examination on the reference object.

[0237] Implementation form 33: Method according to one of implementation forms 1 to 32, wherein the first reference representation in the frequency domain was generated on the basis of a first reference representation in spatial space, wherein the first reference representation in spatial space represents the reference range of the reference object after application of the first quantity of the contrast agent in spatial space, wherein the second reference representation in the frequency domain was generated on the basis of a second reference representation in spatial space, wherein the second reference representation in spatial space represents the reference range of the reference object after application of the second quantity of the contrast agent in spatial space.

[0238] wherein the third reference representation in the frequency domain was generated on the basis of a third reference representation in spatial space, wherein the third reference representation in spatial space represents the reference range of the reference object after application of the third quantity of the contrast agent in spatial space,

[0239] wherein the first reference representation in local space, the second reference representation in local space, and the third reference representation in local space of each reference person are the result of a radiological examination of the reference person.

[0240] Implementation form 34: Procedure according to one of implementation forms 30 to 33, wherein the radiological examination is or includes an MRI examination.

[0241] Implementation form 35: Procedure according to one of implementation forms 30 to 34, wherein the radiological examination is or includes a CT examination.

[0242] Implementation form 36: Method according to one of implementation forms 1 to 35, wherein the contrast medium comprises one or more of the following substances:

[0243] Gadoxetate disodium

[0244] Gadolinium(III) 2-[4, 7, 10-tris(carboxymethyl)-1,4,7, 10-tetrazacyclododec-1-yl]acetic acid, Gadolinium(III) ethoxybenzyl-diethylenetriaminepentaacetic acid,

[0245] Gadolinium(III) 2-[3,9-bis[l-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,

[0246] Dihydrogen[(±)-4-carboxy-5, 8, 11 -tris(carboxymethyl)- 1 -phenyl-2-oxa-5, 8, 11 -triazatridecane- 13 - oato(5-)]gadolinate(2-),

[0247] Tetragadolinium-[4,10-bis(carboxylatomethyl)-7-{3,6,12,15-tetraoxo-16-[4,7,10-tris- (carboxylatomethyl)-l,4,7,10-tetraazacyclododecan-l-yl]-9,9-bis({[({2-[4,7,10-tris- (carboxylatomethyl)- 1,4,7, 10-tetraazacyclododecan- 1 -yl]propanoyl} aminojacetyl] - amino}methyl)-4,7, 11 , 14-tetraazahepta-decan-2-yl} - 1 ,4,7, 10-tetraazacyclododecan- 1 -yl]acetat, Gadolinium 2,2',2"-( 10- { 1 -carboxy -2-[2-(4-ethoxyphenyl)ethoxy]ethyl} - 1 ,4,7, 10- tetraazacyclododecan- 1,4,7 -triyljtriacetat,

[0248] Gadolinium 2,2',2"-{ 10-[l-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-l,4,7,10- tetraazacyclododecan-l,4,7-triyl}triacetat,

[0249] Gadolinium 2,2',2"-{ 10-[(lR)-l-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]- 1,4,7, 10-tetraazacyclododecan- 1 ,4,7-triyl [triacctat.

[0250] Gadolinium (2S,2'S,2"S)-2,2',2"-{ 10-[(lS)-l-carboxy-4-{4-[2-(2-ethoxyethoxy)ethoxy] phenyl [butyl] - 1 ,4,7, 10-tetraazacyclododecan- 1 ,4,7-triyl} tris (3 -hydroxypropanoat),Gadolinium 2, 2', 2"- { 10- [( 1 S)-4-(4-butoxyphenyl)- 1 -carboxybutyl] - 1 ,4,7, 10-tetraazacyclododecan-l,4,7-triyl}triacetat,

[0251] Gadolinium-2,2',2"- { (25)- 10-(carboxymethyl)-2-[4-(2-ethoxyethoxy)benzyl] -1,4,7,10-tetraazacyclododecane- 1 ,4,7-triyl}triacetat,

[0252] Gadolinium-2,2',2"- [ 10-(carboxymethyl)-2-(4-ethoxybenzyl)- 1 ,4,7, 10-tetraazacyclododecane- 1.4.7-triyl]triacetat,

[0253] Gadolinium(III) 5,8-bis(carboxylatomethyl)-2-[2-(methylamino)-2-oxoethyl]-10-oxo-2,5,8,l 1-tetraazadodecan- 1 -carboxylat-Hydrat,

[0254] Gadolinium(III) 2-[4-(2-hydroxypropyl)-7,10-bis(2-oxido-2-oxoethyl)-l,4,7,10-tetrazacyclododec- 1 -yl]acetat,

[0255] Gadolinium(III) 2,2',2"-(10-((2R,3S)-1,3,4-trihydroxybutan-2-yl)-1,4,7, 10-tetraazacyclododecane-1.4.7-triyl)triacetate,

[0256] Gd 3+ -Complex of a compound of formula (I)

[0257]

[0258] (I) ,

[0259] where

[0260] Ar a group selected from

[0261]

[0262] where # represents the link to X,

[0263] X represents a group consisting of

[0264] CH2, (CH2)2, (CH2)3, (CH2)4and *-(CH2)2-O-CH2- # is selected

[0265] where * represents the connection to Ar and # which represents the connection to the acetic acid residue, R 1 , R 2 and R 3 independently represent a hydrogen atom or a group selected from Ci-Cs-alkyl, -CH2OH, -(CH2)2OH and -CH2OCH3,

[0266] R 4represents 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-

[0267] R 5 represents a hydrogen atom

[0268] and

[0269] R 6 represents a hydrogen atom

[0270] or a stereoisomer, tautomer, hydrate, solvate or salt thereof, or a mixture thereof, Gd complex of a compound of formula (II)

[0271]

[0272] where

[0273] Ar a group selected from

[0274] R 8

[0275]

[0276] where # represents the link to X,

[0277] X represents a group consisting of CH2, (CH2)2, (CH2)3, (CH2)4 and *-(CH2)2-O-CH2- # is selected, where * represents the link to Ar and # which represents the connection to the acetic acid residue, R 7a hydrogen atom or group selected from C'i-C's alkyl. -CH2OH, -(CH2)2OH and -CH2OCH3 represents;

[0278] R 8 represents 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-;

[0279] R 9 and R 10 represent a hydrogen atom independently of each other;

[0280] or a stereoisomer, tautomer, hydrate, solvate or salt thereof, or a mixture thereof. Embodiment 37: Method according to any one of embodiments 1 to 36, wherein the subject of investigation and each reference subject is a mammal.

[0281] Embodiment 38: Method according to one of embodiments 1 to 37, wherein the subject of investigation and each reference object is a human being.

[0282] Embodiment 39: A method according to any one of embodiments 1 to 38, wherein the area of ​​investigation and the reference area of ​​each reference object is or comprises a boar. Embodiment 40: A method according to any one of embodiments 1 to 38, wherein the area of ​​investigation and the reference area of ​​each reference object is or comprises a brain. Embodiment 41: A method according to any one of embodiments 1 to 38, wherein the area of ​​investigation and the reference area of ​​each reference object is or comprises a heart. Embodiment 42: A method according to any one of embodiments 1 to 38, wherein the area of ​​investigation and the reference area of ​​each reference object is or comprises a kidney. Embodiment 43: A method according to any one of embodiments 1 to 38, wherein the area of ​​investigation and the reference area of ​​each reference object is or comprises a tongue.

[0283] Embodiment 44: A method according to any one of embodiments 1 to 38, wherein the examination area and the reference area of ​​each reference object is or comprises a stomach. Embodiment 45: A method according to any one of embodiments 1 to 38, wherein the examination area and the reference area of ​​each reference object is or comprises an intestine. Embodiment 46: A method according to any one of embodiments 1 to 45, wherein the examination area and the reference area of ​​each reference object is or comprises a pancreas.

[0284] Implementation form 47: Procedure according to any of implementation forms 1 to 38, wherein the examination area and the reference area of ​​each reference object is or comprises a thyroid gland.

[0285] Embodiment 48: Method according to any of embodiments 1 to 38, wherein the examination area and the reference area of ​​each reference object is or comprises a prostate.

[0286] Embodiment 49: A method according to any one of embodiments 1 to 38, wherein the examination area and the reference area of ​​each reference object is or comprises a breast. Embodiment 50: A method according to any one of embodiments 1 to 49, wherein the reference area is the same for all reference objects and the examination area corresponds to the reference area.

[0287] Embodiment 51: Method according to any of embodiments 1 to 50, wherein the machine learning model is or comprises an artificial neural network.

[0288] Embodiment 52: Method according to one of embodiments 1 to 51, wherein the machine learning model is or comprises a transformer network.

[0289] Embodiment 53: Method according to one of embodiments 1 to 52, wherein training the machine learning model comprises:

[0290] Inputting the first reference-difference representation into the machine learning model, receiving a predicted second reference-difference representation as output from the machine learning model,

[0291] Quantifying the deviation between the second reference difference representation and the predicted second reference difference representation,

[0292] Reducing the deviation by modifying model parameters of the machine learning model.

[0293] Embodiment 54: Method according to one of embodiments 1 to 53, wherein the first quantity is zero, the second quantity is equal to a standard quantity of the contrast agent, and the third quantity is greater than the standard quantity, or

[0294] the first set is zero, the second set is smaller than the standard set, and the third set is equal to or larger than the standard set, or

[0295] the first set is smaller than the standard set, the second set is smaller than the standard set, and the third set is equal to or larger than the standard set, or

[0296] the first set is smaller than the standard set, the second set is equal to the standard set, and the third set is larger than the standard set, or

[0297] the first set is smaller than the standard set, the second set is equal to the standard set, and the third set is zero, or

[0298] The first set is equal to the standard set, the second set is smaller or larger than the standard set, and the third set is zero.

[0299] Design 55: Computer system comprising

[0300] a receiving unit,

[0301] a control and computing unit and

[0302] an output unit,

[0303] wherein the control and computing unit is configured to execute the method according to one of embodiments 1 to 54. Embodiment 56: Computer program product comprising a computer program that can be loaded into a working memory of a computer system and causes the computer system there to execute the method according to one of embodiments 1 to 54.

[0304] Version 75: Kit comprising a contrast agent and a computer program product according to version 56.

[0305] Fig. 1 shows an implementation of the computer-implemented method of the present disclosure in the form of a flowchart.

[0306] The procedure (100) comprises the following steps:

[0307] (110) Providing a first representation, wherein the first representation represents an area of ​​investigation of an object of investigation after application of a first quantity of a contrast agent in the frequency space,

[0308] (120) Providing a second representation, wherein the second representation represents the area under investigation of the subject after application of a second quantity of the contrast agent in the frequency space,

[0309] (130) Generating a first difference representation based on the first representation and the second representation, wherein the first difference representation represents a difference between the first representation and the second representation,

[0310] (140) Inputting the first difference representation into a trained machine learning model, (150) Receiving a second difference representation as output from the machine learning model,

[0311] (160) Generating a third representation, wherein generating the third representation includes adding the second difference representation to the second representation or to the first representation,

[0312] (170) Transforming the third representation into a third spatial representation,

[0313] (180) Output and / or storage of the third place-space representation and / or transmission of the third place-space representation to a separate computer system,

[0314] wherein the trained machine learning model was trained in a supervised learning process based on training data, wherein the training data for each reference object of a plurality of reference objects comprise (i) a first reference difference representation as input data and (ii) a second reference difference representation as target data, wherein the first reference difference representation represents a difference between a first reference representation and a second reference representation, and the second reference difference representation represents a difference between the second reference representation and a third reference representation or between the first reference representation and the third reference representation, wherein the first reference representation represents a reference range of the reference object after application of the first amount of the contrast agent in the frequency domain,where the second reference representation represents the reference range of the reference object after application of the second amount of contrast agent in the frequency domain, and the third reference representation represents the reference range of the reference object after application of a third amount of contrast agent in the frequency domain.

[0315] where the first set is different from the second set and the third set, and the second set is different from the third set.

[0316] Fig. 2 shows, by way of example and schematically, a method for training the machine learning model of the present disclosure. - TI -

[0317] The machine learning model (MLM) is trained using training data. The training data includes, for each reference object in a plurality of reference objects, (i) a first reference difference representation (RRD). 1) as input data and (ii) a second reference difference representation (RRD) 2 ) as target data.

[0318] In Fig. 2, only one data set of a reference object comprises a first reference difference representation (RRD). 1 ) and a second reference difference representation (RRD) 2 ) shown. The training data typically comprises a large number of such datasets.

[0319] During training, the first reference difference representation (RRD) is created. 1 The data is fed into the machine learning model (MLM). The model is configured and trained based on the first reference difference representation (RRD). 1 ) and based on model parameters (MP) (and optionally based on further input data) a predicted second reference difference representation (RRD) 2 *) TO generate. The predicted second reference-difference representation (RRD) 2*) is combined with the second reference difference representation (RRD). 2 ) compared. An error function (LF) is used to account for deviations between the predicted second reference difference representation (RRD). 2 *) and the second reference difference representation (RRD) 2 ) TO quantify. In an optimization procedure (e.g., a gradient descent method), the deviations are reduced by modifying the model parameters (MP).

[0320] The steps shown in Fig. 2 are performed for a large number of datasets. The training of the machine learning model (MLM) can be terminated when a stop criterion is met. Such a stop criterion could be, for example: a predefined maximum number of training steps / cycles / epochs has been performed; deviations between the predicted second reference difference representation and the second reference difference representation can no longer be reduced by modifying the model parameters; a predefined minimum of the error function is reached; and / or an extreme value (e.g., maximum or minimum) of another performance metric is reached.

[0321] Once the training is complete, the trained machine learning model can be used for prediction. This is shown schematically and by way of example in Fig. 3.

[0322] Figure 2 shows how the training data can be obtained, in addition to the training of the machine learning model (MLM). This, like the training, is demonstrated using a reference object.

[0323] The starting point shown in Fig. 2 consists of three reference representations, a first reference representation (RRi) 1 ), a second reference representation (RR2) 1 ) and a third reference representation (RR3) 1 ).

[0324] The first reference representation (RR1) 1 ) represents a reference area of ​​the reference object after application of a first amount of a contrast agent in spatial space. The second reference representation (RR2) 1 ) represents the reference range of the reference object after application of a second amount of the contrast agent in spatial space. The third reference representation (RR3) 1) represents the reference range of the reference object after application of a third quantity of the contrast agent in spatial space. In this example, the reference range comprises the lungs of a human.

[0325] From the first reference representation (RR1) 1 ) is transformed, for example, by a Fourier transform, into a first reference representation (RRi) F ) generated in the frequency domain. The first reference representation (RRi) F ) represents the reference range of the reference object after application of the first amount of contrast agent in the frequency domain.

[0326] From the second reference representation (RR2) 1 ) is transformed, for example, by a Fourier transform, into a second reference representation (RR2). F ) in the frequency domain. The second reference representation (RR2) F) represents the reference range of the reference object after application of the second amount of contrast agent in the frequency domain.

[0327] From the third reference representation (RR3) 1 ) is transformed, for example, by a Fourier transform, into a third reference representation (RR3). F ) in the frequency domain. The third reference representation (RR / ) represents the reference range of the reference object after application of the third amount of contrast agent in the frequency domain.

[0328] Based on the first reference representation (RRI) F ) and the second reference representation (RR2) F ) becomes the first reference difference representation (RRD). 1 ) generated. The generation of the first reference-difference representation (RRD) 1 ) can be a subtraction of the first reference representation (RR / ) from the second reference representation (RR2). F) include the creation of the first reference-difference representation (RRD). 1 ) can involve subtracting the second reference representation (RRI). F ) from the first reference representation (RR2) F ) include.

[0329] Based on the second reference representation (RR2) F ) and the third reference representation (RR3) F ) becomes the second reference difference representation (RRD). 2 ) generated. The generation of the second reference-difference representation (RRD) 2 ) can involve subtracting the second reference representation (RR2). F ) from the third reference representation (RR3) F ) include.

[0330] It is also possible to use the second reference difference representation (RRD). 2 ) based on the first reference representation (RRI) F ) and the third reference representation (RR3) F ) to generate. Generating the second reference-difference representation (RRD).2 ) can involve subtracting the first reference representation (RR2). F ) from the third reference representation (RR3) F ) include.

[0331] Figure 2 illustrates how spatial representations are transformed into frequency-space representations. These spatial representations are typically the result of a radiological examination of the reference object. Therefore, they are usually measurement-generated representations. It should be noted that spatial representations are not strictly necessary. Frequency-space representations can also be generated as a result of the radiological examination of the reference object. For example, an MRI scan typically generates so-called k-space data, which represent a frequency-space representation of an area of ​​investigation within the reference object.It is therefore possible, for example, that the frequency space representations are generated directly during the radiological examination of the reference object and used to generate the training data (first reference difference representation, second reference difference representation).

[0332] Fig. 3 shows, by way of example and schematically, the use of the trained machine learning model (MLM') for prediction.

[0333] In a first step, an initial representation (RI) is created. F ) and a second representation (RI) F ) provided.

[0334] The first representation (R1) F ) represents an examination area of ​​an object under investigation after application of the first amount of contrast agent in the frequency space.

[0335] The second representation (R2) F) represents the examination area of ​​the object under investigation after application of the second amount of contrast agent in the frequency domain.

[0336] The area of ​​investigation, as with the training of the machine learning model, encompasses the lungs of a human being.

[0337] The first representation (RI) F ) in the frequency domain is based on a first representation (Ri 1 ) is generated in spatial space. The first spatial representation (R / ) represents the area under investigation of the object after application of the first amount of contrast agent in spatial space. The first representation (R / ) in the frequency domain can be generated, for example, by Fourier transformation from the first spatial representation (R / ).

[0338] The second representation (R2) F ) in the frequency domain is based on a second representation (R2) 1 ) generated in the local space. The second local space representation (R2) 1) represents the area of ​​investigation of the object after application of the second amount of contrast agent in spatial space. The second representation (R2) F ) in the frequency domain can, for example, be obtained from the second representation (R2) by Fourier transformation 1 ) are generated in the local area.

[0339] Based on the first representation (RI) F ) in the frequency domain and the second representation (R2) F ) in the frequency domain a first difference representation (RD) is created 1 ) generated. The first difference representation (RD) 1 ) represents a difference between the first representation (RI) F ) and the second representation (R2) F ). Generating the first difference representation (RD) 1 ) can, for example, involve subtracting the first representation (RI) F ) from the second representation (R2 F ) include the generation of the first difference representation (RD). 1) can, for example, involve subtracting the second representation (R2). F ) from the first representation (Ri F ) include. Here, the subtraction method chosen is the same one used when training the machine learning model.

[0340] The first difference representation (RD) 1 ) is applied to the trained model (MLM) 1 ) of machine learning. The machine learning model (MLM') may, for example, have been trained as described in relation to Fig. 2.

[0341] The trained machine learning model (MLM') generates based on the first difference representation (RD). 1 ) (and optionally based on further input data) a predicted second difference representation (RD) 2 *).

[0342] In a further step, a third representation (R, 1 ) is generated. The generation of the third representation (R3) 1) can be an addition of the second representation (R2) F ) to the predicted second difference representation (RD) 2 *) include. The generation of the third representation (R, 1 ) can be an addition of the first representation (RI) F ) to the predicted second difference representation (RD) 2 *) include. Here, the addition method chosen is the same one used when training the machine learning model.

[0343] The third representation (R3) 1 ) represents the area of ​​investigation of the object after application of the third amount of contrast agent in the frequency domain. Based on the third representation (R3) 1 ) in the frequency domain, a third representation (R3) can be created by a transformation, e.g. an inverse Fourier transform. 1 ) are generated in the local space. The third local space representation (R3) 1) represents the area of ​​investigation of the object under investigation after application of the third amount of contrast agent in the local space.

[0344] The third spatial representation (R3) 1 ) can be output, i.e., displayed on a monitor and / or printed on a printer and / or stored in a data storage device and / or transmitted to a separate computer system.

[0345] Figure 3 illustrates how spatial representations are transformed into frequency-space representations. Spatial representations are typically the result of a radiological examination of the object under investigation. Therefore, they are usually measurement-generated representations. It should be noted that spatial representations are not strictly necessary. Frequency-space representations can also be generated as a result of the radiological examination of the object. For example, an MRI scan typically produces so-called k-space data, which represent a frequency-space representation of an area of ​​the object under investigation.It is therefore possible, for example, that the frequency space representations are generated directly during the radiological examination of the object under investigation and are used to generate the input (first difference representation) for the trained machine learning model.

[0346] Fig. 4 shows, by way of example and schematically, a computer system according to the present disclosure, which can be used to train the machine learning model and / or to use the trained machine learning model for prediction.

[0347] A "computer system" is a system for electronic data processing that processes data using programmable instructions. Such a system typically comprises a "computer," the unit containing a processor for performing logical operations, as well as peripherals.

[0348] In computer technology, "peripherals" refers to all devices connected to a computer that are 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 ports and expansion cards are also considered peripherals in computer technology.

[0349] The computer system (10) shown in Fig. 4 comprises a receiving unit (11), a control and computing unit (12) and an output unit (13).

[0350] The control and computing unit (12) serves to control the computer system (10), to coordinate the data flows between the units of the computer system (10) and to perform calculations.

[0351] The control and computing unit (12) is configured:

[0352] to provide a first representation and / or to cause the receiving unit (11) to receive the first representation, wherein the first representation represents an area of ​​investigation of an object of investigation after application of a first quantity of a contrast agent in the frequency space,

[0353] to provide a second representation and / or to cause the receiving unit to receive the second representation, wherein the second representation represents the area under investigation of the object after application of a second quantity of the contrast agent in the frequency domain,

[0354] to generate a first difference representation based on the first representation and the second representation, where the first difference representation represents a difference between the first representation and the second representation,

[0355] to input the first difference representation into a trained machine learning model,

[0356] to receive a second difference representation as output from the machine learning model,

[0357] to generate a third representation, wherein generating the third representation involves adding the second difference representation to the second representation or to the first representation,

[0358] to transform the third representation into a third spatial representation, to cause the output unit (13) to output and / or store the third spatial representation and / or to transmit it to a separate computer system.

[0359] Fig. 5 shows, by way of example and schematic, another embodiment of the computer system of the present disclosure.

[0360] The computer system (1) comprises a processing unit (21) connected to a memory (22). The processing unit (21) and the memory (22) form a control and arithmetic unit, as shown in Fig. 4.

[0361] 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 ordinary 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 array of electronic circuits, some of which may be implemented as an integrated circuit or as several interconnected integrated circuits (an integrated circuit is sometimes referred to as a "chip"). The processing unit (21) may be configured to execute computer programs that may be stored in a working memory of the processing unit (21) or in the memory (22) of the same or another computer system.

[0362] The memory (22) can be ordinary computer hardware capable of storing information such as digital image recordings (e.g., representations of the study area), data, computer programs, and / or other digital information, either temporarily and / or permanently. The memory (22) can be volatile and / or non-volatile and can be permanently installed or removable. Examples of suitable memory include RAM (Random Access Memory), ROM (Read-Only Memory), a hard disk, flash memory, a removable computer disk, an optical disc, a magnetic tape, or a combination of the above. Optical discs can include read-only compact discs (CD-ROM), read / write compact discs (CD-R / W), DVDs, Blu-ray discs, and similar devices.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 include one or more communication interfaces (32, 33) and / or one or more user interfaces (11, 12, 31). The one or more communication interfaces can be configured to send and / or receive information, for example, 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 configured to transmit and / or receive information via physical (wired) and / or wireless communication links.The one or more communication interfaces may include one or more interfaces for connecting to a network, e.g., using technologies such as cellular, 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 near-field communication interfaces configured to connect devices using near-field communication technologies such as NFC, RFID, Bluetooth, Bluetooth LE, ZigBee, infrared (e.g., IrDA), or similar technologies.

[0363] The user interfaces may include a display (31). A display (31) may be configured to show information to a user. Suitable examples include a liquid crystal display (LCD), a light-emitting diode (LED) display, a plasma display (PDP), or the like. 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), for example, for processing, storage, and / or display. Suitable examples of user input interfaces include a microphone, an image or video recording device (e.g., a camera), a keyboard or keypad, a joystick, a touch-sensitive surface (separate from or integrated into a touchscreen), or the like.In some examples, the user interfaces may include automatic identification and data capture (AIDC) technology for machine-readable information. This could include barcodes, radio frequency identification (RFID), magnetic stripes, optical character recognition (OCR), integrated circuit cards (ICC), and similar technologies. The user interfaces may also include one or more interfaces for communication with peripheral devices such as printers and the like.

[0364] One or more computer programs (40) can be stored in memory (22) and executed by the processing unit (21), which is programmed to perform the functions described in this description. The retrieval, loading, and execution of instructions from the computer program (40) can be performed sequentially, with one instruction being retrieved, loaded, and executed at a time. However, the retrieval, loading, and / or execution can also be performed in parallel.

[0365] The memory (22) can also store the machine learning model. The computer system of the present disclosure can be implemented as a laptop, notebook, netbook and / or tablet PC; it can also be a component of an MRI scanner, a CT scanner or an ultrasound diagnostic device.

[0366] The subject matter of this disclosure also includes a computer program product. Such a computer program product comprises a non-volatile data carrier such as a CD, a DVD, a USB flash drive, or another medium for storing data. A computer program is stored on the data carrier. The computer program can be loaded into the main memory of a computer system (in particular, into the main memory of a computer system of this disclosure) and cause the computer system to perform the following steps:

[0367] Providing a first representation, wherein the first representation represents an investigation area of ​​an object under investigation after application of a first amount of a contrast agent in the frequency space,

[0368] Providing a second representation, wherein the second representation represents the investigation area of ​​the object under investigation after application of a second amount of the contrast agent in the frequency space,

[0369] Generating a first difference representation based on the first representation and the second representation, where the first difference representation represents a difference between the first representation and the second representation.

[0370] Inputting the first difference representation into a trained machine learning model, receiving a second difference representation as output from the machine learning model,

[0371] Generating a third representation, wherein generating the third representation involves adding the second difference representation to the second representation or to the first representation,

[0372] Transforming the third representation into a third representation in spatial space, outputting and / or storing the third representation in spatial space and / or transmitting the third representation in spatial space to a separate computer system.

[0373] The computer program product can also be marketed in combination (in a kit) with the contrast agent. Such a kit is also referred to as a kit. A kit includes the contrast agent and the computer program product. It is also possible for such a kit to include the contrast agent and means that allow a buyer to obtain the computer program, for example, by downloading it from an internet site. These means may include a link, i.e., an address of the internet site where the computer program can be obtained, for example, from which the computer program can be downloaded to an internet-connected computer system. These means may include a code (e.g., an alphanumeric string, a QR code, a DataMatrix code, a barcode, or another optically and / or electronically readable code) that grants the buyer access to the computer program.Such a link and / or code can, for example, be printed on the packaging of the contrast agent and / or on an accompanying leaflet for the contrast agent. A kit is therefore a combination product comprising a contrast agent and a computer program (e.g., in the form of access to the computer program or in the form of executable program code on a data carrier), which is offered for sale together.

Claims

Patent claims 1. Computer-implemented method including: Providing an initial representation (RI) F ), where the first representation (RI F ) represents an examination area of ​​an object under investigation after application of an initial amount of a contrast agent in the frequency domain, Providing a second representation (R2) F ), where the second representation (R2) F ) represents the examination area of ​​the object under investigation after application of a second amount of the contrast agent in the frequency domain, Generating a first difference representation (RD) 1 ) based on the first representation (RI) F ) and the second representation (R2) F ), where the first difference representation (RD) 1 ) a difference between the first representation (Ri F ) and the second representation (R2) F ) represents, Entering the first difference representation (RD) 1 ) into a trained machine learning model (MLM'), Receiving a second difference representation (RD) 2 *) as output from the trained model (MLM) 1 ) of machine learning, Generating a third representation (R3) F ), where the generation of the third representation (FU 1 ) an addition of the second difference representation (RD) 2 *) TO the second representation (R2 F ) or to the first representation (RI) F ) includes, Transforming the third representation (FU) 1 ) into a third spatial representation (R3) 1 ), Output and / or storage of the third spatial representation (R3) 1 ) and / or transmission of the third spatial representation (R3) 1 ) to a separate computer system, where the trained model (MLM) 1) of machine learning in a supervised learning process based on training data, wherein the training data for each reference object of a plurality of reference objects (i) is a first reference difference representation (RRD) 1 ) as input data and (ii) a second reference difference representation (RRD) 2 ) as target data, where the first reference difference representation (RRD) 1 ) a difference of a first reference representation (RRI) F ) and a second reference representation (RR2) F ) represents and the second reference difference representation (RRD) 2 ) a difference of the second reference representation (RR2) F ) and a third reference representation (RR3) F ) or the first reference representation (RRI) F ) and the third reference representation (RR3) F) represents, where the first reference representation (RRÜ) represents a reference range of the reference object after application of the first amount of contrast agent in the frequency domain, where the second reference representation (RR2) F ) represents the reference range of the reference object after application of the second amount of contrast agent in the frequency domain and the third reference representation (RR3) F ) represents the reference range of the reference object after application of a third amount of the contrast agent in the frequency domain, where the first set, the second set, and the third set are different from each other.

2. The method according to claim 1, wherein the generation of the first difference representation (RD) 1 ) a subtraction of the first representation (RI) F ) from the second representation (R2 F ) includes generating the third representation (R3) F) an addition of the second difference representation (RD) 2 *) TO the second representation (R2 F ) includes, and where the second reference difference representation (RRD) 2 ) based on the second reference representation (RR2) F ) and the third reference representation (RR3) F ) was generated, whereby the generation of the second reference difference representation (RRD) 2 ) Subtracting the second reference representation (RR2) F ) from the third reference representation (RRS) F ) included.

3. Method according to one of claims 1 or 2, wherein the first representation (RI) F ) and the second representation (R2) F ) are the result of a radiological examination of the object under investigation.

4. Method according to any one of claims 1 to 3, further comprising: - Generating the first representation (Ri F) from a first spatial representation (Ri '). where the first spatial representation (Ri 1 ) represents the area of ​​investigation of the object under investigation after application of the first amount of contrast agent in the local space, - Generating the second representation (R2) F ) from a second spatial representation (R2) 1 ), where the second spatial representation (R2) 1 ) represents the area of ​​investigation of the object under investigation after application of the second amount of contrast agent in the local space, where the first spatial representation (Ri 1 ) and the second spatial representation (R2) 1 ) are the result of a radiological examination of the object under investigation.

5. Method according to any one of claims 1 to 3, wherein the first reference representation (RRI) F ) and the second reference representation (RR2) F) of each reference object are the result of a radiological examination of the reference object.

6. Method according to claim 4, wherein the first reference representation (RRI) F ) in the frequency domain based on a first reference representation (RR1) 1 ) was generated in spatial space, with the first reference representation (RR1) 1 ) in local space represents the reference area of ​​the reference object after application of the first amount of contrast agent in local space, where the second reference representation (RR2) F ) in the frequency domain based on a second reference representation (RR2) 1 ) was generated in the local space, with the second reference representation (RR2) 1 ) in local space represents the reference area of ​​the reference object after application of the second amount of contrast agent in local space, where the third reference representation (RR3) F) in the frequency domain based on a third reference representation (RR3) 1 ) was generated in the local space, with the third reference representation (RR3) 1 ) in local space represents the reference area of ​​the reference object after application of the third amount of contrast agent in local space, where the first reference representation (RR1) 1 ) in the local space and the second reference representation (RR2) 1 ) in the local space and the third reference representation (RR3) 1 ) in the local area of ​​each reference person are the result of a radiological examination of the reference person.

7. Method according to any one of claims 3 to 6, wherein the radiological examination is or comprises an MRI examination.

8. A method according to any one of claims 3 to 7, wherein the radiological examination is or comprises a CT examination.

9. A method according to any one of claims 1 to 8, wherein the trained machine learning model is configured to provide the second difference representation (RD). 2 *) exclusively based on the first difference representation (RD) 1 ) and optionally generate further data on the subject of the study, wherein the further data are selected from: age, sex, height, weight, diagnosed diseases and / or lifestyle habits, wherein the input data of the training data for each reference object of the multitude of reference objects from the first reference difference representation (RRD) 1 ) and optionally further data on the reference object, whereby the further data on the reference object are selected from: age, gender, height, weight, diagnosed diseases, lifestyle habits.

10. Method according to any one of claims 1 to 9, wherein the contrast agent comprises one or more of the following substances: Gadoxetate disodium Gadolinium(III) 2-[4, 7, 10-tris(carboxymethyl)-1,4,7, 10-tetrazacyclododec-1-yl]acetic acid, Gadolinium(III) ethoxybenzyl-diethylenetriaminepentaacetic acid, Gadolinium(III) 2-[3,9-bis[l-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, Dihydrogen[(±)-4-carboxy-5, 8, 11 -tris(carboxymethyl)- 1 -phenyl-2-oxa-5, 8, 11 -triazatridecane- 13 - oato(5-)]gadolinate(2-), Tetragadolinium-[4,10-bis(carboxylatomethyl)-7-{3,6,12,15-tetraoxo-16-[4,7,10-tris- (carboxylatomethyl)-l,4,7,10-tetraazacyclododecan-l-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]acetat, Gadolinium 2,2',2"-( 10- { 1 -carboxy -2-[2-(4-ethoxyphenyl)ethoxy]ethyl} - 1 ,4,7, 10- tetraazacyclododecan- 1,4,7 -triyljtriacetat, Gadolinium 2,2',2"-{ 10-[l-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-l,4,7,10- tetraazacyclododecan-l,4,7-triyl}triacetat, Gadolinium 2,2',2"-{ 10-[(lR)-l-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]- 1,4,7, 10-tetraazacyclododecan- 1 ,4,7-triyl [triacctat. Gadolinium (2S,2'S,2"S)-2,2',2"-{ 10-[(lS)-l-carboxy-4-{4-[2-(2-ethoxyethoxy)ethoxy] phenyl [butyl] - 1 ,4,7, 10-tetraazacyclododecan- 1 ,4,7-triyl} tris (3 -hydroxypropanoat), Gadolinium 2,2',2"- { 10- [( 1 S)-4-(4-butoxyphenyl)- 1 -carboxybutyl] - 1 ,4,7, 10- tetraazacyclododecan-l,4,7-triyl}triacetat, Gadolinium-2,2',2"- { (25)- 10-(carboxymethyl)-2-[4-(2-ethoxyethoxy)benzyl] -1,4,7,10- tetraazacyclododecane- 1 ,4,7-triyl [triacctat. Gadolinium-2,2',2"- [ 10-(carboxymethyl)-2-(4-ethoxybenzyl)- 1 ,4,7, 10-tetraazacyclododecane- 1.4.7-triyl]triacetat, Gadolinium(III) 5,8-bis(carboxylatomethyl)-2-[2-(methylamino)-2-oxoethyl]-10-oxo-2,5,8,l 1- tetraazadodecan- 1 -carboxylat-Hydrat, Gadolinium(III) 2-[4-(2-hydroxypropyl)-7,10-bis(2-oxido-2-oxoethyl)-l,4,7,10- tetrazacyclododec- 1 -yl]acetat, Gadolinium(III) 2,2',2"-( 10-((2R,3 S)- 1 ,3 ,4-trihydroxybutan-2-yl)- 1,4,7, 10-tetraazacyclododecan- 1.4.7-triyl)triacetate, Gd 3+ -Complex of a compound of formula (I) (I) , where Ar a group selected from where # represents the link to X, X represents a group consisting of CH2, (CH2)2, (CH2)S, (CH2)4 and *-(CH2)2-O-CH2- # is selected where * represents the connection to Ar and # which represents the connection to the acetic acid residue, R 1 , R 2 and R 3 independently represent a hydrogen atom or a group selected from Ci-Cs-alkyl, -CH2OH, -(CH2)2OH and -CH2OCH3, R 4 represents 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 6 represents a hydrogen atom or a stereoisomer, tautomer, hydrate, solvate or salt thereof, or a mixture thereof, Gd 3+ -Complex of a compound of formula (II) (II) , where Ar a group selected from where # represents the link to X, X represents a group consisting of QU, (CH2)2, (CH2)3, (CH2)4 and *-(CH2)2-O-CH2- # is selected, where * represents the link to Ar and # which represents the connection to the acetic acid residue, R 7 represents a hydrogen atom or a group selected from Ci-Cs-alkyl, -CH2OH, -(CH2)2OH and -CH2OCH3; R 8 represents 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 10 represent a hydrogen atom independently of each other; or a stereoisomer, tautomer, hydrate, solvate or salt thereof, or a mixture thereof.

11. Method according to any one of claims 1 to 10, wherein the subject of investigation and each reference object is a mammal, wherein the area of ​​investigation and the reference area of ​​each reference object is or comprises a stomach, brain, heart, kidney, lung, stomach, intestine, pancreas, thyroid gland, prostate gland and / or breast, wherein the reference area is the same for all reference objects and the area of ​​investigation corresponds to the reference area.

12. Method according to any one of claims 1 to 11, wherein the first quantity is zero, the second quantity is equal to a standard quantity of the contrast agent, and the third quantity is greater than the standard quantity, or the first set is zero, the second set is smaller than the standard set, and the third set is equal to or larger than the standard set, or the first set is smaller than the standard set, the second set is smaller than the standard set, and the third set is equal to or larger than the standard set, or the first set is smaller than the standard set, the second set is equal to the standard set, and the third set is larger than the standard set, or the first set is smaller than the standard set, the second set is equal to the standard set, and the third set is zero, or The first set is equal to the standard set, the second set is smaller or larger than the standard set, and the third set is zero.

13. Computer system (10) comprising a receiving unit (11), a control and calculation unit (12) and an output unit (13), wherein the control and computing unit (12) is configured to execute the method according to any one of claims 1 to 12.

14. Computer program product comprising a computer program that can be loaded into the main memory of a computer system and causes the computer system to execute the method according to any one of claims 1 to 12.

15. Kit comprising a contrast agent and a computer program product according to claim 14.

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