Generation of artificial contrast-enhanced radiological images

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:
• generate a first representation (R1) or cause the receiving unit to receive a first representation (R1), wherein the first representation (R1) represents an examination region of an examination object without contrast agent or after administration of a first amount of a contrast agent;
• generate a second representation (R2) or to cause the receiving unit to receive a second representation (R2), wherein the second representation (R2) represents the examination region of the examination object after administration of a second amount of the contrast agent, the second amount being larger than the first amount;
• feed the first representation (R1) and the second representation (R2) to a trained machine-learning model (MT);
• wherein the trained machine-learning model (MT) has been trained on the basis of training data (TD),
• wherein the training data (TD) for each reference object of a plurality of reference objects comprise as target data:
• (i) a first reference representation (RR1) of a reference region of the reference object and a second reference representation (RR2) of the reference region of the reference object as input data and (ii) a third reference representation (RR3) of the reference region of the reference object,
• wherein the first reference representation (RR1) represents the reference region without contrast agent or after administration of a first amount of a contrast agent,
• wherein the second reference representation (RR2) represents the reference region after administration of a second amount of the contrast agent, the second amount being larger than the first amount,
• wherein the third reference representation (RR3) represents the reference region after administration of a third amount of the contrast agent, the third amount being different from the second amount,
• wherein the trained machine-learning model (MT) comprises two submodels:
• a first submodel (SM1T) and a second submodel (SM2), wherein the first submodel (SM1T) is configured and has been trained to determine, based on the first reference representation (RR1) and second reference representation (RR2) of each such reference object, at least one model parameter (MP) for the second submodel (SM2) and wherein the second submodel (SM2) is configured to generate, based on at least one of the first reference representation (RR1) and second reference representation (RR2) and on the at least one model parameter (MP) determined by the first submodel (SM1T), a synthetic third representation (RR3*);
• receive from the trained machine-learning model (MT) a synthetic third representation (R3*) of the examination region of the examination object, wherein the synthetic third representation (R3*) represents the reference region after administration of a third amount of the contrast agent, the third amount being different from the second amount;
• and
• cause the output unit to output the synthetic third representation (R3*) and to store it in a data storage medium and to transmit it to a separate computer system.

A computer program product comprising a data carrier on which there is stored a computer program that can be loaded into a working memory of a computer system, wherein the computer program causes the computer system to:

• provide a trained machine-learning model (MT);
• wherein the trained machine-learning model (MT) has been trained on the basis of training data (TD),
• wherein the training data (TD) for each reference object of a plurality of reference objects comprise as target data:
• (i) a first reference representation (RR1) of a reference region of the reference object and a second reference representation (RR2) of the reference region of the reference object as input data and (ii) a third reference representation (RR3) of the reference region of the reference object,
• wherein the first reference representation (RR1) represents the reference region without contrast agent or after administration of a first amount of a contrast agent,
• wherein the second reference representation (RR2) represents the reference region after administration of a second amount of the contrast agent, the second amount being larger than the first amount,
• wherein the third reference representation (RR3) represents the reference region after administration of a third amount of the contrast agent, the third amount being different from the second amount,
• wherein the trained machine-learning model (MT) comprises two submodels:
• a first submodel (SM1T) and a second submodel (SM2), wherein the first submodel (SM1T) is configured and has been trained to determine, based on the first reference representation (RR1) and second reference representation (RR2) of an examination region of a reference object, at least one model parameter (MP) for the second submodel (SM2) and wherein the second submodel (SM2) is configured to generate, based on at least one of the first reference representation (RR1) and second reference representation (RR2) and on the at least one model parameter (MP) determined by the first submodel (SM1T), a synthetic third reference representation (RR3*);
• provide a first representation (R1), wherein the first representation (R1) represents an examination region of an examination object without contrast agent or after administration of a first amount of a contrast agent;
• provide a second representation (R2), wherein the second representation (R2) represents the examination region of the examination object after administration of a second amount of the contrast agent, the second amount being larger than the first amount;
• feed the first representation (R1) and the second representation (R2) to the trained machine-learning model (MT);
• receive from the trained machine-learning model (MT) a synthetic third representation (R3*) of the examination region of the examination object, wherein the synthetic third representation (R3*) represents the reference region after administration of a third amount of the contrast agent, the third amount being different from the second amount;
• and
• output and store the synthetic third representation (R3*) and transmit the synthetic third representation (R3*) to a separate computer system.

A contrast agent for use in a radiological examination method, the method comprising:

• providing a trained machine-learning model (MT);
• wherein the trained machine-learning model (MT) has been trained on the basis of training data (TD),
• wherein the training data (TD) for each reference object of a plurality of reference objects comprise as target data:
• (i) a first reference representation (RR1) of a reference region of the reference object and a second reference representation (RR2) of the reference region of the reference object as input data and (ii) a third reference representation (RR3) of the reference region of the reference object,
• wherein the first reference representation (RR1) represents the reference region without contrast agent or after administration of a first amount of the contrast agent,
• wherein the second reference representation (RR2) represents the reference region after administration of a second amount of the contrast agent, the second amount being larger than the first amount,
• wherein the third reference representation (RR3) represents the reference region after administration of a third amount of the contrast agent, the third amount being different from the second amount,
• wherein the trained machine-learning model (MT) comprises two submodels:
• a first submodel (SM1T) and a second submodel (SM2), wherein the first submodel (SM1T) is configured and has been trained to determine, based on the first reference representation (RR1) and second reference representation (RR2) of an examination region of a reference object, at least one model parameter (MP) for the second submodel (SM2) and where the second submodel (SM2) is configured to generate, based on at least one of the first reference representation (RR1) and second reference representation (RR2) and on the at least one model parameter (MP) determined by the first submodel (SM1T), a synthetic third reference representation (RR3*);
• providing a first representation (R1), wherein the first representation (R1) represents an examination region of an examination object without contrast agent or after administration of a first amount of the contrast agent;
• providing a second representation (R2), wherein the second representation (R2) represents the examination region of the examination object after administration of a second amount of the contrast agent, the second amount being larger than the first amount;
• feeding the first representation (R1) and the second representation (R2) to the trained machine-learning model (MT);
• receiving from the trained machine-learning model (MT) a synthetic third representation (R3*) of the examination region of the examination object, wherein the synthetic third representation (R3*) represents the reference region after administration of a third amount of the contrast agent, the third amount being different from the second amount;
• and
• outputting and storing the synthetic third representation (R3*) and transmitting the synthetic third representation (R3*) to a separate computer system.