BUILDING A MACHINE-LEARNING MODEL TO PREDICT SEMANTIC CONTEXT INFORMATION FOR CONTRAST-ENHANCED MEDICAL IMAGING MEASUREMENTS

1 . A computer-implemented method, comprising:

generating a pre-trained machine-learning model by unsupervised pre-training of a machine-learning model for predicting time-related information from at least one pre-training image, the at least one pre-training image acquired by a contrast-enhanced medical imaging system using a contrast-enhanced measurement of a patient with multiple contrast agent distribution phases during an observation period, and the time-related information being associated with one or more points of time during the observation period; and

building a further machine-learning model using at least part of the pre-trained machine-learning model, the further machine-learning model being for predicting semantic context information from at least one inference image acquired by the contrast-enhanced medical imaging system using the contrast-enhanced measurement or a further contrast-enhanced measurement.

2 . The computer-implemented method of claim 1 ,

wherein said at least one pre-training image is acquired by the contrast-enhanced medical imaging system at a first point in time during the observation period, and

wherein said time-related information includes information associated with an acquisition by the contrast-enhanced medical imaging system at a second point in time during the observation period, the second point in time being different than the first point in time.

3 . The computer-implemented method of claim 1 , wherein said unsupervised pre-training of a machine-learning model for predicting time-related information comprises:

obtaining said at least one pre-training image of the patient acquired by the contrast-enhanced medical imaging system at a first point in time during the observation period;

applying said machine-learning model to the at least one pre-training image, wherein said time-related information is predicted for a second point in time during the observation period;

obtaining ground-truth information based on a further image acquired by the contrast-enhanced medical imaging system at the second point in time; and

training the machine-learning model based on comparing the ground-truth information and the time-related information predicted for the second point in time.

4 . The computer-implemented method of claim 2 ,

wherein the first point in time corresponds to a pre-contrast phase of the observation period prior to a contrast agent being introduced into the patient, and

wherein the second point in time corresponds to a post-injection phase of the observation period after the contrast agent is introduced into the patient.

5 . The computer-implemented method of claim 1 ,

wherein the time-related information comprises at least one further pre-training image at the one or more points of time during the observation period.

6 . The computer-implemented method of claim 1 , wherein the time-related information comprises statistical information for image pixel intensities across the observation period.

7 . The computer-implemented method of claim 1 ,

wherein the time-related information comprises a mask or a map for pixels of the at least one pre-training image.

8 . The computer-implemented method of claim 1 ,

wherein the pre-trained machine-learning model comprises at least one of an autoencoder neural network architecture or a u-net neural network architecture.

9 . The computer-implemented method of claim 1 ,

wherein said using of said at least part of the pre-trained machine-learning model comprises:

incorporating said at least part of the pre-trained machine-learning model into the further machine-learning model.

10 . The computer-implemented method of claim 9 , wherein said at least part of the pre-trained machine-learning model generates embedded features from the at least one inference image in the further machine-learning model, and wherein the semantic context information for the at least one inference image is determined based on the embedded features.

11 . The computer-implemented method of claim 1 ,

wherein said using of said at least part of the pre-trained machine-learning model comprises:

supervised training of said at least part of the pre-trained machine-learning model using further training images which are annotated with ground-truth semantic context information.

12 . The computer-implemented method of claim 1 ,

wherein the semantic context information comprises at least one of information about presence of a region of interest in the inference image or segmentation information related to the region of interest.

13 . A computer-implemented method for predicting semantic context information from an image acquired by a contrast-enhanced medical imaging system using a further machine-learning model built according to the computer-implemented method of claim 1 .

14 . A computing device comprising a processor and a memory, the memory comprising instructions executable by the processor, wherein when executing the instructions at the processor, the computing device is configured to perform the computer-implemented method of claim 1 .

15 . A medical imaging system comprising at least one computing device according to claim 14 .

16 . A non-transitory computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to carry out the computer-implemented method of claim 1 .

17 . The computer-implemented method of claim 2 ,

wherein said unsupervised pre-training of a machine-learning model for predicting time-related information comprises:

obtaining said at least one pre-training image of the patient acquired by the contrast-enhanced medical imaging system at the first point in time during the observation period;

applying said machine-learning model to the at least one pre-training image, wherein said time-related information is predicted for the second point in time during the observation period;

obtaining ground-truth information based on a further image acquired by the contrast-enhanced medical imaging system at the second point in time; and

training the machine-learning model based on comparing the ground-truth information and the time-related information predicted for the second point in time.

18 . The computer-implemented method of claim 17 ,

Wherein the first point in time corresponds to a pre-contrast phase of the observation period prior to a contrast agent being introduced into the patient, and

wherein the second point in time corresponds to a post-injection phase of the observation period after the contrast agent is introduced into the patient.

19 . The computer-implemented method of claim 2 ,

wherein the time-related information comprises at least one further pre-training image at the one or more points of time during the observation period.

20 . A computing device comprising:

a memory storing computer-executable instructions; and

at least one processor configured to execute the computer-executable instructions to cause the computing device to

generate a pre-trained machine-learning model by unsupervised pre-training of a machine-learning model for predicting time-related information from at least one pre-training image, the at least one pre-training image acquired by a contrast-enhanced medical imaging system using a contrast-enhanced measurement of a patient with multiple contrast agent distribution phases during an observation period, the time-related information being associated with one or more points of time during the observation period, and

build a further machine-learning model using at least part of the pre-trained machine-learning model, the further machine-learning model configured to predict semantic context information from at least one inference image acquired by the contrast-enhanced medical imaging system using the contrast-enhanced measurement or a further contrast-enhanced measurement.

21 . The computer-implemented method of claim 6 , wherein the statistical information includes at least one of a variance or a standard deviation of the image pixel intensities across the observation period.

22 . The computer-implemented method of claim 12 , wherein the region of interest is a diseased region.