# US 2023/0410312 A1 · Forecast of mri images by means of a forecast model trained by supervised learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0410312 A1 - Document ID: US20230410312A1 - Status: Published application - Kind: A1 - Application number: 18/340,051 - Filed: 2023-06-23 - Published: 2023-12-21 - Earliest claimed: 2019-09-18 - Assignees: Bayer Aktiengesellschaft - Applicants: Bayer Aktiengesellschaft - Inventors: Martin Rohrer; Arthur Uber, III - Web page: https://patentgrep.com/patent/US20230410312A1 ## CPC - G01R 33/5601: {involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent} - G06T 7/0016: {involving temporal comparison} - A61B 5/004: {adapted for image acquisition of a particular organ or body part (A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062)} - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - A61B 5/4244: {liver} - A61K 49/103: {the complex-forming compound being acyclic, e.g. DTPA} - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/09: Supervised learning - G06T 2207/10016: Video; Image sequence - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30056: Liver; Hepatic - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - A61K: PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES (devices or methods specially adapted for bringing pharmaceutical products into particular physical or administering forms A61J3/00; chemical aspects of, or use of materials for deodorisation of air, for disinfection or sterilisation, or for bandages, dressings, absorbent pads or surgical articles A61L; soap compositions C11D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract The present disclosure deals with the quickening of MRI examinations. Subjects of the present disclosure are a method, a system, a computer program product, a use, a contrast agent for use and a kit. ## Claims ### Claim 1 A computer-implemented method comprising: receiving at least two magnetic resonance imaging (MRI) images, wherein at least one received MRI image of the at least two MRI images shows an examination region before administering a contrast agent, and at least one received MRI image of the at least two MRI images shows the examination region after administering the contrast agent; feeding the at least two MRI images to a prediction model, the prediction model having been trained by means of supervised learning to predict based on a multiplicity of reference MRI images, some of which show the examination region during a first time span, some of which show the examination region during a second time span, the first time span starting before administering the contrast agent and ending at a time span after administering the contrast agent, and the second time span following the first time span chronologically; generating one or more predicted MRI images showing the examination region during the second time span by means of the prediction model, the second time span following the first time span chronologically; and displaying the one or more predicted MRI images, outputting the one or more predicted MRI images, or storing the one or more predicted MRI images in a data storage medium. ### Claim 2 (depends on 1) The method of claim 1, wherein the examination region is a liver or a portion of a liver of a mammal. ### Claim 3 (depends on 2) The method of claim 2, wherein the first time span is chosen such that the at least two MRI images show the examination region in different phases, wherein the phases comprise a native phase, an arterial phase, a portal-vein phase, and/or a late phase, wherein at least one MRI image is received which shows the examination region in the native phase, and at least one MRI image is received which shows the examination region during the arterial phase, and/or at least one MRI image is received which shows the examination region in the portal-vein phase, and/or at least one MRI image is received which shows the examination region in the late phase. ### Claim 4 (depends on 1) The method of claim 1, wherein the at least two MRI images show a liver or a portion of a liver of a mammal prior to a time point TP0 and during a time span from TP0 to TP1 and/or during a time span from TP1 to TP2 and/or during a time span from TP2 to TP3 and/or during a time span from TP3 to TP4, at time point TP0 the contrast agent is administered intravenously as a bolus and then reaches liver cells via liver arteries and liver veins, at time point TP1 the contrast agent in the liver arteries reaches a maximum concentration, at time point TP2 a signal intensity generated in the liver veins by the contrast agent assumes a value which is the same size as a value of a signal intensity generated in the liver arteries by the contrast agent, at time point TP3 the contrast agent in the liver veins reaches a maximum concentration, at time point TP4 a signal intensity generated in the liver cells by the contrast agent assumes a value which is the same size as a value of a signal intensity generated in the liver veins by the contrast agent. ### Claim 5 (depends on 1) The method of claim 1, wherein the first time span starts within a time span of from one minute to one second before administration of the contrast agent or with the administration of the contrast agent, and lasts for a time span of from 2 minutes to 15 minutes from the administration of the contrast agent. ### Claim 6 (depends on 1) The method of claim 1, wherein the second time span is within a hepatobiliary phase. ### Claim 7 (depends on 1) The method of claim 1, wherein the second time span starts at least 10 minutes after administration of the contrast agent. ### Claim 8 (depends on 1) The method of claim 1, wherein the prediction model is an artificial neural network. ### Claim 9 (depends on 1) The method of claim 1, wherein the contrast agent is a hepatobiliary contrast agent. ### Claim 10 (depends on 9) The method of claim 9, wherein the contrast agent is a disodium salt of gadolinium ethoxybenzyl diethylenetriaminepentaacetic acid. ### Claim 11 A system comprising: a receiving unit; a control and calculation unit; and an output unit; wherein the control and calculation unit being configured to prompt the receiving unit to receive at least two magnetic resonance imaging (MRI) images, wherein at least one received MRI image of the at least two MRI images shows an examination region before administering a contrast agent, and at least one received MRI image of the at least two MRI images shows the examination region after administering the contrast agent, the control and calculation unit being configured to predict one or more MRI images by means of a prediction model based on the at least two MRI images, wherein the prediction model was trained by means of supervised learning to predict based on a multiplicity of reference MRI images, some of which show the examination region during a first time span, some of which show the examination region during a second time span, the first time span starting before administering the contrast agent and ending at a time span after administering the contrast agent, and the second time span following the first time span chronologically, wherein the one or more predicted MRI images show the examination region during a second time span, the second time span following the first time span chronologically, and the control and calculation unit being configured to prompt the output unit to display the one or more predicted MRI images, to output the one or more predicted MRI images, or to store the one or more predicted MRI images in a data storage medium. ### Claim 12 A computer program product comprising at least one non-transitory computer-readable medium comprising one or more instructions that, when executed by a computer system, the computer system to execute the following: receiving at least two magnetic resonance imaging (MRI) images, wherein at least one received MRI image of the at least two MRI images shows an examination region before administering a contrast agent, and at least one received MRI image of the at least two MRI images shows the examination region after administering the contrast agent; feeding the at least two MRI images to a prediction model, the prediction model having been trained by means of supervised learning to predict based on a multiplicity of reference MRI images, some of which show the examination region during a first time span, some of which show the examination region during a second time span, the first time span starting before administering the contrast agent and ending at a time span after administering the contrast agent, and the second time span following the first time span chronologically; generating one or more predicted MRI images showing the examination region during the second time span by means of the prediction model, the second time span following the first time span chronologically; and displaying the one or more predicted MRI images, outputting the one or more predicted MRI images, or storing the one or more predicted MRI images in a data storage medium. ### Claim 13 (depends on 12) The computer program product of claim 12, wherein the first time span is chosen such that the at least two MRI images show the examination region in different phases, wherein the phases comprise a native phase, an arterial phase, a portal-vein phase, and/or a late phase, wherein at least one MRI image is received which shows the examination region in the native phase, and at least one MRI image is received which shows the examination region during the arterial phase, and/or at least one MRI image is received which shows the examination region in the portal-vein phase, and/or at least one MRI image is received which shows the examination region in the late phase. ### Claim 14 (depends on 12) The computer program product of claim 12, wherein the at least two MRI images show a liver or a portion of a liver of a mammal prior to a time point TP0 and during a time span from TP0 to TP1 and/or during a time span from TP1 to TP2 and/or during a time span from TP2 to TP3 and/or during a time span from TP3 to TP4, at time point TP0 the contrast agent is administered intravenously as a bolus and then reaches liver cells via liver arteries and liver veins, at time point TP1 the contrast agent in the liver arteries reaches a maximum concentration, at time point TP2 a signal intensity generated in the liver veins by the contrast agent assumes a value which is the same size as a value of a signal intensity generated in the liver arteries by the contrast agent, at time point TP3 the contrast agent in the liver veins reaches a maximum concentration, at time point TP4 a signal intensity generated in the liver cells by the contrast agent assumes a value which is the same size as a value of a signal intensity generated in the liver veins by the contrast agent. ### Claim 15 (depends on 12) The computer program product of claim 12, wherein the first time span starts within a time span of from one minute to one second before the administration of the contrast agent or with the administration of the contrast agent, and lasts for a time span of from 2 minutes to 15 minutes. ### Claim 16 (depends on 12) The computer program product of claim 12, wherein the second time span is within a hepatobiliary phase. ### Claim 17 (depends on 12) The computer program product of claim 12, wherein the prediction model is an artificial neural network. ### Claim 18 (depends on 11) A kit comprising a contrast agent and the system as claimed in claim 11. ### Claim 19 (depends on 18) The kit of claim 18, wherein the contrast agent is a disodium salt of gadolinium ethoxybenzyl diethylenetriaminepentaacetic acid. ### Claim 20 Use of a contrast agent in a magnetic resonance imaging (MRI) method, the MRI method comprising the following: administering the contrast agent, the contrast agent spreading in an examination region; generating at least two MRI images, the at least two MRI images comprising at least one MRI image of the examination region before administering the contrast agent and comprising at least one MRI image of the examination region after administering the contrast agent; feeding the generated MRI images to a prediction model, the prediction model having been trained by means of supervised learning to predict, based on a multiplicity of reference MRI images, some of which show the examination region during a first time span, some of which show the examination region during a second time span, the first time span starting before administering the contrast agent and ending at a time span after administering the contrast agent, and the second time span following the first time span chronologically; receiving one or more predicted MRI images showing the examination region during the second time span, as output from the prediction model, the second time span following the first time span chronologically; and displaying the one or more predicted MRI images, outputting the one or more predicted MRI images, or storing the one or more predicted MRI images in a data storage medium. ### Claim 21 (depends on 20) The use of the contrast agent in the MRI method of claim 20, wherein the contrast agent is preferably a substance or a substance mixture with gadoxetic acid or a gadoxetic acid salt as contrast-enhancing active substance. ### Claim 22 (depends on 21) The use of the contrast agent in the MRI method of claim 21, wherein the contrast agent is a disodium salt of gadolinium ethoxybenzyl diethylenetriaminepentaacetic acid.