Determining a signal-to-noise ratio in a radiological image

Abstract

The systems, methods, and computer programs disclosed herein relate to determining a signal-to-noise ratio in a radiological image.

Description

[0001] Determining a signal-to-noise ratio in a radiological image

[0002] TECHNICAL FIELD

[0003] The systems, methods, and computer programs disclosed herein relate to determining a signal-to-noise ratio in a radiological image.

[0004] INTRODUCTION

[0005] WO2019 / 074938A1 discloses a method for reducing the amount of contrast agent when generating radiological images using an artificial neural network.

[0006] In the disclosed method, a training dataset is generated in a first step. The training dataset includes, for each person in a plurality of persons, i) a native radiological image (zero-contrast image), ii) a radiological image after the application of a small amount of contrast agent (low-contrast image), and iii) a radiological image after the application of a standard amount of contrast agent (full-contrast image).

[0007] In a second step, an artificial neural network is trained to predict an artificial radiographic image for each person in the training dataset based on the native radiographic image and the radiographic image after application of the small amount of contrast agent. This image shows an uptake area after application of the standard amount of contrast agent. The measured radiographic image after application of the standard amount of contrast agent serves as the reference (ground truth) during training.

[0008] In a third step, the trained artificial neural network can be used to predict an artificial radiological image for a new person based on a native radiological image and a radiological image after the application of the small amount of contrast agent, which shows the acquired area as it would look if the standard amount of contrast agent had been applied.

[0009] The method disclosed in WO2019 / 074938A1 has disadvantages.

[0010] Training data is required to train the artificial neural network. A large number of radiological examinations must be performed on a large number of individuals, and the training data must be generated to train the network.

[0011] The artificial neural network disclosed in WO2019 / 074938A1 is trained to predict a radiological image after the application of a standard amount of contrast agent. The artificial neural network is not configured or trained to predict a radiological image after the application of a smaller or larger amount of contrast agent than the standard amount. The method described in WO2019 / 074938A1 can, in principle, be trained to predict a radiological image after the application of a different amount of contrast agent than the standard amount—however, this requires additional training data and further training.

[0012] It is possible to generate radiological images with variable contrast enhancement. One approach involves subtracting a native radiological image of an examination area of ​​a subject from a radiological image of the examination area of ​​the subject, image element by image element, multiplying the result of the subtraction image element by image element by a variable enhancement factor, and adding the result of the multiplication image element by image element to the native radiological image. This approach is described, for example, in: AF Mingo et al.: Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data, Investigative Radiology 58(12), 2023, 853-864. The approach is used in the cited publication to generate artificial radiological images for training a machine learning model.A disadvantage of this approach is that in addition to the contrast agent signal, noise is also amplified to the same extent.

[0013] WO2024 / 052156A1 describes how noise can be suppressed: the subtraction of the native radiological image from the contrast-enhanced radiological image can be performed in the frequency domain, and the result of the subtraction can be multiplied by a frequency-dependent weighting function before being multiplied by an amplification factor and added to the native radiological image. The weighting function can weight low frequencies higher than higher frequencies. Contrast information is represented by low frequencies in a frequency-domain representation, while the higher frequencies represent information about fine structures. Image noise is typically evenly distributed in the frequency representation. The frequency-dependent weighting function acts as a filter. The filter increases the signal-to-noise ratio by reducing the spectral noise density for high frequencies.

[0014] The signal-to-noise ratio is not the same for all radiological images, but depends on a variety of factors. To determine a suitable measure to increase the signal-to-noise ratio, it would be advantageous to know the signal-to-noise ratio in each individual case.

[0015] SUMMARY

[0016] The above-mentioned problems and further problems are addressed by the subject matter of the independent claims of the present disclosure. Preferred embodiments can be found in the dependent claims, the description, and the drawings.

[0017] A first subject of the present disclosure is a computer-implemented method comprising:

[0018] Receiving or generating a first representation, wherein the first representation represents an examination area of ​​an examination object without contrast agent or after application of a first amount of contrast agent,

[0019] Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount,

[0020] Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation,

[0021] Generating a histogram of the third representation,

[0022] Approximating the histogram by a sum of at least two distribution functions,

[0023] Determining a signal-to-noise ratio based on parameters of at least two distribution functions,

[0024] Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio when generating a synthetic representation of the examination area of ​​the examination object. A further subject of the present disclosure is a computer system comprising:

[0025] • an input unit,

[0026] • a control and computing unit and

[0027] • an output unit, wherein the control and computing unit is configured to receive or generate a first representation, wherein the first representation represents an examination area of ​​an examination object without contrast agent or after application of a first amount of a contrast agent, to receive or generate a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount, to generate a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation, to generate a histogram of the third representation, to approximate the histogram by a sum of at least two distribution functions,to determine a signal-to-noise ratio based on parameters of the at least two distribution functions, to output and / or store the signal-to-noise ratio and / or to transmit it to a separate computer system and / or to use it in generating a synthetic representation of the examination area of ​​the examination object.

[0028] Another subject of the present disclosure is a non-transitory computer-readable storage medium having stored thereon software instructions that, when executed by a processor of a computer system, cause the computer system to perform the following:

[0029] Receiving or generating a first representation, wherein the first representation represents an examination area of ​​an examination object without contrast agent or after application of a first amount of contrast agent,

[0030] Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount,

[0031] Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation,

[0032] Generating a histogram of the third representation,

[0033] Approximating the histogram by a sum of at least two distribution functions,

[0034] Determining a signal-to-noise ratio based on parameters of at least two distribution functions,

[0035] Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio when generating a synthetic representation of the examination area of ​​the examination object. A further subject of the present disclosure is the use of a contrast agent in a radiological examination method comprising:

[0036] Receiving or generating a first representation, wherein the first representation represents an examination region of an examination object without contrast agent or after application of a first amount of the contrast agent,

[0037] Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount,

[0038] Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation,

[0039] Generating a histogram of the third representation,

[0040] Approximating the histogram by a sum of at least two distribution functions,

[0041] Determining a signal-to-noise ratio based on parameters of at least two distribution functions,

[0042] Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio in generating a synthetic representation of the examination area of ​​the examination object.

[0043] Another subject of the present disclosure is a contrast agent for use in a radiological examination method comprising:

[0044] Receiving or generating a first representation, wherein the first representation represents an examination region of an examination object without contrast agent or after application of a first amount of the contrast agent,

[0045] Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount,

[0046] Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation,

[0047] Generating a histogram of the third representation,

[0048] Approximating the histogram by a sum of at least two distribution functions,

[0049] Determining a signal-to-noise ratio based on parameters of at least two distribution functions,

[0050] Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio in generating a synthetic representation of the examination area of ​​the examination object.

[0051] A further subject of the present disclosure is a kit comprising a computer program product and a contrast agent, wherein the computer program product comprises a computer program that can be loaded into a working memory of a computer system and causes the computer system to carry out the following: receiving or generating a first representation, wherein the first representation represents an examination area of ​​an examination object without contrast agent or after application of a first amount of the contrast agent,

[0052] Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount,

[0053] Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation,

[0054] Generating a histogram of the third representation,

[0055] Approximating the histogram by a sum of at least two distribution functions,

[0056] Determining a signal-to-noise ratio based on parameters of at least two distribution functions,

[0057] Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio in generating a synthetic representation of the examination area of ​​the examination object.

[0058] BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Fig. 1 shows, by way of example and schematically, the generation of a third representation of an examination area of ​​an examination object on the basis of a first representation and a second representation of the examination area of ​​the examination object, the generation of a histogram of the third representation and the approximation of the histogram by a mathematical function.

[0060] Fig. 2 shows an example and schematically the application of a noise filter to the third representation.

[0061] Fig. 3 shows, by way of example and schematically, the generation of a fourth representation based on a third representation and a first representation.

[0062] Fig. 4 shows examples of frequency-dependent weight functions that can be used to weight the third representation.

[0063] Fig. 5 schematically shows an embodiment of the computer-implemented method of the present disclosure in the form of a flowchart.

[0064] Fig. 6 shows an exemplary and schematic illustration of an embodiment of the computer system of the present disclosure.

[0065] Fig. 7 shows an exemplary and schematic illustration of another embodiment of the computer system of the present disclosure.

[0066] DETAILED DESCRIPTION

[0067] The subject matter of the present disclosure is explained in more detail below, without distinguishing between the subject matter of the present disclosure (method, computer system, computer-readable storage medium, use, contrast agent for use, kit). Rather, the following statements apply mutatis mutandis to all subject matter of the present disclosure, regardless of the context in which they are described (method, computer system, computer-readable storage medium, use, contrast agent for use, kit).

[0068] If steps are specified in a particular order in this description or in the claims, this does not mean that the present disclosure is limited to the specified order. Rather, it is conceivable that the steps could also be performed in a different order or even in parallel, unless, for example, one step builds on another, which requires that the subsequent step be performed (although this will become clear in the individual case). The specified orders are thus exemplary embodiments of the present disclosure.

[0069] The subject matter of the present disclosure is explained in more detail at some points with reference to drawings. The drawings depict specific embodiments with specific features and combinations of features, which primarily serve for illustrative purposes; the drawings should not be understood to imply that the present disclosure is limited to the features and combinations of features depicted in the drawings. Furthermore, statements made in the description of the drawings regarding features and combinations of features are intended to apply generally, meaning they are also transferable to other embodiments and are not limited to the embodiments shown.

[0070] The terms used in this disclosure have the meaning that such terms have in the prior art, particularly in the prior art referred to in this disclosure, unless otherwise specified in this description.

[0071] One subject matter of the present disclosure is a computer-implemented method. A computer-implemented method is characterized in that the steps constituting the method are executed on one or more computers and / or computer systems and / or by one or more computers or computer systems.

[0072] In a first step, a first representation and a second representation are received or generated.

[0073] The term "receiving" encompasses both retrieving representations and receiving representations that are transmitted, for example, to the computer system of the present disclosure. The representations can be received, for example, from a computer tomography scanner, a magnetic resonance imaging scanner, an ultrasound scanner, or a PET scanner. The representations can, for example, be read from one or more data storage devices and / or transmitted from a separate computer system.

[0074] The term “generate” can mean that a representation is generated, preferably calculated, on the basis of another (e.g., a received) representation or on the basis of several other (e.g., received) representations. For example, a received representation can be a representation of an examination region of an examination object in the spatial domain. On the basis of this spatial domain representation, for example, a representation of the examination region of the examination object can be generated in the frequency domain by means of a transformation (e.g., a Fourier transform). The received representation can also be a representation of an examination region of an examination object in the frequency domain. On the basis of this frequency domain representation, for example, a representation of the examination region of the examination object can be generated in the spatial domain by means of a transformation (e.g., an inverse Fourier transform).Further possibilities for creating a representation based on one or more other representations are described in this description.

[0075] The term "generate" can also mean that a representation is created by measurement technology on the examination subject, for example, in a radiological examination procedure. The first representation represents an examination area of ​​an examination subject without contrast agent or after application of a first amount of contrast agent. The second representation represents the examination area of ​​the examination subject after application of a second amount of contrast agent, where the second amount is larger than the first amount.

[0076] The "object of investigation" is typically a living being, such as a mammal, such as a human. In one embodiment of the present disclosure, the object of investigation is a human.

[0077] The “area of ​​investigation” is a part of the object of investigation, for example an organ or a part of an organ or several organs or another part of the object of investigation.

[0078] The area of ​​investigation may, for example, be an intestine, 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 a part thereof or another part of the body of a mammal (e.g. a human).

[0079] In one embodiment, the examination region comprises a liver or a part of a liver or the examination region is a liver or a part of a liver of a mammal, for example a human.

[0080] In a further embodiment, the examination area comprises a brain or a part of a brain or the examination area is a brain or a part of a brain of a mammal, for example a human.

[0081] In a further embodiment, the examination region comprises a heart or a part of a heart or the examination region is a heart or a part of a heart of a mammal, for example a human.

[0082] In a further embodiment, the examination region comprises a thorax or a part of a thorax or the examination region is a thorax or a part of a thorax of a mammal, for example a human.

[0083] In a further embodiment, the examination region comprises a stomach or a part of a stomach or the examination region is a stomach or a part of a stomach of a mammal, for example a human.

[0084] In a further embodiment, the examination region comprises a thyroid or a part of a thyroid or the examination region is a thyroid or a part of a thyroid of a mammal, for example a human.

[0085] In a further embodiment, the examination area comprises a uterus or a part of a uterus or the examination area is a uterus or a part of a uterus of a female mammal, for example a female human.

[0086] In a further embodiment, the examination region comprises a pancreas or a part of a pancreas or the examination region is a pancreas or a part of a pancreas of a mammal, for example a human.

[0087] In a further embodiment, the examination region comprises a kidney or a part of a kidney or the examination region is a kidney or a part of a kidney of a mammal, for example a human.

[0088] In a further embodiment, the examination area comprises one or both lungs or part of a lung of a mammal, for example a human.

[0089] In a further embodiment, the examination region comprises a breast or part of a breast, or the examination region is a breast or part of a breast of a female mammal, for example, a female human. In a further embodiment, the examination region comprises a prostate or part of a prostate, or the examination region is a prostate or part of a prostate of a male mammal, for example, a male human.

[0090] The examination area, also called the field of view (FOV), represents a volume depicted in radiological images. The examination area is typically defined by a radiologist, for example, on an overview image. Alternatively or additionally, the examination area can also be defined automatically, for example, based on a selected protocol.

[0091] In one embodiment of the present disclosure, the first representation and the second representation are radiological images. In other words, the first representation and the second representation are the result of one or more radiological examinations.

[0092] "Radiology" is the branch of medicine that deals with the application of electromagnetic radiation and (including ultrasound diagnostics, for example) mechanical waves for diagnostic, therapeutic, and / or scientific purposes. In addition to X-rays, other ionizing radiation such as gamma radiation or electrons is also used. Since a key application is imaging, other imaging techniques such as sonography and magnetic resonance imaging (MRI) are also considered radiology, even though these techniques do not involve the use of ionizing radiation. The term "radiology" within the meaning of this disclosure thus encompasses, in particular, the following examination methods: computed tomography (CT), magnetic resonance imaging (MRI), sonography, and positron emission tomography (PET).

[0093] In one embodiment, the first representation and the second representation are CT scans.

[0094] In a further embodiment, the first representation and the second representation are MRI images.

[0095] In a further embodiment, the first representation and the second representation are ultrasound images.

[0096] In a further embodiment, the first representation and the second representation are PET images.

[0097] In radiological examinations, contrast agents are often used to enhance contrast.

[0098] “Contrast agents” are substances or mixtures of substances that enhance the visualization of structures and functions of the body during radiological examinations.

[0099] In computed tomography, iodine-containing solutions are typically used as contrast agents. In magnetic resonance imaging (MRI), superparamagnetic substances (e.g., iron oxide nanoparticles, superparamagnetic iron-platinum particles (SIPPs)) or paramagnetic substances (e.g., gadolinium chelates, manganese chelates, hafnium chelates) are commonly used as contrast agents. In sonography, fluids containing gas-filled microbubbles are typically administered intravenously. Examples of contrast agents can be found in the literature (see e.g. ASL Jascinth et al.: Contrast Agents in computed tomography: A Review, Journal of Applied Dental and Medical Sciences, 2016, Vol. 2, Issue 2, 143 - 149; H. Lusic et al.: X-ray- Computed Tomography Contrast Agents, Chem. Rev. 2013, 113, 3, 1641-1666; https: / / www.radiology.wisc.edu / wp-content / uploads / 2017 / 10 / contrast-agents-tutorial.pdf, MR Nough et al.: Radiographic and magnetic resonances contrast agents: Essentials and tips for safe practices, World J Radiol. 2017 Sep 28; 9(9): 339-349; L. C. Abonyi et al.: Intravascular Contrast Media in Radiography: Historical Development & Review of Risk Factors for Adverse Reactions, South American Journal of Clinical Research, 2016, Vol. 3, Issue 1, 1-10; ACR Manual on Contrast Media, 2020, ISBN: 978-1-55903-012-0; A. Ignee et al.: Ultrasound contrast agents , Endosc Ultrasound. 2016 Nov-Dec; 5(6): 355-362).

[0100] 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: paramagnetic and superparamagnetic substances. Both groups of substances have unpaired electrons that induce a magnetic field around the individual atoms or molecules. Superparamagnetic contrast agents lead to a predominant T2 shortening, while paramagnetic contrast agents essentially lead to a T1 shortening. The effect of these contrast agents is indirect, 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 iron oxide nanoparticles (SPIO). Examples of paramagnetic contrast agents are gadolinium chelates such as gadopentetate dimeglumine (trade name: Magnevist®, etc.).), gadoteric acid (Dotarem®, Dotagita®, Cyclolux®), gadodiamide (Omniscan®), gadoteridol (ProHance®), gadobutrol (Gadovist®), gadopiclenol (Elucirem, Vueway) and gadoxetic acid (Primovist® / Eovist®) .

[0101] The first representation represents an examination region of an examination subject without contrast agent or after application of a first amount of contrast agent. In one embodiment of the present disclosure, the first representation represents the examination region without contrast agent.

[0102] The second representation represents the examination area of ​​the examination object after application of a second amount of contrast agent, whereby the second amount is greater than the first amount (whereby the first amount can also be zero as described).

[0103] The expression "after application of the second amount of contrast agent" should not be understood to mean that the first amount and the second amount in the examination region are added together. The expression "the representation represents the examination region after application of a (first or second) amount" should rather mean: "the representation represents the examination region with a (first or second) amount" or "the representation represents the examination region comprising a (first or second) amount."

[0104] In one embodiment of the present disclosure, both the first amount and the second amount of contrast agent are less than the standard amount.

[0105] In a further embodiment, the second amount of contrast agent corresponds to the standard amount.

[0106] In another embodiment, the first amount of contrast agent is zero and the second amount of contrast agent is less than the standard amount.

[0107] In a further embodiment, the first amount of contrast agent is zero and the second amount of contrast agent is the standard amount.

[0108] The standard amount is usually the amount recommended by the manufacturer and / or distributor of the contrast agent and / or the amount approved by a regulatory authority and / or the amount listed in a package insert accompanying the contrast agent.

[0109] For example, the standard amount of Primovist® is 0.025 mmol Gd-EOB-DTPA disodium / kg body weight.

[0110] The radiological examination procedure and the contrast agent used can be independent of each other. This means that, for example, the first representation can be a CT scan representing the examination area without contrast agent or after application of a first amount of MRI contrast agent, while the second representation can be a CT scan representing the examination area after application of a second amount of MRI contrast agent.

[0111] Preferably, the contrast agent is a contrast agent comprising one or more of the following substances:

[0112] Gadolinium(III) 2-[4, 7, 10-tris(carboxymethyl)-1,4,7, 10-tetrazacyclododec-1-yl]acetic acid,

[0113] Gadolinium(III) ethoxybenzyl-diethylenetriaminepentaacetic acid,

[0114] 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 -tri e n- 6 -y 1] - 5 - (2 , 3 -dihydroxypropylamino)-5 - oxopentanoat,

[0115] Dihydrogen[(±)-4-carboxy-5 , 8, 11 -tris(carboxymethyl)- 1 -phenyl-2-oxa-5 , 8, 11 -triazatridecan- 13 - oato(5-)]gadolinat(2-),

[0116] 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] acetat, 2,2',2"-(10-{ l -carboxy-2-[2-(4-ethoxyphenyl)ethoxy] ethyl} - 1 ,4,7, 10-tetraazacyclododecan- 1,4,7- triyl)triacetat,

[0117] Gadolinium 2,2',2"-{ 10-[l-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-l,4,7,10- tetraazacyclododecan- 1 ,4,7-triyl}triacetat,

[0118] Gadolinium 2,2',2"-{ 10-[(lR)-l-carboxy-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-

[0119] 1 ,4,7, 10-tetraazacyclododecan- 1 ,4,7-triyl}triacetat,

[0120] 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)

[0121] Gadolinium 2,2',2"-{ 10-[(lS)-4-(4-butoxyphenyl)-l -carboxybutyl] -1,4, 7,10- tetraazacyclododecan-l,4,7-triyl}triacetat,

[0122] Gadolinium(III) 5,8-bis(carboxylatomethyl)-2-[2-(methylamino)-2-oxoethyl]-10-oxo-2,5,8,l 1- tetraazadodecan- 1 -carboxylat-Hydrat

[0123] Gadolinium(III) 2-[4-(2-hydroxypropyl)-7, 10-bis(2-oxido-2-oxoethyl)-l,4,7, 10- tetrazacyclododec- 1 -yl] acetat,

[0124] Gadolinium(III) 2,2',2"-( 10-((2R,3S)- 1,3,4-trihydroxybutan-2-yl)- 1,4,7, 10-tetraazacyclododecane-

[0125] 1,4,7-triyl)triacetate,

[0126] Gadolinium -2, 2', 2"- { (25)- 10-(carboxymethyl)-2-[4-(2-ethoxyethoxy)benzyl] -1,4,7,10-tetraazacyclododecane-1,4,7-triyl}triacetate,

[0127] Gadolinium -2, 2', 2"-[10-(carboxymethyl)-2-(4-ethoxybenzyl)-1,4,7, 10-tetraazacyclododecane-

[0128] 1.4.7-triyl]triacetate.

[0129] The first representation and the second representation can be representations of the

[0130] Investigation area of ​​an object under investigation in the local area.

[0131] The first representation and the second representation can be representations of the

[0132] Examination area of ​​an object under investigation in the frequency space.

[0133] The “local space” is the usual three-dimensional Euclidean space, which corresponds to the space that we humans experience with our senses and in which we move. A representation in local space is therefore the representation that is familiar to humans. In a representation in local space, also referred to in this description as a local space representation or local space representation, the area under investigation is usually represented by a large number of image elements (e.g. pixels or voxels), which can be arranged in a grid, for example, with each image element representing a part of the area under investigation. Each image element can be assigned a color value or gray value. The color value or gray value usually represents the signal intensity of a measured signal.

[0134] A widely used format in radiology for storing and processing representations in spatial space 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.

[0135] The “frequency space” is a domain in which a signal is considered as the sum of its individual frequency components.

[0136] In a frequency-space representation, also referred to in this description as a frequency-space representation 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 / or cosine functions with different amplitudes, frequencies, and phases. The amplitudes and phases can be plotted as a function of frequency, for example in a two- or three-dimensional representation. Typically, the lowest frequency (origin) is placed in 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 representation, and a phase, which indicates the extent to which the respective oscillation is shifted compared to a sine or cosine oscillation.

[0137] The raw data generated during magnetic resonance imaging examinations (so-called k-space data) is an example of a representation in the frequency domain. Such raw data (k-space data) from magnetic resonance imaging examinations can be used as a first and / or second representation within the meaning of the present disclosure.

[0138] A representation in spatial space can be converted (transformed) into a representation in frequency space, for example, using a Fourier transform. Conversely, a representation in frequency space can be converted (transformed) into a representation in spatial space, for example, using an inverse Fourier transform.

[0139] Details about position-space representations and frequency-space representations and their respective conversion into each other are described in numerous publications, see for example: https: / / see.stanford.edu / materials / lsoftaee261 / book-fall-07.pdf.

[0140] Based on the first representation and the second representation, a third representation is generated. The third representation can be generated by subtracting the first representation from the second representation.

[0141] Subtraction can be performed in the spatial domain or the frequency domain. In the spatial domain, for example, the gray values ​​or color values ​​of corresponding image elements are subtracted from each other. Corresponding image elements are those that represent the same sub-area of ​​the examination area.

[0142] A histogram is generated from the third representation.

[0143] The histogram indicates the frequency of occurrence of different difference values, difference levels, or difference ranges within the third representation. For example, if the first representation and the second representation are grayscale images, subtracting the first representation from the second representation creates a third image-like representation in which, for each image element, the corresponding difference between the gray value of the first representation and the gray value of the second representation is specified. The histogram of the third representation can then indicate how often the individual differences in the gray values ​​occur in the third representation.

[0144] In a further step, a computation (also called adjustment) is performed. This means that the histogram is approximated using a mathematical function. In other words, a mathematical function is determined that describes the histogram.

[0145] The adaptation / approximation of the histogram of the third representation by a mathematical function can be carried out by known methods of adjustment calculation such as regression and / or fitting.

[0146] The histogram of the third representation typically has two peaks: a first peak and a second peak. The first peak typically has its maximum at a difference value of zero.

[0147] The width of the first peak reflects the noise in the third representation. The wider the peak, the greater the noise is typically; the narrower the peak, the lower the noise is typically.

[0148] The second peak is caused by the different amounts of contrast agent in the first and second representations. The maximum of the second peak is usually further away from the maximum of the first peak, the greater the difference in signal intensity caused by the different amounts of contrast agent in the first and second representations.

[0149] The peaks are explained in more detail later in the description with reference to the example shown in Fig. 1.

[0150] The mathematical function used to describe (approximate) the histogram is a sum of at least two distribution functions, where each of the two distribution functions represents one of the peaks.

[0151] In other words, the mathematical function used to describe (approximate) the histogram comprises a first distribution function and a second distribution function. The first distribution function represents the difference in the signal intensities of those image elements of the first and second representations whose signal intensities are independent of the amount of contrast agent. The second distribution function represents the difference in the signal intensities of those image elements of the first and second representations whose signal intensities are dependent on the amount of contrast agent. The histogram of the third representation has two peaks, a first peak and a second peak. The first distribution function approximates the first peak, and the second distribution function approximates the second peak.

[0152] The distribution functions can, for example, be Gaussian normal distributions (Gaussian bell curves), which are uniquely defined by an expected value and a standard deviation as function parameters.

[0153] Other functions are also possible.

[0154] In a further step, at least one signal-to-noise ratio is determined based on function parameters of at least two distribution functions.

[0155] The determined signal-to-noise ratio is a measure of the ratio of a desired signal to unwanted noise in the third representation.

[0156] The determined signal-to-noise ratio can be a quotient of two parameters, where the dividend can be a measure of the distance of the second peak from the first peak and the divisor can be a measure of the width of the first peak.

[0157] The dividend can be, for example, the distance between the maximum value of the second peak and the maximum value of the first peak. The divisor can be, for example, the half-width (width at half height) of the first peak.

[0158] The determined signal-to-noise ratio can be output (e.g. displayed on a monitor and / or printed out with a printer) and / or stored in a data memory.

[0159] The determined signal-to-noise ratio can be used to generate a synthetic representation of the examination area of ​​the object under investigation.

[0160] The determined signal-to-noise ratio can be used to determine a measure to increase the signal-to-noise ratio.

[0161] The determined signal-to-noise ratio can be used to select a frequency-dependent weighting function with which a frequency-space representation of the third representation can be multiplied to increase the signal-to-noise ratio.

[0162] The determined signal-to-noise ratio can be used to determine values ​​for one or more parameters of a frequency-dependent weighting function with which a frequency-space representation of the third representation can be multiplied to increase the signal-to-noise ratio.

[0163] The determined signal-to-noise ratio can be used to determine whether an increase in the signal-to-noise ratio is necessary at all or not. The determined signal-to-noise ratio can be compared to a predefined threshold. If the determined signal-to-noise ratio is greater than or equal to the threshold, the signal-to-noise ratio may already be so "good" that (further) noise reduction is not necessary. If the determined signal-to-noise ratio is less than the threshold, (further) noise reduction may be necessary.

[0164] Thresholds may be defined (set) by the user of the computer system of the present disclosure and / or a radiologist.

[0165] The determined signal-to-noise ratio can be used to determine an enhancement factor. The third representation or a weighted third representation can be multiplied by an enhancement factor and added to the first and / or second representation to generate a fourth representation. The enhancement factor can indicate the extent to which the signal intensities induced by the contrast agent in the second representation should be attenuated or enhanced in the fourth representation.

[0166] The determined signal-to-noise ratio can be used to determine the maximum enhancement of the contrast agent-attributable signal intensities, which can be achieved by multiplying the third representation by a gain factor. In other words, the determined signal-to-noise ratio can be used to determine a maximum gain factor.

[0167] The determined signal-to-noise ratio can be used to determine whether amplification of the signal intensities induced by contrast agents is even appropriate. For example, if the signal-to-noise ratio is low, signal amplification (even after noise suppression) may not be appropriate because the resulting representation is too noisy to be used for diagnostic and / or therapeutic purposes. If the signal-to-noise ratio is lower than a predefined threshold, a message can be issued indicating that signal amplification is not appropriate or possible.

[0168] In a further step, a fourth representation of the examination area of ​​the examination object can be generated in which the signal intensities attributable to contrast agents are enhanced compared to the first representation and the second representation.

[0169] For this purpose, a third representation of the examination area of ​​the examination object in the frequency domain can be multiplied by a frequency-dependent weighting function, and the result of the multiplication can be added to the first and / or the second representation. The previously determined signal-to-noise ratio can be used as described (i) to determine whether such a multiplication by a frequency-dependent weighting function is necessary at all, (ii) to determine whether the signal-to-noise ratio is so low that amplification of the signal intensities attributable to contrast agents does not appear to be appropriate, (iii) to select a frequency-dependent weighting function, (iv) to determine one or more values ​​for one or more parameters of the frequency-dependent weighting function, (v) to determine an amplification factor, and / or (vi) to determine a maximum amplification factor.

[0170] Before multiplying the third representation with the frequency-dependent weighting function, the third representation can be normalized. This means that the amplitude and / or phase values ​​of the individual frequencies can be multiplied by a factor so that the amplitude / phase with the highest value is represented, for example, by the hue white, and the amplitude / phase with the lowest value is represented, for example, by the hue black. It is also possible that, in such a normalization, negative values ​​that may arise when subtracting the first representation from the second representation are set to zero (or another value) to avoid negative values.

[0171] In a frequency-dependent weighting function, each frequency is assigned a weighting factor. If the weighting factor of a particular frequency is zero, for example, when the third representation in the frequency domain is multiplied by the frequency-dependent weighting function, the amplitude of the corresponding frequency in the third representation is set to zero, i.e., the frequency is eliminated. If the weighting factor of a particular frequency is one, for example, when the third representation in the frequency domain is multiplied by the frequency-dependent weighting function, the amplitude of the corresponding frequency in the third representation remains unchanged; i.e., the frequency remains unchanged. If the weighting factor of a particular frequency is 0.5, for example, when the third representation in the frequency domain is multiplied by the frequency-dependent weighting function, the amplitude of the corresponding frequency is reduced to half its value; i.e.,The corresponding frequency is attenuated in the third representation in the frequency domain. If the weighting factor of a particular frequency is, for example, two, the amplitude of the corresponding frequency is doubled when the third representation in the frequency domain is multiplied by the frequency-dependent weighting function; ie, the corresponding frequency is amplified in the third representation in the frequency domain.

[0172] In the frequency-dependent weighting of the third representation, the amplitudes of the lower frequencies are preferably multiplied by a higher weighting factor than the amplitudes of the higher frequencies. In a preferred embodiment, the higher the frequencies, the lower the weighting factor is.

[0173] Examples of frequency-dependent weight functions are shown in Fig. 4. These are also described in WO2024 / 052156A1, the content of which is incorporated in its entirety into the present disclosure by this reference.

[0174] It is therefore possible to add the optionally weighted third representation multiplied by an amplification factor to the first representation, where the amplification factor indicates the extent to which the contrast is increased in the fourth representation. It is also possible to choose an amplification that is less than 1, i.e. the contrast between areas with contrast agent and areas without contrast agent is lower in the fourth representation than in the second representation. It is also possible to achieve an amplification that is greater than the contrast enhancement caused by a standard amount of contrast agent. Such contrast enhancement is not possible with the method described in WO2019 / 074938A1 without applying an amount of contrast agent to people to generate the training data that is higher than the standard amount and thus lies outside the range approved by the approval authority.

[0175] The gain factor is therefore a positive or negative real number. The gain factor can be user-selected, meaning it can be variable, or predefined, meaning it can be specified.

[0176] The fourth representation can also be subjected to normalization.

[0177] If the fourth representation is a frequency space representation, the fourth representation can be converted into a position space representation by a transformation (e.g. an inverse Fourier transform).

[0178] The fourth representation in the spatial space can be output (ie displayed on a monitor and / or printed with a printer), stored in a data storage device and / or transmitted to a separate computer system.

[0179] The fourth representation is also referred to in this disclosure as a synthetic representation. The term "synthetic" means that the synthetic representation is not the (direct) result of a physical measurement on a real object under investigation, but rather that the synthetic representation is generated by a calculation based on the first representation and the second representation. A synonym for the term "synthetic" is the term "artificial."

[0180] The invention is explained in more detail below with reference to drawings, without intending to limit the invention to the features and combinations of features shown in the drawings. Statements made with reference to a drawing are intended to apply generally, i.e., not to be limited to the embodiment shown.

[0181] Fig. 1 shows, by way of example and schematically, the generation of a third representation of an examination area of ​​an examination object on the basis of a first representation and a second representation of the examination area of ​​the examination object, the generation of a histogram of the third representation and the approximation of the histogram by a mathematical function.

[0182] The examination area shown in Fig. 1 includes the liver of a pig.

[0183] A first representation (RI) represents the examination area in spatial space without contrast agent. The first representation (RI) is a magnetic resonance imaging (MRI) image.

[0184] The second representation R2 represents the same examination area of ​​the same examination object as the first representation RI, also in spatial space. The second representation R2 is also a magnetic resonance imaging (MRI) image.

[0185] The second representation R2 represents the examination area after application of a quantity of contrast agent.

[0186] Based on the first representation RI and the second representation R2, a third representation R3 of the examination area of ​​the examination object is generated. In the example shown in Fig. 1, the third representation R3 is generated by subtracting the first representation RI from the second representation R2 (R3 = R2 - RI). The subtraction affects the gray values ​​of the individual image elements. The subtraction is performed image element by image element, i.e., each gray value of an image element of the first representation is subtracted from the gray value of the corresponding image element of the second representation. Corresponding image elements are image elements that represent the same sub-area of ​​the examination area.

[0187] It should be noted that the generation of the third representation does not necessarily have to be performed in the spatial domain, as shown in Fig. 1; it can also be performed, for example, in the frequency domain or in another space.

[0188] A histogram is generated from the third representation R3. The histogram plots the frequencies (F) of the difference values ​​(7) occurring in the representation R3.

[0189] Two peaks can be seen in the histogram: a first peak P1 and a second peak P2. The histogram is approximated using a mathematical function. In the example shown in Fig. 1, the mathematical function is a sum of two Gaussian bell curves. A first Gaussian bell curve describes the first peak, a second Gaussian bell curve describes the second peak.

[0190] The first peak has its maximum at a difference value of ZPI = 0. Therefore, there are many image elements in the third representation R3 that have a difference value of 0. These image elements are those for which the gray values ​​in the first representation R1 and the second representation R2 are the same. These image elements represent subregions of the examination area where the signal intensities are independent of the applied amount of contrast agent. These are usually the subregions to which no contrast agent has penetrated.

[0191] The first peak (PI) has a defined half-width (FWMH). This half-width (FWMH) is a measure of the number of image elements for which the difference values ​​are not zero, whereby these deviations from zero are not attributable to the administered amounts of contrast agent. The half-width (FWHM) of the first peak (PI) is therefore a measure of the noise.

[0192] The second peak P2 has its maximum at a difference value A>2 that is non-zero. The second peak represents those image elements whose signal intensities were influenced by the amount of contrast agent. The difference value ΔP2 of the maximum corresponds to the mean of the difference values ​​of all those image elements whose signal intensities were influenced by the amount of contrast agent. The greater the distance between the difference value i>2 of the maximum of the second peak and the difference value Tpi of the maximum of the first peak, the more the signal intensities of the image elements are influenced by the contrast agent. The distance between the difference value i>2 of the maximum of the second peak and the difference value Zpi of the maximum of the first peak is a measure of the signal strength in the third representation.

[0193] A measure of the signal-to-noise ratio in the third representation can be the quotient be.

[0194] The signal-to-noise ratio (SNR) can be output, stored, and / or transmitted to a separate computer system.

[0195] The signal-to-noise ratio can be used as described (i) to determine whether such multiplication by a frequency-dependent weighting function is necessary at all, (ii) to determine whether the signal-to-noise ratio is so low that amplification of the signal intensities attributable to contrast agents does not appear reasonable, (iii) to select a frequency-dependent weighting function, (iv) to determine one or more values ​​for one or more parameters of the frequency-dependent weighting function, (v) to determine an amplification factor and / or (vi) to determine a maximum amplification factor.

[0196] Fig. 2 shows an example and schematically the application of a noise filter to the third representation.

[0197] In the third representation R3 shown in Fig. 2 1 This is the third representation R3 already shown in Fig. 1. A superscript I has been added to the reference symbol to indicate that it is a spatial-domain representation. In this example, the noise filtering is performed in the frequency domain. Therefore, the spatial-domain representation R3 1 into a frequency space representation R3 F This can be done, for example, using a Fourier transformation. The superscript F in the reference symbol R3 F indicates that it is a frequency domain representation. Subsequently, the third representation R3 F in the frequency domain with a frequency-dependent weight function WF. Specifically, the amplitude values ​​of the third frequency domain representation R3 Fmultiplied by weight factors wf. The weight factors wf are frequency-dependent, i.e., the weight factors wf are a function of the frequency f. For illustrative purposes, the weight function WF is shown in two-dimensional form in Fig. 2. The weight function WF shows the weight factors wf as a function of the frequency f along one dimension (along the dashed line). Along the dimension perpendicular to the dashed line in the same image plane, the weight function has the same form; it is merely compressed, since the representation R3 F in this example is rectangular but not square.

[0198] The weight function WF determines the amplitudes of low frequencies (in the example shown, the frequencies decrease from the center of the representation R3 Foutwards) are multiplied by a higher weighting factor than the amplitudes of higher frequencies (which are further from the center of the representation R3 F removed); ie, the low frequencies are weighted higher than the higher frequencies. This is in the weighted representation R3 F w This can be seen by the fact that grey values ​​towards the edges of the representation are darker than in the case of the representation R3 F and the overall brightness decreases faster from the center to the outside than in the representation R3 F .

[0199] The weighted representation R3 F w can be subjected to normalization, i.e. the amplitude values ​​can be multiplied by a factor so that the amplitude with the highest value is represented, for example, by the color white and the amplitude with the lowest value is represented, for example, by the color black.

[0200] The signal-to-noise ratio (SNR) described with reference to Fig. 1 can be used in the example shown in Fig. 2 to select the frequency-dependent weighting function (WF). The signal-to-noise ratio (SNR) can be used to specify one or more values ​​of one or more parameters of the frequency-dependent weighting function (WF). For example, a parameter can define the shape of the weighting function (WF). For example, a parameter can define how much the weighting factors (wf) decrease with increasing frequency (f). For example, a parameter can define the maximum weighting factor.

[0201] Fig. 3 shows an example and schematic representation of the generation of a fourth representation based on a third representation and a first representation.

[0202] In the third representation R3 shown in Fig. 3 F w This is the third representation R3 already shown in Fig. 2 F win the frequency domain. In the first representation RI shown in Fig. 3 1 This is the first representation RI already shown in Fig. 1. A superscript I has been added to the reference symbol to indicate that it is a position-space representation. The first representation RI 1 is converted in a first step into a first representation R1 F converted into frequency domain. This can be done, for example, using a Fourier transformation.

[0203] In a further step, the third representation R3 F w in the frequency space a-fold to the first representation R1 F in the frequency domain, a is the gain factor with which the third representation R3 F w in the frequency domain before the result of the multiplication is applied to the first representation R1 F in the frequency domain. Each amplitude value of the third representation R3 F wmultiplied by the value a.

[0204] Multiplication by the gain factor a and addition leads to the fourth representation R4 F . The fourth representation R4 F is a representation of the examination area of ​​the object under investigation in the frequency domain. This is then converted into a fourth representation R4 1 of the object's examination area in spatial space. The conversion can be performed, for example, using an inverse Fourier transform.

[0205] The fourth representation R4 1 of the examination area of ​​the object under investigation in spatial space can be output (e.g., displayed on a monitor and / or printed out with a printer) and / or stored in a data storage device and / or transmitted to a separate computer system. The fourth representation (R4 1 , R4 F) is a synthetic representation of the examination area of ​​the object under investigation. In the example shown in Fig. 3, the fourth representation (R4 1 , R4 F ) a synthetic magnetic resonance imaging (MRI) scan.

[0206] It should be noted that the multiplication of the weighted third representation by the gain factor and / or the addition to the first representation does not necessarily have to take place in the frequency domain, as shown in Fig. 3; it can also take place in the spatial domain or another space.

[0207] Fig. 4 shows examples of frequency-dependent weight functions that can be used to weight the third representation. For simplicity, the weight functions are depicted as two-dimensional graphs, with the weight factors wf (ordinate) plotted as a function of frequency f (abscissa).

[0208] Fig. 4 (a) shows the weight function WF already shown in Fig. 2. In this weight function, the weight factors can, for example, decrease exponentially from the center with increasing frequency.

[0209] Fig. 4 (b) shows a weight function in which the weight factors decrease linearly from the center with increasing frequency.

[0210] Fig. 4 (c) shows a weight function in which the weight factors decrease from the center with increasing frequency in the form of an inverted parabola.

[0211] Fig. 4 (d) shows a weight function in which the weight factors are constant over a defined range around the center and then decrease exponentially above a threshold frequency.

[0212] Fig. 4 (e) shows a weight function in which the weight factors are in the form of a cosine function around the center.

[0213] Fig. 4 (f) shows a weight function in which the weight factors run in the form of a step function around the center.

[0214] Fig. 4 (g) shows a weight function in which the weight factors are in the form of a Gaussian bell curve around the center.

[0215] Fig. 4 (h) shows a weight function in which the weight factors are in the form of a Hann function around the center.

[0216] Combinations of the weight functions shown and additional / other weight functions are possible. Examples of other weight functions can be found, for example, at https: / / de.wikipedia.org / wiki / Fensterfimktion#Beispiele_von_Fensterfunktionen; FJ Harris et al.: On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform, Proceedings of the IEEE, Vol. 66, No. 1, 1978; https: / / docs.scipy.org / doc / scipy / reference / signal.windows.html; KM M Prabhu: Window Functions and Their Applications in Signal Processing, CRC Press, 2014, 978-1-4665-1583-3).

[0217] Weight functions that can be used are also referred to as window functions in the literature.

[0218] Preferably, weighting functions are used which have proven themselves in MRI imaging and spectroscopy for weighting k-space data, such as the Hann function (also known as Hann Window, see e.g.: Hanning Window, see e.g. R. Pohmann et al.: Accurate phosphorus metabolite images of the human heart by 3D acquisition-weighted CSI, Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 45.5 (2001): 817-826).

[0219] Another preferred weight function is the Poisson function (Poisson Window).

[0220] Fig. 5 schematically shows an embodiment of the computer-implemented method of the present disclosure in the form of a flowchart. The method (100) comprises the steps:

[0221] (110) receiving or generating a first representation, wherein the first representation represents an examination region of an examination object without contrast agent or after application of a first amount of contrast agent,

[0222] (120) receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount,

[0223] (130) generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation,

[0224] (140) Generating a histogram of the third representation,

[0225] (150) Approximation of the histogram by a sum of at least two distribution functions,

[0226] (160) Determining a signal-to-noise ratio based on parameters of the at least two distribution functions,

[0227] (170) Outputting and / or storing the signal-to-noise ratio and / or using the signal-to-noise ratio to determine a measure for increasing the signal-to-noise ratio.

[0228] The steps, methods and / or functions described in this disclosure may be performed in whole or in part by a computer system.

[0229] A "computer system" is an electronic data processing system that processes data using programmable computing instructions. Such a system typically includes a "computer," the unit that includes a processor for performing logical operations, and peripherals.

[0230] In computer technology, "peripherals" refers to all devices connected to a computer that control the computer and / or serve as input and output devices. Examples include monitors, printers, scanners, mice, keyboards, drives, cameras, microphones, speakers, etc. Internal connectors and expansion cards are also considered peripherals in computer technology.

[0231] Today's computer systems are often divided into desktop PCs, portable PCs, laptops, notebooks, netbooks, and tablet PCs, as well as so-called handheld devices (e.g., smartphones); all of these systems can be used to implement the invention.

[0232] The term “computer” should be broadly interpreted to include any type of electronic device with data processing capabilities, including, as a non-limiting example, personal computers, servers, embedded cores, communications devices, processors (e.g., digital signal processor (DSP), microcontroller, field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), etc.) and other electronic computing devices.

[0233] The term "process," as used above, is intended to encompass any type of calculation, manipulation, or transformation of data that is represented as physical, e.g., electronic, phenomena and that may occur or be stored, e.g., in registers and / or memory of at least one computer or processor. The term "processor" includes a single processing unit or a plurality of distributed or remote processing units.

[0234] Fig. 6 shows an exemplary and schematic representation of a computer system according to the present disclosure. The computer system (1) shown in Fig. 6 comprises a receiving unit (10), a control and computing unit (20), and an output unit (30).

[0235] The control and computing unit (20) serves to control the computer system (1), to coordinate the data flows between the units of the computer system (1) and to carry out calculations.

[0236] The control and computing unit (20) is configured to: generate a first representation or cause the receiving unit (10) to receive the first representation, wherein the first representation represents an examination region of an examination object without contrast agent or after application of a first amount of contrast agent, generate a second representation or cause the receiving unit (10) to receive the second representation, wherein the second representation represents the examination region of the examination object after application of a second amount of contrast agent, wherein the second amount is greater than the first amount, generate a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation, generate a histogram of the third representation,to approximate the histogram by a sum of at least two distribution functions, a signal-to-noise ratio based on parameters of at least two,

[0237] to determine distribution functions, to use the signal-to-noise ratio when generating a synthetic representation of the examination area of ​​the examination object and / or to cause the output unit (30) to output the signal-to-noise ratio and / or to store it and / or to transmit it to a separate computer system.

[0238] Fig. 7 shows an exemplary and schematic illustration of another embodiment of a computer system of the present disclosure.

[0239] 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 computing unit, as shown in Fig. 6.

[0240] The processing unit (21) may comprise one or more processors alone or in combination with one or more memories. The processing unit (21) may be conventional computer hardware capable of processing information such as digital images, computer programs, and / or other digital information. The processing unit (21) typically consists of an arrangement of electronic circuits, some of which may be embodied as an integrated circuit or as multiple interconnected integrated circuits (an integrated circuit is sometimes referred to as a "chip"). The processing unit (21) may be configured to execute computer programs that may be stored in a main memory of the processing unit (21) or in the memory (22) of the same or another computer system.

[0241] The memory (22) may be ordinary computer hardware capable of storing information such as digital image recordings (e.g. representations of the examination area), data, computer programs and / or other digital information either temporarily and / or permanently. The memory (22) may comprise volatile and / or non-volatile memory and may be permanently installed or removable. Examples of suitable memories are RAM (Random Access Memory), ROM (Read-Only Memory), a hard disk, flash memory, a removable computer diskette, an optical disc, magnetic tape or a combination of the above. Optical discs may include read-only compact discs (CD-ROM), read / write compact discs (CD-R / W), DVDs, Blu-ray discs and the like.

[0242] In addition to the memory (22), the processing unit (21) can also be connected to one or more interfaces (11, 12, 31, 32, 33) for displaying, transmitting, and / or receiving information. The interfaces can comprise one or more communication interfaces (11, 32, 33) and / or one or more user interfaces (12, 31). The one or more communication interfaces can be configured to send and / or receive information, e.g., to and / or from an MRI scanner, a CT scanner, an ultrasound camera, other computer systems, networks, data storage devices, or the like. The one or more communication interfaces can be configured to transmit and / or receive information via physical (wired) and / or wireless communication connections.The one or more communication interfaces may include one or more interfaces for connecting to a network, e.g., using technologies such as cellular, Wi-Fi, satellite, cable, DSL, fiber optic, and / or the like. In some examples, the one or more communication interfaces may include one or more short-range communication interfaces configured to connect devices using short-range communication technologies such as NFC, RFID, Bluetooth, Bluetooth LE, ZigBee, infrared (e.g., IrDA), or the like.

[0243] The user interfaces may comprise a display (31). A display (31) may be configured to display information to a user. Suitable examples include a liquid crystal display (LCD), a light-emitting diode display (LED), a plasma display (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), e.g., 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 with 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 may include barcodes, radio frequency identification (RFID), magnetic stripes, optical character recognition (OCR), integrated circuit cards (ICC), and the like. The user interfaces may further include one or more interfaces for communicating with peripheral devices such as printers and the like.

[0244] One or more computer programs (40) can be stored in the memory (22) and executed by the processing unit (21), which is thereby programmed to perform the functions described in this description. The retrieval, loading, and execution of instructions of the computer program (40) can occur sequentially, such that one instruction is retrieved, loaded, and executed at a time. However, the retrieval, loading, and / or execution can also occur in parallel.

[0245] The computer system of the present disclosure may be embodied as a laptop, notebook, netbook and / or tablet PC, and may also be a component of an MRI scanner, a CT scanner or an ultrasound diagnostic device.

[0246] The computer system of the present disclosure may be embodied as a laptop, notebook, netbook and / or tablet PC, it may also be a component of an MRI scanner, a CT scanner, an ultrasound diagnostic device or a PET scanner.

[0247] The present invention also relates to a computer program product. Such a computer program product comprises a non-volatile data carrier, such as a CD, a DVD, a USB stick, or another medium for storing data. A computer program is stored on the data carrier. The computer program can be loaded into a working memory of a computer system (in particular into a working memory of a computer system of the present disclosure) and there cause the computer system to perform the following steps:

[0248] Receiving or generating a first representation, wherein the first representation represents an examination area of ​​an examination object without contrast agent or after application of a first amount of contrast agent,

[0249] Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount,

[0250] Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation,

[0251] Generating a histogram of the third representation,

[0252] Approximating the histogram by a sum of at least two distribution functions, a first distribution function and a second distribution function,

[0253] Determining a signal-to-noise ratio based on parameters of at least two distribution functions,

[0254] Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio in generating a synthetic representation of the examination area of ​​the examination object.

[0255] The computer program product can also be marketed in combination (in a compilation) with the contrast agent. Such a compilation is also referred to as a kit. Such a kit comprises the contrast agent and the computer program product. It is also possible for such a kit to comprise the contrast agent and means allowing a purchaser to obtain the computer program, e.g., to download it from an internet site. These means can include a link, i.e., an address of the internet site from which the computer program can be obtained, e.g., from which the computer program can be downloaded onto a computer system connected to the internet. These means can include a code (e.g., an alphanumeric character string, a QR code, a DataMatrix code, a barcode, or another optically and / or electronically readable code) that allows the purchaser to access the computer program.Such a link and / or code can, for example, be printed on the packaging of the contrast agent and / or on a package insert accompanying the contrast agent. A kit is thus 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 storage medium) that is offered for sale together.

Claims

PATENT CLAIMS 1. Computer-implemented method comprising: Receiving or generating a first representation, wherein the first representation represents an examination area of ​​an examination object without contrast agent or after application of a first amount of contrast agent, Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount, Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation, Generating a histogram of the third representation, Approximating the histogram by a sum of at least two distribution functions, a first distribution function and a second distribution function, Determining a signal-to-noise ratio based on parameters of at least two distribution functions, Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio in generating a synthetic representation of the examination area of ​​the examination object.

2. The method according to claim 1, wherein the object to be examined is a living being and the examination area is a part of the living being.

3. The method according to claim 1 or 2, wherein the first representation is a radiological image, the second representation is a radiological image and the synthetic representation is a synthetic radiological image.

4. The method according to any one of claims 1 to 3, wherein the first representation is an MRI image, the second representation is an MRI image, and the synthetic representation is a synthetic MRI image.

5. The method according to any one of claims 1 to 3, wherein the first representation is a CT scan, the second representation is a CT scan, and the synthetic representation is a synthetic CT scan.

6. The method according to any one of claims 1 to 5, wherein the first distribution function of the at least two distribution functions represents the difference between the signal intensities of those image elements of the first and second representations whose signal intensities are independent of the amount of contrast agent, wherein the second distribution function of the at least two distribution functions represents the difference between the signal intensities of those image elements of the first and second representations whose signal intensities are dependent on the amount of contrast agent.

7. The method according to any one of claims 1 to 6, wherein the histogram has two peaks, a first peak and a second peak, wherein the first distribution function of the at least two distribution functions approximates the first peak and the second distribution function approximates the second peak.

8. The method according to any one of claims 1 to 7, wherein determining the signal-to-noise ratio comprises: Determine a maximum of the first distribution function, Determine a maximum of the second distribution function, Determining a distance of the maximum of the second distribution function from the maximum of the first distribution function, Determine a width of the first distribution function, Determining the signal-to-noise ratio based on the distance of the maximum of the second distribution function from the maximum of the first distribution function and the width of the first distribution function.

9. The method according to claim 8, wherein the signal-to-noise ratio is or comprises a ratio of the distance of the maximum of the second distribution function from the maximum of the first distribution function to the width of the first distribution function.

10. The method according to claim 8 or 9, wherein the width of the first distribution function is the half-width of the first distribution function or a value derived from the half-width of the first distribution function.

11. The method according to any one of claims 1 to 10, wherein the first and / or the second distribution function is a Gaussian bell curve.

12. The method according to any one of claims 1 to 11, wherein using the signal-to-noise ratio in generating the synthetic representation of the examination area of ​​the examination object comprises: Comparing the signal-to-noise ratio with a first predefined threshold and, in the event that the determined signal-to-noise ratio is less than the first threshold, performing a measure to increase the signal-to-noise ratio, and / or Comparing the signal-to-noise ratio with a second predefined threshold and, in the event that the determined signal-to-noise ratio is less than the second threshold, outputting a message that the signal-to-noise ratio is too low for generating a synthetic representation of the examination area of ​​the examination object, and / or Identifying a measure to increase the signal-to-noise ratio based on the determined signal-to-noise ratio, and / or Selecting a frequency-dependent weighting function based on the determined signal-to-noise ratio, and / or Determining one or more parameters of a frequency-dependent weighting function based on the determined signal-to-noise ratio, and / or Determining an amplification factor based on the determined signal-to-noise ratio, and / or Determine a maximum gain factor based on the determined signal-to-noise ratio.

13. The method according to any one of claims 1 to 12, further comprising: optionally: generating a weighted third representation of the examination area of ​​the examination object, wherein generating the weighted third representation comprises: multiplying the third representation by a frequency-dependent weighting function in the frequency domain, Generating a fourth representation of the examination area of ​​the examination object, wherein generating the fourth representation comprises: adding the optionally weighted third representation a-fold to the first representation or to the second representation, where a is a positive or negative real number, in the event that the fourth representation is a representation of the examination area of ​​the examination object in the frequency domain: converting the fourth representation of the examination area of ​​the examination object in the frequency domain into a fourth representation of the examination area of ​​the examination object in the spatial domain, Outputting and / or storing the fourth representation of the examination area of ​​the examination object in the spatial space and / or transmitting the fourth representation of the examination area of ​​the examination object in the spatial space to a separate computer system.

14. Computer system comprising: • an input unit • a control and computing unit and • an output unit, wherein the control and computing unit is configured to receive or generate a first representation, wherein the first representation represents an examination area of ​​an examination object without contrast agent or after application of a first amount of a contrast agent, to receive or generate a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount, to generate a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation, to generate a histogram of the third representation, to approximate the histogram by a sum of at least two distribution functions,to determine a signal-to-noise ratio based on parameters of the at least two distribution functions, to output the signal-to-noise ratio and / or to transmit it to a separate computer system and / or to store it and / or to use it in generating a synthetic representation of the examination area of ​​the examination object.

15. Non-transitory computer-readable storage medium on which software instructions are stored which, when executed by a processor of a computer system, cause the computer system to: Receiving or generating a first representation, wherein the first representation represents an examination area of ​​an examination object without contrast agent or after application of a first amount of contrast agent, Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount, Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation, Generating a histogram of the third representation, Approximating the histogram by a sum of at least two distribution functions, Determining a signal-to-noise ratio based on parameters of at least two distribution functions, Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio in generating a synthetic representation of the examination area of ​​the examination object.

16. Use of a contrast agent in a radiological examination procedure comprising: Receiving or generating a first representation, wherein the first representation represents an examination region of an examination object without contrast agent or after application of a first amount of the contrast agent, Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount, Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation, Generating a histogram of the third representation, Approximating the histogram by a sum of at least two distribution functions, Determining a signal-to-noise ratio based on parameters of at least two distribution functions, Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio in generating a synthetic representation of the examination area of ​​the examination object.

17. Kit comprising a computer program product and a contrast agent, wherein the computer program product comprises a computer program that can be loaded into a working memory of a computer system and causes the computer system to carry out the following steps: Receiving or generating a first representation, wherein the first representation represents an examination region of an examination object without contrast agent or after application of a first amount of the contrast agent, Receiving or generating a second representation, wherein the second representation represents the examination area of ​​the examination object after application of a second amount of the contrast agent, wherein the second amount is greater than the first amount, Generating a third representation based on the first representation and the second representation, wherein generating the third representation comprises subtracting the first representation from the second representation, Generating a histogram of the third representation, Approximating the histogram by a sum of at least two distribution functions, Determining a signal-to-noise ratio based on parameters of at least two distribution functions, Outputting and / or storing the signal-to-noise ratio and / or transmitting the signal-to-noise ratio to a separate computer system and / or using the signal-to-noise ratio in generating a synthetic representation of the examination area of ​​the examination object.

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