Forecast of MRI images by means of a forecast model trained by supervised learning

Patent metadata

Field	Value
Publication number	US-11727571-B2
Application number	US-202017753838-A
Country	US
Kind code	B2
Filing date	Sep 14, 2020
Priority date	Sep 18, 2019
Publication date	Aug 15, 2023
Grant date	Aug 15, 2023

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.

First claim

The invention claimed is:

1. A computer-implemented method comprising:
receiving a plurality of magnetic resonance imaging (MRI) images, wherein at least one received MRI image of the plurality of MRI images shows an examination region before administering a contrast agent, and at least one received MRI image of the plurality of MRI images shows the examination region during a first time span after administering the contrast agent;
feeding the plurality of MRI images to a prediction model, the prediction model having been trained by means of supervised learning to predict based on MRI images, at least some of which show the examination region during the first time span after administering the contrast agent, one or more MRI images showing the examination region during a second time span, 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 and/or outputting the one or more predicted MRI images and/or storing the one or more predicted MRI images in a data storage medium.

2. The method of claim 1 , wherein the examination region is a liver or a portion of a liver of a mammal.

3. The method of claim 2 , wherein the first time span is chosen such that it shows the examination region in different phases, wherein the phases comprise a native phase, an arterial phase, a portal-vein phase, and a late phase, wherein at least one MRI image is received which shows the examination region in the native phase, at least one MRI image is received which shows the examination region during the arterial phase, at least one MRI image is received which shows the examination region in the portal-vein phase, and at least one MRI image is received which shows the examination region in the late phase.

4. The method of claim 1 , wherein the plurality of MRI images show a liver or a portion of a liver of a mammal prior to a time point TP 0 and during a time span from TP 0 to TP 1 and/or during a time span from TP 1 to TP 2 and/or during a time span from TP 2 to TP 3 and/or during a time span from TP 3 to TP 4 , at time point TP 0 the contrast agent being administered intravenously as a bolus and then reaching liver cells via liver arteries and liver veins, at time point TP 1 the contrast agent in the liver arteries reaching a maximum concentration, at time point TP 2 a signal intensity generated in the liver veins by the contrast agent assuming 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 TP 3 the contrast agent in the liver veins reaching a maximum concentration, at time point TP 4 a signal intensity generated in the liver cells by the contrast agent assuming a value which is the same size as a value of a signal intensity generated in the liver veins by the contrast agent.

5. 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.

6. The method of claim 1 , wherein the second time span is within a hepatobiliary phase.

7. The method of claim 1 , wherein the second time span starts at least 10 minutes after administration of the contrast agent.

8. The method of claim 1 , wherein the prediction model is an artificial neural network.

9. The method of claim 1 , wherein the contrast agent is a hepatobiliary contrast agent.

10. 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 a plurality of magnetic resonance imaging (MRI) images, wherein at least one received MRI image of the plurality of MRI images shows an examination region before administering a contrast agent, and at least one received MRI image of the plurality of MRI images shows the examination region during a first time span after administering the contrast agent,
the control and calculation unit being configured to predict one or more MRI images based on the plurality of MRI images, the one or more predicted MRI images showing 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.

11. 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, cause the computer system to execute the following:
receiving a plurality of magnetic resonance imaging (MRI) images, wherein at least one received MRI image of the plurality of MRI images shows an examination region before administering a contrast agent, and at least one received MRI image of the plurality of MRI images shows the examination region during a first time span after administering the contrast agent;
feeding the plurality of MRI images to a prediction model, the prediction model having been trained by means of supervised learning to predict based on MRI images, at least some of which show the examination region during the first time span after administering the contrast agent, one or more MRI images showing the examination region during a second time span, 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 and/or outputting the one or more predicted MRI images and/or storing the one or more predicted MRI images in a data storage medium.

12. 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 a plurality of MRI images, the plurality of 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 during a first time span 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 MRI images showing the examination region during the first time span, one or more MRI images showing the examination region during a second time span, 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 and/or outputting the one or more predicted MRI images and/or storing the one or more predicted MRI images in a data storage medium.

13. A kit comprising a contrast agent and the computer program product as claimed in claim 11 .

14. The use of the contrast agent in the MRI method of claim 12 , wherein the contrast agent is preferably a substance or a substance mixture with gadoxeti

Assignees

• Bayer Ag

Inventors

• Rohrer Martin
• Uber Iii Arthur

Classifications

• A61B5/055

  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06T7/0016 Primary

  involving temporal comparison · CPC title

• A61B5/004

  adapted for image acquisition of a particular organ or body part (A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062) · CPC title