ESTIMATING PHARMACOKINETICS OF A CONTRAST MEDIUM THROUGH A CARDIOVASCULAR SYSTEM ANALYTICAL MODEL AND/OR NEURAL NETWORK ALGORITHM

1 . A computer-implemented method, comprising:

training a neural network algorithm to estimate, based on a measurement indicative of a reference contrast medium bolus, pharmacokinetics of a contrast medium through a cardiovascular system of a patient, the training being dependent on at least one loss; and

configuring an angiographic imaging protocol based on an estimate of the pharmacokinetics of the contrast medium obtained from the neural network algorithm, wherein

the at least one loss includes a loss determined based on a compartment model of the cardiovascular system, the compartment model being based on a fragmentation of the cardiovascular system into multiple compartments and associated differential equations defining a change in a concentration of the contrast medium as a function of time and a concentration of the contrast medium at a respective compartment.

2 . The computer-implemented method of claim 1 ,

wherein the loss that is determined based on the compartment model is based on a comparison between time and spatial derivatives of a prediction of the neural network algorithm for the pharmacokinetics of the contrast medium and further time and spatial derivatives of the concentration of the contrast medium as defined by the associated differential equations of the compartment model.

3 . A computer-implemented method, comprising:

training a neural network algorithm to estimate, based on a measurement indicative of a reference contrast medium bolus, pharmacokinetics of a contrast medium through a cardiovascular system of a patient, the training being dependent on at least one loss; and

configuring an angiographic imaging protocol based on an estimate of the pharmacokinetics of the contrast medium obtained from the neural network algorithm, wherein

the at least one loss includes a loss determined based on a linear time invariant model of the cardiovascular system, the linear time invariant model being based on a response function of a vessel of the cardiovascular system with respect to a time-dependent inflow of the contrast medium.

4 . The computer-implemented method of claim 1 ,

wherein the measurement includes time series data that includes samples that are irregularly spaced in the time domain, and

wherein the neural network algorithm includes a Neural Ordinary Differential Equations algorithm.

5 . The computer-implemented method of claim 1 ,

wherein the at least one loss includes multiple losses, and

wherein the method further includes weighting the multiple losses.

6 . A computer-implemented method, comprising:

obtaining an estimate of pharmacokinetics of a contrast medium through a cardiovascular system of a patient, the estimate being obtained from a neural network algorithm that obtains, as input, at least a three-dimensional image of the patient; and

configuring an angiographic imaging protocol based on the estimate of the pharmacokinetics of the contrast medium.

7 . A computer-implemented method, comprising:

determining one or more parameter values of at least one free parameter of an analytical model that estimates pharmacokinetics of a contrast medium through a cardiovascular system of a patient using an optimization algorithm, the optimization algorithm operating based on an input including a measurement indicative of a reference contrast medium bolus; and

configuring an angiographic imaging protocol based on an estimate of the pharmacokinetics of the contrast medium obtained from the analytical model.

8 . The computer-implemented method of claim 7 , wherein the at least one free parameter includes a time-dependent in-flow concentration of a contrast-medium measurement bolus.

9 . A computer-implemented method, comprising:

obtaining an estimate of pharmacokinetics of a contrast medium through a cardiovascular system of a patient, the estimate being obtained from a neural network algorithm that includes an operator layer implementing an analytical model that also estimates the pharmacokinetics of the contrast medium through the cardiovascular system of the patient; and

configuring an angiographic imaging protocol based on the estimate of the pharmacokinetics of the contrast medium obtained from the neural network algorithm.

10 . The computer-implemented method of claim 9 ,

wherein the analytical model includes at least one free parameter; and

wherein the method further includes determining one or more parameter values of the at least one free parameter using an optimization algorithm, the optimization algorithm operating based on an input including a measurement indicative of a reference contrast medium bolus.

11 . A computer-implemented method, comprising:

determining multiple pre-estimates of pharmacokinetics of a contrast medium through a cardiovascular system of a patient based on at least one of multiple models or neural network algorithms;

determining an estimate of the pharmacokinetics based on a weighted combination of the multiple pre-estimates; and

configuring an angiographic imaging protocol based on the estimate of the pharmacokinetics.

12 . The computer-implemented method of claim 11 , further comprising:

determining weights of the weighted combination based on ensemble learning.

13 . A method executed by at least one processor of a computing device upon loading program code from a memory of the computing device, the method comprising:

determining one or more estimates of pharmacokinetics of a contrast medium through a cardiovascular system of a patient based on one or more analytical models and one or more neural network algorithms; and

configuring an angiographic imaging protocol based on the one or more estimates of pharmacokinetics of the contrast medium.

14 . A computing device comprising:

at least one processor and a memory, the at least one processor being configured to load program code from the memory and to execute the program code, wherein the at least one processor, upon executing the program code, is configured to cause the computing device to perform the method of claim 1 .

15 . A non-transitory computer-readable medium storing computer-executable instructions that, when executed by at least one processor at a computing device, cause the computing device to perform the method of claim 1 .

16 . A computing device comprising:

at least one processor and a memory, the at least one processor being configured to load program code from the memory and to execute the program code, wherein the at least one processor, upon executing the program code, is configured to cause the computing device to perform the method of claim 3 .

17 . A computing device comprising:

at least one processor and a memory, the at least one processor being configured to load program code from the memory and to execute the program code, wherein the at least one processor, upon executing the program code, is configured to cause the computing device to perform the method of claim 6 .

18 . A computing device comprising:

at least one processor and a memory, the at least one processor being configured to load program code from the memory and to execute the program code, wherein the at least one processor, upon executing the program code, is configured to cause the computing device to perform the method of claim 7 .

19 . A computing device comprising:

at least one processor and a memory, the at least one processor being configured to load program code from the memory and to execute the program code, wherein the at least one processor, upon executing the program code, is configured to cause the computing device to perform the method of claim 9 .

20 . A computing device comprising:

at least one processor and a memory, the at least one processor being configured to load program code from the memory and to execute the program code, wherein the at least one processor, upon executing the program code, is configured to cause the computing device to perform the method of claim 11 .

21 . A computing device comprising:

at least one processor and a memory, the at least one processor being configured to load program code from the memory and to execute the program code, wherein the at least one processor, upon executing the program code, is configured to cause the computing device to perform the method of claim 13 .

22 . The computer-implemented method of claim 2 ,

wherein the measurement includes time series data that includes samples that are irregularly spaced in the time domain, and

wherein the neural network algorithm includes a Neural Ordinary Differential Equations algorithm.

23 . The computer-implemented method of claim 2 ,

wherein the at least one loss includes multiple losses, and

wherein the method further includes weighting the multiple losses.

24 . The computer-implemented method of claim 3 ,

wherein the measurement includes time series data that includes samples that are irregularly spaced in the time domain, and

wherein the neural network algorithm includes a Neural Ordinary Differential Equations algorithm.

25 . The computer-implemented method of claim 3 ,

wherein the at least one loss includes multiple losses, and

wherein the method further includes weighting the multiple losses.