Determining signal-to-noise ratio in radiological images

Abstract

The systems, methods, and computer programs disclosed herein relate to determining signal-to-noise ratio in radiology images.

Description

Technical Field

[0001] The systems, methods, and computer programs disclosed herein relate to determining the signal-to-noise ratio in radiographic images. Background Technology

[0002] WO2019 / 074938A1 discloses a method for reducing the amount of contrast agent when generating radiographic images by using artificial neural networks.

[0003] In the disclosed method, in the first step, a training dataset is generated. For each of the multiple individuals, the training dataset includes: i) the original radiographic image (zero-contrast image), ii) the radiographic image after the application of a low amount of contrast agent (low-contrast image), and iii) the radiographic image after the application of a standard amount of contrast agent (full-contrast image).

[0004] In the second step, the artificial neural network is trained based on the original radiographic images and radiographic images after the application of a low amount of contrast agent, thereby predicting artificial radiographic images for each individual's training dataset, which show the acquisition area after the application of a standard amount of contrast agent. In each case, the radiographic image measured after the application of a standard amount of contrast agent is used as a reference (ground truth) in the training.

[0005] In the third step, the trained artificial neural network can be used to predict artificial radiographic images for new individuals based on the original radiographic images and radiographic images after the application of a low amount of contrast agent. These artificial radiographic images show the acquisition area as if a standard amount of contrast agent had been applied.

[0006] The method disclosed in WO2019 / 074938A1 has several drawbacks.

[0007] For example, training an artificial neural network requires training data. This necessitates performing numerous radiological examinations on a large number of people and generating training data to train the network.

[0008] The artificial neural network disclosed in WO2019 / 074938A1 was trained to predict radiographic images after the administration of a standard amount of contrast agent. The artificial neural network was not configured or trained to predict radiographic images after the administration of contrast agents at amounts lower or higher than the standard. The method described in WO2019 / 074938A1 can, in principle, be trained to predict radiographic images after the administration of contrast agents at amounts different from the standard, but this requires further training data and further training.

[0009] Generating radiographic images with variable contrast enhancement is possible. One approach is to subtract the original radiographic image of the examined area of ​​the subject from the original radiographic image of the examined area element-wise, multiply the subtraction result element-wise by a variable gain factor, and then add the multiplication result element-wise back to the original radiographic image. This method is described, for example, by 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. This cited paper uses this method to generate artificial radiographic images for training machine learning models. A drawback of this method is that noise is amplified to an equal degree in addition to the contrast agent signal.

[0010] WO2024 / 052156A1 explains that noise can be reduced as follows: Subtracting the original radiographic image from the contrast-enhanced radiographic image can be performed in the frequency domain, and the result of the subtraction can be multiplied by a frequency-dependent weighting function, then multiplied by a gain factor, and added back to the original radiographic image. The weighting function can assign higher weights to low frequencies than to high frequencies. In frequency domain rendering, contrast information is represented by low frequencies, while high frequencies represent information about fine structures. Image noise is typically uniformly distributed in frequency domain rendering. The frequency-dependent weighting function acts as a filter. This filter improves the signal-to-noise ratio because the spectral noise density at high frequencies is reduced.

[0011] Signal-to-noise ratio (SNR) is not the same for all radiological images; it depends on a large number of factors. Understanding the SNR in an individual case is beneficial in order to determine appropriate measures to improve the SNR. Summary of the Invention

[0012] The problems stated, as well as others, are addressed by the subject matter of the independent claims of this disclosure. Preferred embodiments can be found in the dependent claims, the description, and the drawings.

[0013] The first subject of this disclosure is a computer-implemented method, comprising: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

[0014] A further subject of this disclosure is a computer system comprising: Input unit, Control and computing units, and Output unit, The control and computing unit is configured as follows: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

[0015] A further subject of this disclosure is a non-volatile computer-readable storage medium having software instructions stored thereon, which, when executed by a processor of a computer system, cause the computer system to perform the following steps: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

[0016] A further subject of this disclosure is the use of contrast agents in radiological examination methods, said radiological examination methods including: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

[0017] A further subject of this disclosure is a contrast agent used in a radiological examination method, said radiological examination method comprising: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

[0018] A further subject of this disclosure is a suite of components including a computer program product and a contrast agent, the computer program product including a computer program that can be loaded into the main memory of a computer system, wherein the computer program causes the computer system to perform the following steps: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object. Attached Figure Description

[0019] Figure 1 The illustration demonstrates, illustratively and schematically, the generation of a third representation of the inspection region of the inspection object based on a first and second representation of the inspection region, the generation of a histogram of the third representation, and the approximation of the histogram by a mathematical function.

[0020] Figure 2 An example and schematic illustration shows the application of a noise filter to the third characterization.

[0021] Figure 3 An exemplary and schematic illustration is shown of generating a fourth representation based on a third representation and a first representation.

[0022] Figure 4 An example of a frequency-related weighting function that can be used to weight the third representation is shown.

[0023] Figure 5 An implementation scheme of the computer-based method of this disclosure is schematically illustrated in flowchart form.

[0024] Figure 6 An exemplary and schematic embodiment of the computer system disclosed herein is shown.

[0025] Figure 7 Further embodiments of the computer system of this disclosure are illustrated, by way of example and illustration. Detailed Implementation

[0026] The subject matter of this disclosure will be explained in more detail below without distinguishing between the subject matter of this disclosure (method, computer system, computer-readable storage medium, use, contrast agent used, assembly). Rather, the following statements apply to all subject matter of this disclosure, with necessary modifications, regardless of the context in which they are described (method, computer system, computer-readable storage medium, use, contrast agent used, assembly).

[0027] If the steps are specified in a particular order in this specification or claims, this does not mean that the disclosure is limited to that specified order. Rather, it is conceivable that these steps may be performed in a different order or in parallel with each other, unless one step builds upon another, for example, a step that builds upon a preceding step must be performed subsequently (however, this will be clear in individual cases). Therefore, the mentioned order is an exemplary embodiment of the disclosure.

[0028] In some places, the subject matter of this disclosure will be explained in more detail with reference to the accompanying drawings. The drawings illustrate specific embodiments having particular features and combinations thereof, which are primarily intended for illustrative purposes; the drawings should not be construed as limiting the disclosure to the features and combinations thereof shown in the drawings. Furthermore, the statements made in the description of the drawings regarding features and combinations thereof are intended to be generally applicable, i.e., also applicable to other embodiments, not limited to those shown.

[0029] The terms used in this disclosure have the meanings that they have in the prior art, and in particular in the prior art referenced in this disclosure, unless otherwise stated in this specification.

[0030] One subject of this disclosure is a computer-implemented method. The computer-implemented method is characterized in that the steps constituting the method are performed on and / or by one or more computers or computer systems.

[0031] In the first step, a first representation and a second representation are received or generated.

[0032] The term "receive" encompasses both the retrieval of representations and the acceptance of representations, for example, by a computer system transmitted to this disclosure. The representations may be received, for example, from a CT scanner, MR scanner, ultrasound scanner, or PET scanner. The representations may be read, for example, from one or more data storage devices and / or transmitted from a separate computer system.

[0033] The term "generate" can mean generating, preferably computing, a representation based on another (e.g., received) representation or based on multiple other (e.g., received) representations. For example, the received representation could thus be a representation of the inspection region of the object in the spatial domain. Based on this spatial domain representation, a representation of the inspection region of the object in the frequency domain can be generated, for example, through a transformation operation (e.g., a Fourier transform operation). The received representation could also be a representation of the inspection region of the object in the frequency domain. Based on this frequency domain representation, a representation of the inspection region of the object in the spatial domain can be generated, for example, through a transformation operation (e.g., an inverse Fourier transform operation). Other methods for generating representations based on one or more other representations are described in this specification.

[0034] The term "generation" can also refer to generating a representation of an object being examined through measurement, for example in radiological examination methods.

[0035] The first characterization represents the examination area of ​​a subject before or after the application of a first amount of contrast agent. The second characterization represents the examination area of ​​a subject after the application of a second amount of contrast agent, which is greater than the first amount.

[0036] The "object of inspection" is typically a living organism, such as a mammal, like a human. In one embodiment of this disclosure, the object of inspection is a human.

[0037] The "examination area" is a part of the object being examined, such as an organ or part of an organ, or another part of multiple organs or the object being examined.

[0038] For example, the area to be examined could be the liver, kidneys, heart, lungs, brain, stomach, bladder, prostate, intestines, female breasts, uterus, thyroid, pancreas, spleen or a part thereof, or another part of the body of a mammal (e.g., human).

[0039] In one implementation, the examination area includes the liver or a portion of the liver, or the examination area is the liver or a portion of the liver of a mammal (e.g., a human).

[0040] In a further embodiment, the area to be examined includes the brain or a portion of the brain, or the area to be examined is the brain or a portion of the brain of a mammal (e.g., a human).

[0041] In a further embodiment, the examination area includes the heart or a portion of the heart, or the examination area is the heart or a portion of the heart of a mammal (e.g., a human).

[0042] In a further embodiment, the examination area includes the chest or a portion of the chest, or the examination area is the chest or a portion of the chest of a mammal (e.g., a human).

[0043] In a further embodiment, the examination area includes the stomach or a portion of the stomach, or the examination area is the stomach or a portion of the stomach of a mammal (e.g., a human).

[0044] In a further embodiment, the examination area includes the thyroid gland or a portion thereof, or the examination area is the thyroid gland or a portion thereof of a mammal (e.g., a human).

[0045] In a further embodiment, the examination area includes the uterus or a portion thereof, or the examination area is the uterus or a portion thereof of a female mammal (e.g., a human woman).

[0046] In a further embodiment, the examination area includes the pancreas or a portion thereof, or the examination area is the pancreas or a portion thereof of a mammal (e.g., a female human).

[0047] In a further embodiment, the examination area includes the kidney or a portion of the kidney, or the examination area is the kidney or a portion of the kidney of a mammal (e.g., a human).

[0048] In a further embodiment, the area to be examined includes one or both lungs or a portion of a lung of a mammal (e.g., a human).

[0049] In a further embodiment, the examination area includes the breast or a portion of the breast, or the examination area is the breast or a portion of the breast of a female mammal (e.g., a human woman).

[0050] In a further embodiment, the examination area includes the prostate or a portion thereof, or the examination area is the prostate or a portion thereof of a male mammal (e.g., a male human).

[0051] The examination area, also known as the field of view (FOV), is specifically the volume imaged in a radiographic image. The examination area is typically defined by the radiologist, for example, on a locator image. Of course, the examination area can also be defined alternatively or additionally, for example, in an automated manner based on a chosen protocol.

[0052] In one embodiment of this disclosure, the first and second characterizations are radiographic images. In other words, the first and second characterizations are the results of one or more radiographic examinations.

[0053] "Radiology" is a branch of medicine that involves the use of electromagnetic rays and mechanical waves (including, for example, diagnostic ultrasound) for diagnostic, therapeutic, and / or scientific purposes. In addition to X-rays, other ionizing radiation, such as gamma radiation or electrons, is also used. Because imaging is a critical application, other imaging methods (such as ultrasound and magnetic resonance imaging (MRI)) are also included in radiology, although these methods do not use ionizing radiation. Therefore, in the context of this disclosure, the term "radiology" specifically covers the following examination methods: computed tomography (CT), magnetic resonance imaging (MRT), ultrasound, and positron emission tomography (PET).

[0054] In one implementation, the first and second characterizations are CT images.

[0055] In a further embodiment, the first and second characterizations are MRT images.

[0056] In a further embodiment, the first and second characterizations are ultrasound images.

[0057] In a further embodiment, the first and second characterizations are PET images.

[0058] In radiological examinations, contrast agents are commonly used to enhance contrast.

[0059] "Contrast agent" is a substance or mixture of substances used in radiological examinations to improve the depiction of the structure and function of the human body.

[0060] In computed tomography (CT) scans, iodine-containing solutions are commonly used as contrast agents. In magnetic resonance imaging (MRT), superparamagnetic materials (e.g., iron oxide nanoparticles, superparamagnetic iron-platinum particles (SIPPs)) or paramagnetic materials (e.g., gadolinium chelates, manganese chelates, hafnium chelates) are commonly used as contrast agents. In ultrasound examinations, fluids containing aerated microbubbles are typically administered intravenously. Examples of contrast agents can be found in the literature (see, for example, ASL Jascinth et al.). Contrast Agents in computed tomography: A Review Journal of Applied Dental and Medical Sciences, 2016, Vol. 2, No. 2, pp. 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 resonance contrast agents: Essentials and tips for safe practices , World J Radiol, 28 September 2017, 9(9): 339-349; LC Abonyi et al: Intravascular Contrast Media in Radiography: Historical Development & Review of Risk Factors for Adverse Reactions , South American Journal of Clinical Research, 2016, Vol. 3, No. 1, 1-10; ACR Manual on Contrast Media, 2020, ISBN: 978-1-55903-012-0; A. Ignee et al.: Ultrasound contrast agents Endosc Ultrasound, November-December 2016, 5(6), 355-362.

[0061] In MRT examinations, MRT contrast agents exert their effect by altering the relaxation time of structures that take up the contrast agent. A distinction can be made between two groups of substances: paramagnetic and superparamagnetic. Both groups possess unpaired electrons, which induce magnetic fields around individual atoms or molecules. Superparamagnetic contrast agents primarily cause T2 shortening, while paramagnetic contrast agents primarily cause T1 shortening. The effect of the contrast agents is indirect, as they do not emit signals themselves but instead affect the signal strength in their vicinity. Examples of superparamagnetic contrast agents are 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 ® Omniscan ® ), gadoterol (ProHance) ® ), Gadovist ® ), Gadopiclenol (Elucirem, Vueway) and gadoxetine (Primovist) ® / Eovist ® ).

[0062] The first characterization represents the examination area of ​​the subject before or after the application of a first amount of contrast agent. In one embodiment of this disclosure, the first characterization represents the examination area before the application of contrast agent.

[0063] The second characterization refers to the examination area of ​​the subject after the application of a second amount of contrast agent, wherein the second amount is greater than the first amount (as described, the first amount may also be zero).

[0064] The statement "after the application of the second amount of contrast agent" should not be interpreted as the sum of the first and second amounts in the examination area. Therefore, the statement "characterized as the examination area after the application of (the first or second) amount" should be interpreted as "characterized as the examination area having (the first or second) amount" or "characterized as the examination area including (the first or second) amount".

[0065] In one embodiment of this disclosure, both the first and second amounts of contrast agent are less than the standard amount.

[0066] In a further implementation, the second amount of contrast agent corresponds to the standard amount.

[0067] In a further embodiment, the first amount of contrast agent is equal to zero, and the second amount of contrast agent is less than the standard amount.

[0068] In a further embodiment, the first amount of contrast agent is equal to zero, and the second amount of contrast agent corresponds to the standard amount.

[0069] The standard amount is usually the amount recommended by the contrast agent manufacturer and / or distributor, and / or the amount authorized by the regulatory authority, and / or the amount specified in the contrast agent's packaging instructions.

[0070] For example, Primovist ® The standard amount is 0.025 mmol Gd-EOB-DTPA disodium per kilogram of body weight.

[0071] The radiological examination methods and the contrast agents used can be independent of each other. This means, for example, that the first characterization can be a CT image showing the area examined without the application of contrast agent or after the application of a first amount of MRT contrast agent, while the second characterization can be a CT image showing the area examined after the application of a second amount of the MRT contrast agent.

[0072] Preferably, the contrast agent is a contrast agent comprising one or more of the following substances: - 2-[4,7,10-tris(carboxymethyl)-1,4,7,10-tetraazacyclododecane-1-yl]gadolinium(III) acetate, - Ethoxybenzyl diethylenetriaminepentaacetic acid gadolinium(III) - 2-[3,9-bis[1-carboxylate-4-(2,3-dihydroxypropylamino)-4-oxobutyl]-3,6,9,15-tetraazabicyclo[9.3.1]pentadecano-1(15),11,13-trien-6-yl]-5-(2,3-dihydroxypropylamino)-5-oxovalerate gadolinium(III), - Dihydro[(±)-4-carboxylate-5,8,11-tris(carboxymethyl)-1-phenyl-2-oxa-5,8,11-triazatridecane-13-acidyl(5-)]gadolinate (2-), - [4,10-bis(carboxylate-methyl)-7-{3,6,12,15-tetraoxo-16-[4,7,10-tris-(carboxylate-methyl)-1,4,7,10-tetraazacyclododecane-1-yl]-9,9-bis({[({2-[4,7,10-tris-(carboxylate-methyl)-1,4,7,10-tetraazacyclododecane-1-yl]propionyl}amino)acetyl]amino}methyl)-4,7,11,14-tetraazaheptadecane-2-yl}-1,4,7,10-tetraazacyclododecane-1-yl]tetragadolinium acetate, - 2,2',2''-(10-{1-carboxyl-2-[2-(4-ethoxyphenyl)ethoxy]ethyl}-1,4,7,10-tetraazacyclododecane-1,4,7-triyl)gadolinium triacetate, - 2,2',2''-{10-[1-carboxyl-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}gadolinium triacetate, - 2,2',2''-{10-[(1R)-1-carboxyl-2-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}ethyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}gadolinium triacetate, - (2S,2'S,2''S)-2,2',2''-{10-[(1S)-1-carboxyl-4-{4-[2-(2-ethoxyethoxy)ethoxy]phenyl}butyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}tris(3-hydroxypropionate)gadolinium, - 2,2',2''-{10-[(1S)-4-(4-butoxyphenyl)-1-carboxybutyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}gadolinium triacetate, - 5,8-bis(carboxylate-methyl)-2-[2-(methylamino)-2-oxoethyl]-10-oxo-2,5,8,11-tetraazadodecane-1-carboxylate gadolinium(III) - 2-[4-(2-hydroxypropyl)-7,10-bis(2-oxo-2-oxoethyl)-1,4,7,10-tetraazacyclododecane-1-yl]gadolinium(III) acetate, - 2,2',2''-(10-((2R,3S)-1,3,4-trihydroxybutyl-2-yl)-1,4,7,10-tetraazacyclododecane-1,4,7-triyl)gadolinium triacetate(III), - 2,2',2''-{(2S)-10-(carboxymethyl)-2-[4-(2-ethoxyethoxy)benzyl]-1,4,7,10-tetraazacyclododecane-1,4,7-triyl}gadolinium triacetate, - 2,2',2''-[10-(carboxymethyl)-2-(4-ethoxybenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7-triyl]gadolinium triacetate.

[0073] The first and second representations can be representations of the inspection area of ​​the inspection object in the spatial domain.

[0074] The first and second characterizations can be representations of the inspection region of the inspection object in the frequency domain.

[0075] The "spatial domain" is ordinary three-dimensional Euclidean space, which corresponds to the space in which humans experience and move using their senses. Therefore, the representations in the spatial domain are familiar to people.

[0076] In spatial domain representation (also referred to herein as spatial domain depiction or spatial domain characterization), the area under examination is typically represented by a large number of image elements (e.g., pixels or voxels), which may be arranged in a raster pattern, in which case each image element represents a portion of the area under examination, and each image element may be assigned a color value or grayscale value. The color value or grayscale value typically characterizes the signal strength of the measured signal.

[0077] The DICOM format is widely used in radiology for storing and processing spatial domain representations. DICOM (Digital Imaging and Communications in Medicine) is an open standard for storing and exchanging information in medical image data management.

[0078] The "frequency domain" is a domain in which a signal is considered as the sum of its individual frequency components.

[0079] In the frequency domain representation (also referred to as frequency domain plotting or frequency domain characterization in this specification), the region of examination is represented by a superposition of fundamental frequencies. For example, the region of examination can be represented by the sum of sine and / or cosine functions with different amplitudes, frequencies, and phases. The amplitude and phase can be plotted as functions of frequency, for example in a two-dimensional or three-dimensional characterization. Typically, the lowest frequency (origin) is placed at the center. The further away from the center, the higher the frequency. Each frequency can be assigned an amplitude (representing the frequency in the frequency domain plot) and a phase (indicating the degree of deviation of the corresponding oscillation relative to a sine or cosine oscillation).

[0080] The raw data obtained in a magnetic resonance imaging (MRI) examination (so-called k-space data) is an embodiment characterized in the frequency domain. Such raw data (k-space data) from an MRI examination may be used as a first characterization and / or a second characterization in the context of this disclosure.

[0081] A representation in the spatial domain can be transformed into a representation in the frequency domain, for example, through a Fourier transform operation. Conversely, a representation in the frequency domain can be transformed into a representation in the spatial domain, for example, through an inverse Fourier transform operation.

[0082] Details about spatial domain descriptions and frequency domain descriptions and their respective conversions are described in many publications, see, for example: https: / / see.stanford.edu / materials / lsoftaee261 / book-fall-07.pdf.

[0083] A third representation is generated based on the first and second representations. The third representation can be generated by subtracting the first representation from the second representation.

[0084] The subtraction can be performed in the spatial domain or the frequency domain. For example, in the spatial domain, the grayscale or color values ​​of the corresponding image elements are subtracted. In this case, the corresponding image elements are those representing the same sub-region of the area being examined.

[0085] Histograms are generated from the third representation.

[0086] A histogram indicates the frequency of occurrence of multiple different differences, different difference levels, or different difference ranges within a third representation. For example, if the first and second representations are grayscale images, a third image class representation is generated by subtracting the first representation from the second representation, in which the corresponding difference between the grayscale values ​​of the first and second representations is specified for each image element. The histogram of the third representation can then indicate the frequency of occurrence of each difference in the grayscale values ​​within the third representation.

[0087] In a further step, a compensation calculation (also known as adaptation) is performed. This means approximating the histogram with a mathematical function. In other words, the mathematical function describing the histogram is determined.

[0088] Using mathematical functions to fit / approximate the histogram of the third representation can be performed using known compensation computation methods (such as regression and / or fitting).

[0089] The histogram of the third characteristic typically has two peaks: a first peak and a second peak. The first peak usually has zero difference at its maximum value.

[0090] The width of the first peak reflects the noise in the third characteristic. The wider the peak, the greater the noise; the narrower the peak, the less noise.

[0091] The second peak is caused by the different amounts of contrast agent in the first and second characterizations. Generally, the greater the distance between the maximum value of the second peak and the maximum value of the first peak, the greater the difference in signal intensity caused by the different amounts of contrast agent in the first and second characterizations.

[0092] These peaks will be referred to later in the instruction manual. Figure 1 The embodiments shown will be explained in more detail.

[0093] The mathematical function used to describe (approximate) a histogram is the sum of at least two distribution functions, each representing a corresponding peak.

[0094] In other words, the mathematical functions used to describe (approximate) the histogram include a first distribution function and a second distribution function. The first distribution function represents the signal intensity difference between the first and second representations of the image elements whose signal intensity is independent of the amount of contrast agent. The second distribution function represents the signal intensity difference between the first and second representations of the image elements whose signal intensity depends 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.

[0095] The distribution function can be, for example, a Gaussian normal distribution (Gaussian bell curve), which is unambiguously defined by the expected value and standard deviation as function parameters.

[0096] Other functions are also possible.

[0097] In a further step, at least one signal-to-noise ratio is determined based on the function parameters of the at least two distribution functions.

[0098] The determined signal-to-noise ratio is a measure of the ratio of desired signal to unwanted noise in the third characterization.

[0099] The determined signal-to-noise ratio can be the quotient of two parameters, where the dividend can be a measure of the distance between the second peak and the first peak, and the divisor can be a measure of the width of the first peak.

[0100] The dividend can be, for example, the distance between the maximum value of the second peak and the maximum value of the first peak.

[0101] The divisor could be, for example, the full width at half height (width at half height) of the first peak.

[0102] The determined signal-to-noise ratio can be output (e.g., displayed on a monitor and / or printed using a printer) and / or stored in a data memory.

[0103] The determined signal-to-noise ratio can be used when generating a synthetic representation of the inspection area of ​​the inspection object.

[0104] The determined signal-to-noise ratio can be used to determine a metric for improving the signal-to-noise ratio.

[0105] The determined signal-to-noise ratio can be used to select a frequency-dependent weighting function, and the frequency domain representation of the third characterization can be multiplied by this weighting function to improve the signal-to-noise ratio.

[0106] The determined signal-to-noise ratio can be used to determine the values ​​of one or more parameters of the frequency-dependent weighting function, and the frequency domain representation of the third characterization can be multiplied by the frequency-dependent weighting function to improve the signal-to-noise ratio.

[0107] The determined signal-to-noise ratio (SNR) can be used to determine whether there is a genuine need to improve it. The determined SNR can be compared to a predetermined threshold. If the determined SNR is greater than or equal to the threshold, the SNR may already be "good enough" that no further noise reduction is needed. If the determined SNR is less than the threshold, further noise reduction may be required.

[0108] The threshold can be defined (set) by the user of the computer system disclosed herein and / or by the radiologist.

[0109] The determined signal-to-noise ratio can be used to determine the gain factor. The third characterization, or weighted third characterization, can be multiplied by the gain factor and added to the first and / or second characterizations to generate a fourth characterization. The gain factor can indicate the degree to which the signal intensity generated by the contrast agent in the second characterization is expected to be attenuated or enhanced in the fourth characterization.

[0110] The determined signal-to-noise ratio can be used to determine how high the maximum gain attributable to the contrast agent signal intensity should be (achieved by multiplying the third characterization by a gain factor). In other words, the determined signal-to-noise ratio can be used to determine the maximum gain factor.

[0111] The determined signal-to-noise ratio (SNR) can be used to determine whether the signal strength gain caused by the contrast agent is actually useful. For example, if the SNR is low, signal enhancement (even after noise reduction) may be useless because the resulting characterization is too noisy to be used for diagnostic and / or therapeutic purposes. If the SNR is below a predetermined threshold, a message can be output indicating that signal enhancement is useless or impossible.

[0112] In a further step, a fourth characterization of the examination area of ​​the subject can be generated, wherein the signal intensity attributable to the contrast agent is enhanced relative to the first and second characterizations.

[0113] Therefore, the third representation of the inspection area of ​​the inspection object in the frequency domain can be multiplied by a frequency-related weighting function, and the result of the multiplication can be added to the first and / or second representations.

[0114] The previously determined signal-to-noise ratio can be used as described to: (i) determine whether such multiplication with the frequency-dependent weighting function is indeed necessary; (ii) determine whether the signal-to-noise ratio is too low for it to seem meaningless to enhance the signal strength attributable to the contrast agent; (iii) to select the frequency-dependent weighting function; (iv) to determine one or more values ​​of one or more parameters of the frequency-dependent weighting function; (v) to determine the gain factor; and / or (vi) to determine the maximum gain factor.

[0115] Before multiplying the third representation by the frequency-related weighting function, the third representation can be normalized. That is, the amplitude and / or phase values ​​for each frequency can be multiplied by a factor such that the amplitude / phase with the highest value is represented, for example, by a "white" hue, and the amplitude / phase with the lowest value is represented, for example, by a "black" hue. In this normalization, any negative values ​​that might result from subtracting the first representation from the second representation can also be set to zero (or another value) to avoid negative values.

[0116] In the frequency-dependent weighting function, each frequency is assigned a weighting factor. For example, if the weighting factor for a specific frequency is zero, multiplying the third representation in the frequency domain by the frequency-dependent weighting function will set the amplitude of the corresponding frequency in the third representation to zero, meaning the frequency is eliminated. If the weighting factor for a specific frequency is 1, for example, multiplying the third representation in the frequency domain by the frequency-dependent weighting function will keep the amplitude of the corresponding frequency in the third representation unchanged, meaning the frequency remains unchanged. If the weighting factor for a specific frequency is, for example, 0.5, multiplying the third representation in the frequency domain by the frequency-dependent weighting function will reduce the amplitude of the corresponding frequency to half its value, meaning the corresponding frequency is attenuated in the third representation in the frequency domain. If the weighting factor for a specific frequency is, for example, 2, multiplying the third representation in the frequency domain by the frequency-dependent weighting function will double the amplitude of the corresponding frequency, meaning the corresponding frequency is enhanced in the third representation in the frequency domain.

[0117] In the frequency-related weighting of the third characterization, the amplitude of a lower frequency is preferably multiplied by a higher weighting factor compared to the amplitude of a higher frequency. In a preferred embodiment, the higher the frequency, the lower the weighting factor multiplied by the amplitude of that frequency.

[0118] Figure 4 Examples of frequency-related weighting functions are shown in the figure. These examples are also described in WO2024 / 052156A1, the entire contents of which are incorporated herein by reference.

[0119] Therefore, the third characterization, optionally weighted and multiplied by a gain factor, can be added to the first characterization, representing the degree of contrast enhancement in the fourth characterization. A gain less than 1 can also be chosen, meaning the contrast between the region with and without contrast agent is lower in the fourth characterization than in the second. Similarly, a gain for contrast enhancement caused by a greater-than-standard amount of contrast agent can be achieved. This contrast enhancement is not achievable by the method described in WO2019 / 074938A1 unless training data is generated by administering higher-than-standard amounts of contrast agent to individuals (and thus beyond regulatory approval).

[0120] Therefore, the gain factor can be a positive real number or a negative real number.

[0121] The gain factor can be selected by the user, i.e., it can be variable, or it can be predefined, i.e., it can be predetermined.

[0122] The fourth representation can also be normalized.

[0123] If the fourth representation is a frequency domain representation, then the fourth representation can be converted into a spatial domain representation by a transformation operation (e.g., an inverse Fourier transform operation).

[0124] The fourth representation in the spatial domain can be output (i.e., displayed on a monitor and / or printed using a printer), stored in a data storage device, and / or transmitted to a separate computer system.

[0125] In this disclosure, the fourth characterization is also referred to as a synthetic characterization. The term "synthetic" means that the synthetic characterization is not a (direct) result of physical measurements of the actual object under inspection, but rather that the synthetic characterization is generated through calculations based on the first and second characterizations. The term "synthetic" is synonymous with the term "artificial".

[0126] The invention will now be explained in more detail with reference to the accompanying drawings, but it is not intended to limit the invention to the features and combinations thereof shown in the drawings. Statements made with respect to any particular drawing are intended to be generally applicable, i.e., not limited to the embodiments shown.

[0127] Figure 1 The illustration demonstrates, illustratively and schematically, the generation of a third representation of the inspection region of the inspection object based on a first and second representation of the inspection region, the generation of a histogram of the third representation, and the approximation of the histogram by a mathematical function.

[0128] Figure 1 The area shown for examination includes the pig's liver.

[0129] The first characterization R1 represents the area examined in the contrast-free spatial domain. The first characterization R1 is a magnetic resonance imaging (MRT) image.

[0130] The second characterization R2 and the first characterization R1 represent the same examination area of ​​the same object, and are also in the spatial domain. The second characterization R2 is also a magnetic resonance imaging (MRT) image.

[0131] The second characterization, R2, represents the area examined after a certain amount of contrast agent has been applied.

[0132] A third representation R3 is generated based on the first representation R1 and the second representation R2 to determine the area to be inspected. Figure 1 In the illustrated embodiment, a third representation R3 (R3 = R2 - R1) is generated by subtracting the first representation R1 from the second representation R2. This subtraction involves the grayscale values ​​of individual image elements. The subtraction is performed element-by-element; that is, each grayscale value of an image element in the first representation is subtracted from the grayscale value of the corresponding image element in the second representation. A corresponding image element refers to an image element representing the same sub-region of the area being examined.

[0133] It should be noted that the generation of the third representation does not necessarily have to be performed in the spatial domain, such as... Figure 1 As shown; for example, it can also be performed in the frequency domain or other domains.

[0134] Generate a histogram from the third representation R3. Plot the differences that appear in representation R3 in the histogram. I The frequency of ) F ).

[0135] Two peaks can be seen in the histogram: the first peak P1 and the second peak P2.

[0136] The histogram is approximated using mathematical functions. Figure 1 In the illustrated embodiment, the mathematical function is the sum of two Gaussian bell curves. The first Gaussian bell curve describes the first peak, and the second Gaussian bell curve describes the second peak.

[0137] The first peak is at the difference. I P1 The value is at 0. Therefore, there are many image elements with a difference of 0 in the third characterization R3. These image elements are those with the same gray value in the first characterization R1 and the second characterization R2. These image elements represent sub-regions in the examination area where the signal intensity is independent of the amount of contrast agent applied. These are typically those sub-regions where the contrast agent did not penetrate.

[0138] The first peak P1 has a defined full width at half maximum (FWHM). This FWHM is a measure of the number of image elements with non-zero differences, and these deviations from zero cannot be attributed to the amount of contrast agent applied. Therefore, the FWHM of the first peak P1 is a measure of noise.

[0139] The difference between the second peak P2 and the non-zero peak I P2 The first peak has the highest value. The second peak represents those image elements where the signal intensity is affected by the amount of contrast agent. The difference between the highest and lowest values. I P2 This corresponds to the average of the differences in signal intensity among all those image elements affected by the amount of contrast agent. The difference between the maximum values ​​of the second peak. I P2 The difference between the maximum value of the first peak and the maximum value of the second peak I P1 The greater the distance between them, the greater the influence of the contrast agent on the signal intensity of the image elements. The difference between the maximum values ​​of the second peak... I P2 The difference between the maximum value of the first peak and the maximum value of the second peak I P1 The distance between them is a measure of signal strength in the third characterization.

[0140] The signal-to-noise ratio metric in the third characterization can be a quotient. .

[0141] The signal-to-noise ratio (SNR) can be output, stored, and / or transmitted to a separate computer system.

[0142] The signal-to-noise ratio can be used as described to: (i) determine whether such multiplication of the frequency-dependent weighting function is indeed necessary; (ii) determine whether the signal-to-noise ratio is so low that enhancing the signal strength attributable to the contrast agent seems meaningless; (iii) to select the frequency-dependent weighting function; (iv) to determine one or more values ​​of one or more parameters of the frequency-dependent weighting function; (v) to determine the gain factor; and / or (vi) to determine the maximum gain factor.

[0143] Figure 2 An example and schematic illustration shows the application of a noise filter to the third characterization.

[0144] Figure 2 The third characterization R3 shown I yes Figure 1 The third characterization, R3, is shown in the figure. The superscript "I" has been added to the reference numerals to indicate that it is a spatial domain depiction. In this embodiment, noise filtering is performed in the frequency domain. Therefore, the spatial domain characterization R3... I Converted to frequency domain representation R3 F This can be performed, for example, via a Fourier transform operation. (Reference symbol R3) F The superscript "F" indicates that this is a frequency domain description.

[0145] Subsequently, the third representation R3 in the frequency domain F Multiply by the frequency-related weighting function WF. Specifically, represent the third frequency domain as R3. F The amplitude value multiplied by the weighting factor wf Weighting factors wf It is frequency-dependent, i.e., the weighting factor. wf It is frequency f The function. For illustrative purposes, Figure 2 The weighting function WF is shown in two-dimensional form. The weighting function WF represents the weighting factors. wf As frequency f A function along a one-dimensional (dash line) dimension. In the same image plane, along the dimension perpendicular to the dashed line, the weighting function has the same shape; it is simply compressed because of the representation R3 in this embodiment. F It is a rectangle, not a square.

[0146] Compared to higher frequency amplitudes (which further deviate from the characterization of R3) F The center), the weighting function WF will take the amplitude of low frequencies (in the illustrated embodiment, the frequency from the characterization R3) F (Increase from the center outwards) multiplied by a higher weighting factor; that is, lower frequencies are given higher weights compared to higher frequencies. This can be used in the weighted representation R3. F,wIt was identified because, compared to the characterization of R3 F In this case, the gray values ​​towards the edges of the representation are darker, and compared to the representation R3 F In this case, the overall brightness decreases faster from the center outwards.

[0147] The weighted representation R3 can be used F,w Normalization is performed, which means that the amplitude values ​​can be multiplied by a factor so that the amplitude with the highest value is represented by the hue "white", and the amplitude with the lowest value is represented by the hue "black".

[0148] exist Figure 2 In the illustrated embodiment, combined with Figure 1 The described signal-to-noise ratio (SNR) can be used to select the frequency-dependent weighting function (WF). The SNR can be used to set one or more values ​​for one or more parameters of the frequency-dependent weighting function WF. For example, parameters can define the shape of the weighting function WF. Parameters, for example, can define weighting factors. wf With frequency f The degree to which it increases or decreases. For example, a parameter can define the maximum weight factor.

[0149] Figure 3 An exemplary and schematic illustration is shown of generating a fourth representation based on a third representation and a first representation.

[0150] Figure 3 The third characterization R3 shown F,w yes Figure 2 The third characterization R3 in the frequency domain has been shown. F,w . Figure 3 The first characterization R1 shown I yes Figure 1 The first representation R1 is shown in the figure. A superscript "I" has been added to the figure reference numerals to identify it as a spatial domain depiction. In the first step, the first representation R1... I Transformed into the first representation R1 in the frequency domain F This can be done, for example, through a Fourier transform operation.

[0151] In a further step, the third representation R3 in the frequency domain is... F,w The first characterization R1 is added to the frequency domain by a factor of α. F Therefore, α is the gain factor, and the third representation in the frequency domain is R3. F,w After multiplying by this gain factor, the multiplication result is added to the first representation R1 in the frequency domain. F In this case, the third characterization R3 F,w Each amplitude value is multiplied by the value α.

[0152] Multiplying by the gain factor α and summing them yields the fourth characterization R4. F The fourth characterization is R4. F This is the representation of the inspection region of the object in the frequency domain. In a further step, it is transformed into a fourth representation R4 of the inspection region of the object in the spatial domain. I This transformation can be performed, for example, through an inverse Fourier transform operation.

[0153] The fourth representation R4 of the inspection area of ​​the inspection object in the spatial domain I It can be output (e.g., displayed on a monitor and / or printed using a printer) and / or stored in a data storage device and / or transmitted to a separate computer system.

[0154] Fourth characterization (R4) I R4 F () is a synthetic representation of the inspection area of ​​the inspection object. Figure 3 In the illustrated embodiment, the fourth characterization (R4) I R4 F ) is a synthetic magnetic resonance imaging image (MRT image).

[0155] It should be noted that the multiplication of the weighted third representation with the gain factor and / or the addition with the first representation does not necessarily need to be as follows: Figure 3 The examples shown are executed in the frequency domain; they can also be executed in the spatial domain or other domains.

[0156] Figure 4 An example of a frequency-related weighting function that can be used to weight a third representation is shown. For simplicity, the weighting function is represented as a two-dimensional graph, where the weighting factors... wf (Vertical axis) is plotted as frequency. f A function of (x-axis).

[0157] Figure 4 (a) shows that it has already been shown Figure 2 The weighting function WF is shown in the figure. In this weighting function, the weighting factors can decrease exponentially from the center, for example, as the frequency increases.

[0158] Figure 4 (b) shows a weighting function in which the weighting factor decreases linearly from the center as the frequency increases.

[0159] Figure 4 (c) shows a weighting function in which the weighting factor decreases from the center in an inverse parabolic shape as the frequency increases.

[0160] Figure 4(d) shows a weighting function where the weighting factor is constant within a bounded range around the center and then decreases exponentially from the threshold frequency.

[0161] Figure 4 (e) shows a weighting function where the weighting factors have a cosine function shape around the center.

[0162] Figure 4 (f) shows a weighting function where the weighting factors have a step function shape around the center.

[0163] Figure 4 (g) shows a weighting function where the weighting factors have the shape of a Gaussian bell curve around the center.

[0164] Figure 4 (h) shows a weighting function where the weighting factors have the shape of a Hann function around the center.

[0165] The weighting function shown can be combined with other weighting functions. Examples of other weighting functions can be found, for example, at https: / / de.wikipedia.org / wiki / Fensterfunktion#Beispiele_von_Fensterfunktionen; FJ Harris et al. On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform IEEE Transactions on Letters, Volume 66, Issue 1, 1978; https: / / docs.scipy.org / doc / scipy / reference / signal.windows.html; KMM Prabhu: Window Functions and Their Applications in Signal Processing CRC Publishing House, 2014, 978-1-4665-1583-3.

[0166] The weighting functions that can be used are also referred to as window functions in the literature.

[0167] It is preferable to use a weighting function that has been used in MRT imaging and spectroscopy to weight k-space data, such as the Hann function (also known as the Hann window, see example: Hanning window See, for example, 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).

[0168] Another preferred weighting function is the Poisson function (Poisson window).

[0169] Figure 5 An implementation scheme of the computer-based method of this disclosure is schematically illustrated in the form of a flowchart.

[0170] Method (100) includes the following steps: (110) Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; (120) Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; (130) Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; (140) Generate a histogram of the third representation; (150) Approximate the histogram using the sum of at least two distribution functions; (160) Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; (170) Output and / or store the signal-to-noise ratio, and / or use the signal-to-noise ratio to determine a metric for improving the signal-to-noise ratio. The steps, methods, and / or functions described in this disclosure may be performed, in whole or in part, by a computer system.

[0171] A "computer system" is an electronic data processing system that processes data through programmable computational rules. Such a system typically includes a "computer" and peripheral devices, wherein the "computer" is a unit that includes a processor for performing logical operations.

[0172] In computer technology, "peripheral devices" refer to all devices connected to a computer and used to control the computer and / or as input and output devices. Examples of peripheral devices include monitors (screens), printers, scanners, mice, keyboards, drives, cameras, microphones, speakers, etc. Internal ports and expansion cards are also considered peripheral devices in computer technology.

[0173] Modern computer systems are generally classified as desktop PCs, portable PCs, laptops, notebooks, netbooks, tablet PCs, and so-called handheld devices (e.g., smartphones); all of these systems can be used to implement this invention.

[0174] The term "computer" should be interpreted broadly and includes any type of electronic device capable of data processing, including, as a non-limiting example, personal computers, servers, embedded kernels, communication devices, processors (e.g., digital signal processors (DSPs), microcontrollers, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc.) and other electronic computing devices.

[0175] As used above, the term "processing" is intended to cover any form of computation, operation, or transformation of data expressed in terms of physical (e.g., electronic) phenomena, and may occur, for example, in or be stored in the registers and / or memory of at least one computer or processor. The term "processor" covers a single processing unit or a large number of such distributed or remote units.

[0176] Figure 6 A computer system according to this disclosure is illustrated, by way of example and schematic.

[0177] Figure 6 The computer system (1) shown includes a receiving unit (10), a control and calculation unit (20), and an output unit (30).

[0178] The control and computing unit (20) is used to control the computer system (1), coordinate the data flow between multiple units of the computer system (1), and perform calculations.

[0179] The control and computing unit (20) is configured as follows: - Generate a first characterization or cause the receiving unit (10) to receive a first characterization, the first characterization representing the examination area of ​​the examination object before or after the application of a first amount of contrast agent; - Generate a second characterization or cause the receiving unit (10) to receive a second characterization, the second characterization representing the examination area of ​​the examination object after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - The signal-to-noise ratio is used when generating a synthetic representation of the inspection area of ​​the inspection object, and / or the output unit (30) outputs and / or stores the signal-to-noise ratio and / or transmits it to a separate computer system.

[0180] Figure 7 Further embodiments of the computer system of this disclosure are illustrated, by way of example and illustration.

[0181] The computer system (1) includes a processing unit (21) connected to a memory (22). The processing unit (21) and the memory (22) form a control and computing unit, such as Figure 6 As shown in the image.

[0182] The processing unit (21) may include one or more processors, either 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 in the form of integrated circuits or multiple interconnected integrated circuits (integrated circuits are sometimes also called “chips”). The processing unit (21) may be configured to execute a computer program, which may be stored in the main memory of the processing unit (21) or in the memory (22) of the same or different computer systems.

[0183] The memory (22) can be conventional computer hardware capable of temporarily and / or permanently storing information such as digital images (e.g., representations of an inspection area), data, computer programs, and / or other digital information. The memory (22) can include volatile and / or non-volatile memory and can be non-removable or removable. Suitable embodiments of the memory include RAM (random access memory), ROM (read-only memory), hard disk, flash memory, replaceable computer floppy disk, optical disk, magnetic tape, or combinations thereof. Optical disks can include optical discs with read-only memory (CD-ROM), optical discs with read / write capabilities (CD-R / W), DVDs, Blu-ray discs, etc.

[0184] The processing unit (21) may be connected not only to the memory (22) but also to one or more interfaces (11, 12, 31, 32, 33) for displaying, transmitting, and / or receiving information. The interfaces may include one or more communication interfaces (11, 32, 33) and / or one or more user interfaces (12, 31). One or more communication interfaces may be configured to send and / or receive information, for example, to an MRT scanner, CT scanner, ultrasound camera, other computer systems, a network, a data storage device, etc. One or more communication interfaces may be configured to transmit and / or receive information via physical (wired) and / or wireless communication connections. One or more communication interfaces may include one or more interfaces for connecting to a network, such as using technologies like mobile phones, WiFi, satellite, cable, DSL, fiber optics, etc. In some embodiments, 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).

[0185] The user interface may include a display (31). The display (31) may be configured to display information to a user. Suitable embodiments of the display are liquid crystal displays (LCDs), light-emitting diode (LED) displays, plasma display panels (PDPs), etc. The user input interfaces (11, 12) may be wired or wireless and may be configured to receive information from a user in the computer system (1), for example for processing, storage, and / or display. Suitable embodiments of the user input interfaces are microphones, image or video recording devices (e.g., cameras), keyboards or keypads, joysticks, touch-sensitive interfaces (separate from or integrated into a touchscreen), etc. In some embodiments, the user interface may include automatic identification and data capture technology (AIDC) for machine-readable information. This may include barcodes, radio frequency identification (RFID), magnetic stripes, optical character recognition (OCR), integrated circuit cards (ICCs), etc. The user interface may also include one or more interfaces for communicating with peripheral devices such as printers.

[0186] One or more computer programs (40) may be stored in memory (22) and executed by processing unit (21) to program processing unit (21) to perform the functions described herein. The retrieval, loading, and execution of instructions of computer program (40) may be performed sequentially, thereby retrieving, loading, and executing the corresponding instructions. However, retrieval, loading, and / or execution may also be performed in parallel.

[0187] The computer system disclosed herein may be in the form of a laptop, notebook, netbook, and / or tablet PC; it may also be a component of an MRT scanner, CT scanner, or ultrasound diagnostic equipment.

[0188] The computer system disclosed herein may be in the form of a laptop, notebook, netbook, and / or tablet PC; it may also be a component of an MRT scanner, CT scanner, ultrasound diagnostic equipment, or PET scanner.

[0189] Another subject of this invention is a computer program product. Such a computer program product includes a non-volatile data carrier, such as a CD, DVD, USB flash drive, or other data storage medium. The computer program is stored on the data carrier. The computer program can be loaded into the main memory of a computer system (more specifically, into the main memory of the computer system of this disclosure), whereby it can cause the computer system to perform the following steps: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - Generate a third representation based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions (a first distribution function and a second distribution function); - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

[0190] Computer program products can also be sold in combination (as a package) with contrast agents. Such packages are also called kits. A kit includes both the contrast agent and the computer program product. A kit may also include the contrast agent and tools that allow the purchaser to obtain the computer program, such as downloading it from a webpage. These tools may include links to webpage addresses from which the computer program can be downloaded to a computer system connected to the internet. These tools may include codes (e.g., alphanumeric strings or QR codes, or data matrix codes or barcodes or other optically and / or electronically readable codes) that grant the purchaser access to the computer program. Such links and / or codes may, for example, be printed on the packaging of the contrast agent and / or on the instructions for use with the contrast agent. Therefore, a kit is a combination of products offered for sale together, including the contrast agent and the 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).

Claims

1. A computer-implemented method, comprising: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - A third representation is generated based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - The histogram is approximated by the sum of at least two distribution functions, wherein the at least two distribution functions are a first distribution function and a second distribution function; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

2. The method of claim 1, wherein the object of inspection is a living organism, and the area of ​​inspection is part of the living organism.

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

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

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

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 in signal intensity of those image elements in the first and second characterizations whose signal intensity is independent of the amount of contrast agent, and the second distribution function of the at least two distribution functions represents the difference in signal intensity of those image elements in the first and second characterizations whose signal intensity depends on the amount of contrast agent.

7. The method according to any one of claims 1 to 6, wherein the histogram has two peaks, the two peaks being 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, The determination of the signal-to-noise ratio includes: - Determine the maximum value of the first distribution function; - Determine the maximum value of the second distribution function; - Determine the distance between the maximum value of the second distribution function and the maximum value of the first distribution function; - Determine the width of the first distribution function; The signal-to-noise ratio is determined based on the distance between the maximum value of the second distribution function and the maximum value of the first distribution function, as well as the width of the first distribution function.

9. The method of claim 8, wherein the signal-to-noise ratio is or includes the ratio of the distance between the maximum value of the second distribution function and the maximum value of the first distribution function to the width of the first distribution function.

10. The method according to any one of claims 8 and 9, wherein the width of the first distribution function is the full width at half maximum (FWHM) of the first distribution function, or a value derived from the full FWHM of the first distribution function.

11. The method according to any one of claims 1 to 10, wherein the first distribution function 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 when generating the synthetic representation of the inspection area of ​​the inspection object comprises: - The signal-to-noise ratio is compared with a first predetermined threshold, and if the determined signal-to-noise ratio is less than the first threshold, measures to improve the signal-to-noise ratio are performed, and / or - Compare the signal-to-noise ratio (SNR) with a second predetermined threshold, and if the determined SNR is less than the second threshold, output a message indicating that the SNR is too low to generate a synthetic representation of the inspected area of ​​the inspected object, and / or - Identify measures to improve the signal-to-noise ratio based on the determined signal-to-noise ratio, and / or - Select a frequency-related weighting function based on the determined signal-to-noise ratio, and / or - Determine one or more parameters of the frequency-related weighting function based on the determined signal-to-noise ratio, and / or - Determine the gain factor based on the determined signal-to-noise ratio, and / or - Determine the 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: Generate a weighted third representation of the inspection area of ​​the inspection object, wherein the generation of the weighted third representation includes: multiplying the third representation by a frequency-related weighting function in the frequency domain; - Generate a fourth representation of the inspection area of ​​the inspection object, wherein the generation of the fourth representation includes: adding an α times the optionally weighted third representation to the first representation or the second representation, where α is a positive real number or a negative real number; - If the fourth representation is the representation of the inspection area of ​​the inspection object in the frequency domain: convert the fourth representation of the inspection area of ​​the inspection object in the frequency domain into the fourth representation of the inspection area of ​​the inspection object in the spatial domain; - Output and / or store the fourth representation of the inspection area of ​​the inspection object in the spatial domain, and / or transmit the fourth representation of the inspection area of ​​the inspection object in the spatial domain to a separate computer system.

14. A computer system, comprising: Input unit, Control and computing units, and Output unit, The control and computing unit is configured as follows: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - A third representation is generated based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or transmit the signal-to-noise ratio to an independent computer system and / or store the signal-to-noise ratio, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

15. A non-volatile computer-readable storage medium having software instructions stored thereon, the software instructions, when executed by a processor of a computer system, causing the computer system to perform the following steps: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - A third representation is generated based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

16. The use of contrast agents in radiological examination methods, said radiological examination methods including: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - A third representation is generated based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

17. A kit comprising a computer program product and a contrast agent, the computer program product including a computer program loadable into the main memory of a computer system, wherein the computer program causes the computer system to perform the following steps: - Receive or generate a first characterization, the first characterization representing the examination area of ​​the subject before or after the application of a first amount of contrast agent; - Receive or generate a second characterization, the second characterization representing the examination area of ​​the subject after the application of a second amount of the contrast agent, the second amount being greater than the first amount; - A third representation is generated based on the first representation and the second representation, wherein the generation of the third representation includes subtracting the first representation from the second representation; - Generate a histogram of the third representation; - Approximate the histogram using the sum of at least two distribution functions; - Determine the signal-to-noise ratio based on the parameters of the at least two distribution functions; - Output and / or store the signal-to-noise ratio, and / or transmit the signal-to-noise ratio to a separate computer system, and / or use the signal-to-noise ratio when generating a synthetic representation of the inspection area of ​​the inspection object.

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