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US 2026/0087709 A1Published application
Generation of synthetic radiological images
AssigneeBayer Aktiengesellschaft
InventorsMatthias LENGA, Ivo Matteo BALTRUSCHAT, Felix Karl KREIS
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Abstract
The present invention relates to the technical field of radiology. The invention relates to a new approach for training a machine learning model for generating synthetic radiological images on the basis of measured radiological images and to the use of the trained machine learning model for generating synthetic radiological images.
Claims23
A computer-implemented method comprising:
- receiving and/or providing training data (TD), the training data (TD) comprising a set of input data and target data for each examination object of a multiplicity of examination objects, each set comprising:
- at least one input representation (R1, R2) of an examination region of the examination object in a first state as input data;
- and
- a target representation (TR) of the examination region of the examination object in a second state and a transformed target representation (TRT) as target data, the transformed target representation (TRT) representing at least part of the examination region of the examination object in a different space compared to the target representation (TR);
- training a machine-learning model (MLM), the machine-learning model (MLM) configured to generate on the basis of the at least one input representation (R1, R2) of the examination region of the examination object and model parameters (MP) a synthetic representation (SR) of the examination region of the examination object,
- wherein the training comprises for each examination object of the multiplicity of examination objects:
- feeding the at least one input representation (R1, R2) of the examination region of the examination object to the machine-learning model (MLM);
- receiving the synthetic representation (SR) of the examination region of the examination object from the machine-learning model (MLM);
- generating and/or receiving a transformed synthetic representation (SRT) on the basis of the synthetic representation (SR) and/or in relation to the synthetic representation (SR), the transformed synthetic representation (SRT) representing at least part of the examination region of the examination object in a different space compared to the synthetic representation (SR);
- quantifying the differences:
- i) between at least part of the synthetic representation (SR) and at least part of the target representation (TR);
- and
- ii) between at least part of the transformed synthetic representation (SRT) and at least part of the transformed target representation (TRT) by means of a loss function (L);
- and
- reducing the differences by modifying the model parameters (MP);
- and
- outputting and/or storing the trained machine-learning model (MLMt) and/or the model parameters (MP) and/or transmitting the trained machine-learning model (MLMt) and/or the model parameters (MP) to a separate computer system and/or using the trained machine-learning model (MLMt) for generation of a synthetic representation of the examination region of a new examination object.
14 more elements
(canceled)
(canceled)
A computer system comprising:
- a receiving unit;
- a control and calculation unit;
- and
- an output unit,
- wherein the control and calculation unit is configured to cause the receiving unit (11) to receive at least one input representation (R1*, R2*) of an examination region of a new examination object in a first state,
- wherein the control and calculation unit (12) is configured to input the received at least one input representation (R1*, R2*) into a trained machine-learning model (MLMt);
- the trained machine-learning model (MLMt) having been trained by means of training data (TD) to generate on the basis of at least one input representation (R1, R2) of an examination region of an examination object in a first state a synthetic representation (SR) of the examination region in a second state;
- the training data (TD) comprising for each examination object of a multiplicity of examination objects:
- i) at least one input representation (R1, R2) of the examination region,
- ii) a target representation (TR) of the examination region- and
- iii) a transformed target representation (TRT);
- the at least one input representation (R1, R2) representing the examination region of the examination object in the first state and the target representation (TR) representing the examination region of the examination object in the second state;
- the transformed target representation (TRT) representing at least part of the examination region of the examination object in a different space compared to the target representation (TR);
- the training of the machine-learning model (MLI) comprising reducing differences;
- i) between at least part of the synthetic representation (SR) and at least part of the target representation (TR);
- and
- ii) between at least part of a transformed synthetic representation (SRT) and at least part of the transformed target representation (TRT),
- wherein the control and calculation unit is configured to receive from the machine-learning model (MLMt) a synthetic representation (SR*) of the examination region of the new examination object in the second state, and
- wherein the control and calculation unit is configured to cause the output unit to output the received synthetic representation (SR*) and/or to store the received synthetic representation (SR*) and/or to transmit the received synthetic representation (SR*) to a separate computer system.
16 more elements
A computer program product comprising a computer program that can be loaded into a working memory of a computer system, wherein the computer program causes the computer system to execute the following:
- provide a trained machine-learning model (MLMt);
- the trained machine-learning model (MLMt) having been trained by means of training data (TD) to generate on the basis of at least one input representation (R1, R2) of an examination region of an examination object in a first state a synthetic representation (SR) of the examination region in a second state;
- the training data (TD) comprising for each examination object of a multiplicity of examination objects:
- i) at least one input representation (R1, R2) of the examination region;
- ii) a target representation (TR) of the examination region;
- and
- iii) a transformed target representation (TRT);
- the at least one input representation (R1, R2) representing the examination region of the examination object in the first state and the target representation (TR) representing the examination region of the examination object in the second state;
- the transformed target representation (TRT) representing at least part of the examination region of the examination object in a different space compared to the target representation (TR);
- the training of the machine-learning model (MLMt) comprising reducing differences:
- i) between at least part of the synthetic representation (SR) and at least part of the target representation (TR);
- and
- ii) between at least part of a transformed synthetic representation (SR) and at least part of the transformed target representation (TRT);
- receive at least one input representation (R1*, R2*) of an examination region of a new examination object in the first state;
- input the at least one input representation (R1*, R2*) of the examination region of the new examination object into the trained machine-learning model (MLMt);
- receive a synthetic representation (SR*) of the examination region of the new examination object in the second state from the trained machine-learning model (MLMt);
- and
- output and/or store the received synthetic representation (SR*) and/or transmit the received synthetic representation (SR*) to a separate computer system.
15 more elements
22-24. (canceled)
Dependencies are inferred from the claim text.