GENERATION OF MRI IMAGES OF THE LIVER WITHOUT CONTRAST ENHANCEMENT

Abstract

The present disclosure relates to the generation of artificial MRI images of the liver. The disclosure also relates to a method, a system and a computer program product for generating MRI images of the liver without contract enhancement.

Claims

1. A method comprising: receiving at least one first MRI image of an examination object, the at least one first MRI image showing a liver or a portion of a liver of the examination object, blood vessels in the liver being depicted with contrast enhancement as a result of a contrast agent, receiving at least one second MRI image of the same examination object, the at least one second MRI image showing the same liver or the same portion of the liver, healthy liver cells being depicted with contrast enhancement as a result of the contrast agent, feeding the received MRI images to a prediction model, the prediction model having been trained by means of supervised learning to predict, based on MRI images showing a liver or a portion of a liver of an examination object and in which blood vessels in the liver are depicted with contrast enhancement as a result of a contrast agent, and based on MRI images of the same liver or the same portion of the liver of the same examination object, in which healthy liver cells are depicted with contrast enhancement as a result of the contrast agent, one or more MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by the contrast agent, receiving from the prediction model one or more predicted MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by the contrast agent, 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 according to claim 1, wherein the at least one first MRI image is a T1-weighted depiction of the liver or the portion of the liver in a dynamic phase after administration of a hepatobiliary, paramagnetic contrast agent.

3. The method according to claim 2, wherein the at least one first MRI image is an MRI image which: (i) shows the liver or a portion of the liver of the examination object during an arterial phase, (ii) shows the same liver or the same portion of the liver of the same examination object during a venous phase, and (iii) shows the same liver or the same portion of the liver of the same examination object during a late phase.

4. The method according to claim 1, wherein the at least one second MRI image is a T1-weighted depiction of the liver or the portion of the liver in a hepatobiliary phase after administration of a hepatobiliary, paramagnetic contrast agent or of an extracellular, paramagnetic contrast agent.

5. The method according to claim 2, wherein the at least one second MRI image having a T1-weighted depiction of the liver or the portion of the liver in a hepatobiliary phase after a first administration of the hepatobiliary, paramagnetic contrast agent into the examination object is recorded, and the at least one first MRI image having a T1-weighted depiction of the same liver or the portion of the same liver in the dynamic phase after a second administration of the hepatobiliary, paramagnetic contrast agent or of an extracellular, paramagnetic contrast agent into the same examination object is recorded.

6. The method according to claim 1, wherein the contrast agent is a substance or a substance mixture with gadoxetic acid or a gadoxetic acid salt as contrast-enhancing active substance, preferably the disodium salt of gadoxetic acid.

7. The method according to claim 1, wherein the examination object is a mammal, preferably a human.

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

9. 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 one first MRI image of an examination object, the at least one first MRI image showing a liver or a portion of a liver of the examination object, blood vessels in the liver being depicted with contrast enhancement as a result of a contrast agent, the control and calculation unit being configured to prompt the receiving unit to receive at least one second MRI image of the examination object, the at least one second MRI image showing the same liver or the same portion of the liver, healthy liver cells being depicted with contrast enhancement as a result of the contrast agent, the control and calculation unit being configured to predict one or more MRI images based on the received MRI images, the one or more predicted MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by the contrast agent, and the control and calculation unit being configured to prompt the output unit to display the one or more predicted MRI images, to output them or to store them in a data storage medium.

10. A computer program product comprising a computer program which can be loaded into a memory of a computer, where it prompts the computer to execute the following: receiving at least one first MRI image of an examination object, the at least one first MRI image showing a liver or a portion of a liver of the examination object, blood vessels in the liver being depicted with contrast enhancement as a result of a contrast agent, receiving at least one second MRI image of the same examination object, the at least one second MRI image showing the same liver or the same portion of the liver, healthy liver cells being depicted with contrast enhancement as a result of the contrast agent, feeding the received MRI images to a prediction model, the prediction model having been trained by means of supervised learning to predict, based on MRI images showing a liver or a portion of a liver of an examination object and in which blood vessels in the liver are depicted with contrast enhancement as a result of a contrast agent, and based on MRI images of the same liver or the same portion of the liver of the same examination object, in which healthy liver cells are depicted with contrast enhancement as a result of the contrast agent, one or more MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by the contrast agent, receiving one or more predicted MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by the contrast agent, as output from the prediction model, 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.

11. The computer program product according to claim 10, wherein, the at least one first MRI image is a T1-weighted depiction of the liver or the portion of the liver in a dynamic phase after administration of a hepatobiliary, paramagnetic contrast agent.

12. Use of a contrast agent in an MRI method, the MRI method comprising: administering the contrast agent, the contrast agent spreading in a liver of an examination object, generating at least one first MRI image, the at least one first MRI image showing the liver or a portion of the liver of the examination object, blood vessels in the liver being depicted with contrast enhancement as a result of the contrast agent, generating at least one second MRI image, the at least one second MRI image showing the same liver or the same portion of the liver, healthy liver cells being depicted with contrast enhancement as a result of 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, on MRI images showing a liver or a portion of a liver of an examination object and in which the blood vessels in the liver are depicted with contrast enhancement as a result of a contrast agent, and based on MRI images of the same liver or the same portion of the liver of the same examination object, in which healthy liver cells are depicted with contrast enhancement as a result of the contrast agent, one or more MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by the contrast agent, receiving one or more predicted MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by the contrast agent, as output from the prediction model, 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. (canceled)

14. The contrast agent for use according to claim 12, wherein the contrast agent is a substance or a substance mixture with gadoxetic acid or a gadoxetic acid salt as contrast-enhancing active substance, preferably the disodium salt of gadoxetic acid.

15. A kit comprising a contrast agent according to claim 12, and a computer program product according to claim 10.

16. The computer program product according to claim 11, wherein the at least one first MRI image is an MRI image which (i) shows the liver or a portion of the liver of the examination object during an arterial phase, (ii) shows the same liver or the same portion of the liver of the same examination object during a venous phase, and (iii) shows the same liver or the same portion of the liver of the same examination object during a late phase.

17. The computer program product according to claim 10, wherein the at least one second MRI image is a T1-weighted depiction of the liver or the portion of the liver in a hepatobiliary phase after administration of a hepatobiliary, paramagnetic contrast agent or of an extracellular, paramagnetic contrast agent.

18. The computer program product according to claim 11, wherein the at least one second MRI image having a T1-weighted depiction of the liver or the portion of the liver in a hepatobiliary phase after a first administration of the hepatobiliary, paramagnetic contrast agent into the examination object is recorded, and the at least one first MRI image having a T1-weighted depiction of the same liver or the portion of the same liver in the dynamic phase after a second administration of the hepatobiliary, paramagnetic contrast agent or of an extracellular, paramagnetic contrast agent into the same examination object is recorded.

19. The computer program product according to claim 10, wherein the contrast agent is a substance or a substance mixture with gadoxetic acid or a gadoxetic acid salt as contrast-enhancing active substance, preferably the disodium salt of gadoxetic acid.

20. The computer program product according to claim 10, wherein the prediction model is an artificial neural network.

Description

[0127] FIG. 1 shows schematically the temporal profile of the concentrations of contrast agent in the liver arteries (A), the liver veins (P) and the liver cells (L). The concentrations are depicted in the form of the signal intensities I in the stated areas (liver arteries, liver veins, liver cells) in the magnetic resonance measurement as a function of the time t. Upon an intravenous bolus injection, the concentration of the contrast agent rises in the liver arteries (A) first of all (dashed curve). The concentration passes through a maximum and then drops. The concentration in the liver veins (P) rises more slowly than in the liver arteries and reaches its maximum later (dotted curve). The concentration of the contrast agent in the liver cells (L) rises slowly (continuous curve) and reaches its maximum only at a very much later time point (not depicted in FIG. 1). A few characteristic time points can be defined: At time point TP0, contrast agent is administered intravenously as a bolus. At time point TP1, the concentration (the signal intensity) of the contrast agent in the liver arteries reaches its maximum. At time point TP2, the curves of the signal intensities for the liver arteries and the liver veins intersect. At time point TP3, the concentration (the signal intensity) of the contrast agent in the liver veins passes through its maximum. At time point TP4, the curves of the signal intensities for the liver veins and the liver cells intersect. At time point TP5, the concentrations in the liver arteries and the liver veins have dropped to a level at which they no longer cause a measurable contrast enhancement.

[0128] FIG. 2 shows schematically an example of a shortened MRI image acquisition procedure. In a shortened MRI image acquisition procedure, a contrast agent is first administered (1). The examination object is introduced to the MRI after a certain waiting period, for example 10 to 20 minutes (2). Thereafter, the MRI process is started and an MRI of the liver or a portion thereof in the hepatobiliary phase is first carried out (3). Thereafter, a further intravenous bolus injection (4) is administered to the examination object and an MRI of the liver or a portion thereof in the dynamic phase is directly subsequently carried out.

[0129] FIG. 3 shows schematically a preferred embodiment of the system according to the disclosure. The system (10) comprises a receiving unit (11), a control and calculation unit (12) and an output unit (13).

[0130] The control and calculation unit (12) is configured to prompt the receiving unit (11) to receive at least one first MRI image of an examination object, the at least one first MRI image showing a liver or a portion of a liver of the examination object, blood vessels in the liver being depicted with contrast enhancement as a result of a contrast agent.

[0131] The control and calculation unit (12) is further configured to prompt the receiving unit (11) to receive at least one second MRI image of an examination object, the at least one second MRI image showing the same liver or the same portion of the liver, healthy liver cells being depicted with contrast enhancement as a result of a contrast agent.

[0132] The control and calculation unit (12) is further configured to predict one or more MRI images on the basis of the received MRI images, the one or more predicted MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by a contrast agent.

[0133] The control and calculation unit (12) is further configured to prompt the output unit (13) to display the at least one predicted MRI image, to output it or to store it in a data storage medium.

[0134] FIG. 4 shows schematically and exemplarily one embodiment of the method according to the disclosure. The method (100) comprises the steps: [0135] (110) receiving at least one first MRI image of an examination object, the at least one first MRI image showing a liver or a portion of a liver of the examination object, blood vessels in the liver being depicted with contrast enhancement as a result of a contrast agent, [0136] (120) receiving at least one second MRI image of the same examination object, the at least one second MRI image showing the same liver or the same portion of the liver, healthy liver cells being depicted with contrast enhancement as a result of a contrast agent, [0137] (130) feeding the received MRI images to a prediction model, the prediction model having been trained by means of supervised learning to predict, on the basis of MRI images showing a liver or a portion of a liver of an examination object and in which the blood vessels in the liver are depicted with contrast enhancement as a result of a contrast agent, and on the basis of MRI images of the same liver or the same portion of the liver of the same examination object, in which healthy liver cells are depicted with contrast enhancement as a result of a contrast agent, one or more MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by a contrast agent, [0138] (140) receiving from the prediction model one or more predicted MRI images showing the liver or a portion of the liver of the examination object without a contrast enhancement caused by a contrast agent, [0139] (150) 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.

[0140] FIG. 5 shows exemplarily and schematically a further embodiment of the present disclosure. A first MRI image (1) is provided, the first MRI image showing a liver or a portion of a liver of an examination object, blood vessels in the liver being depicted with contrast enhancement (signal enhancement) as a result of a contrast agent.

[0141] A second MRI image (2) is provided, the second MRI image showing the same liver or the same portion of the liver as the first MRI image, the healthy liver tissue (parenchyma) being depicted with contrast enhancement (signal enhancement) as a result of a contrast agent.

[0142] The first MRI image (1) and the second MRI image (2) are fed to a prediction model (PM).

[0143] The prediction model (PM) is configured to generate, on the basis of the first MRI image (1) and the second MRI image (2), a third MRI image (3) showing an MRI image without a contrast enhancement caused by a contrast agent.

[0144] The prediction model was preferably created with the aid of a self-learning algorithm in a supervised machine learning process with a training data set. The training data set comprises a multiplicity of first MRI images, second MRI images and the associated third MRI images, the third MRI images having actually been recorded, e.g. before administration of a first intravenous bolus of the contrast agent.

[0145] The self-learning algorithm generates, during machine learning, a statistical model which is based on the training data. This means that the examples are not simply learnt by heart, but that the algorithm “recognizes” patterns and regularities in the training data. The algorithm can thus also assess unknown data. Validation data can be used to test the quality of the assessment of unknown data.

[0146] The self-learning algorithm is trained by means of supervised learning, i.e. first and second MRI images are presented to the algorithm and it is informed of which third MRI images are associated with the particular first and second MRI images. The algorithm then learns a relationship between the MRI images in order to predict (to calculate) third MRI images for unknown first and second MRI images.

[0147] Self-learning algorithms trained by means of supervised learning are widely described in the prior art (see, for example, C. Perez: Machine Learning Techniques: Supervised Learning and Classification, Amazon Digital Services LLC-Kdp Print Us, 2019, ISBN 1096996545, 9781096996545).

[0148] Preferably, the prediction model is an artificial neural network.

[0149] Such an artificial neural network comprises at least three layers of processing elements: a first layer with input neurons (nodes), an N-th layer with at least one output neuron (nodes) and N-2 inner layers, where N is a natural number and greater than 2.

[0150] The input neurons serve to receive digital MRI images as input values. Normally, there is one input neuron for each pixel or voxel of a digital MRI image. There can be additional input neurons for additional input values (e.g. information about the examination region, about the examination object and/or about conditions which prevailed when generating the MRI images).

[0151] In such a network, the output neurons serve to generate a third MRI image for a first and a second MRI image.

[0152] The processing elements of the layers between the input neurons and the output neurons are connected to one another in a predetermined pattern with predetermined connection weights.

[0153] Preferably, the artificial neural network is a so-called convolutional neural network (CNN for short).

[0154] A convolutional neural network is capable of processing input data in the form of a matrix. This makes it possible to use digital MRI images depicted as a matrix (e.g. width×height×colour channels) as input data. By contrast, a normal neural network, for example in the form of a multilayer perceptron (MLP), requires a vector as input, i.e. to use an MRI image as input, the pixels or voxels of the MRI image would have to be rolled out successively in a long chain. As a result, normal neural networks are, for example, not capable of recognizing objects in an MRI image independently of the position of the object in the MRI image. The same object at a different position in the MRI image would have a completely different input vector.

[0155] A CNN consists essentially of filters (convolutional layer) and aggregation layers (pooling layer) which are repeated alternately and, at the end, of one layer or multiple layers of “normal” completely connected neurons (dense/fully connected layer).

[0156] Details can be gathered from the prior art (see, for example: S. Khan et al.: A Guide to Convolutional Neural Networks for Computer Vision, Morgan & Claypool Publishers 2018, ISBN 1681730227, 9781681730226).

[0157] The training of the neural network can, for example, be carried out by means of a backpropagation method. In this connection, what is striven for, for the network, is a mapping of given input vectors onto given output vectors that is as reliable as possible. The mapping quality is described by an error function. The goal is to minimize the error function. In the case of the backpropagation method, an artificial neural network is taught by altering the connection weights.

[0158] In the trained state, the connection weights between the processing elements contain information regarding the relationship between the contrast-enhanced MRI images of the dynamic and hepatobiliary phase and MRI images without contrast enhancement that can be used in order to predict one or more MRI images which show an examination region without contrast enhancement and which are calculated only by means of contrast-enhanced MRI images of the same examination region.

[0159] A cross-validation method can be used in order to divide the data into training and validation data sets. The training data set is used in the backpropagation training of network weights. The validation data set is used in order to check the accuracy of prediction with which the trained network can be applied to unknown pluralities of MRI images.