Implicit registration for improving synthesized full-contrast image prediction tool

A computer system for training a prediction tool to generate at least one synthetic full-contrast image from zero-contrast and low-contrast images of a subject, the computer system comprising:

• a receiving unit,
• a processing unit, and
• an output unit;
• wherein the processing unit is configured to:
• receive, via the receiving unit, a training set, the training set comprising a set of images of a set of subjects, the images of each subject of the set of subjects comprising:
• a full-contrast image acquired with a full-contrast agent dose administered to the subject,
• a low-contrast image acquired with a low-contrast agent dose administered to the subject, where the low-contrast agent dose is less than the full-contrast agent dose,
• a first zero-contrast image acquired with no contrast agent dose administered to the subject, wherein the first zero-contrast image is acquired prior to the acquisition of the full-contrast image, and
• a second zero-contrast image acquired with no contrast agent dose administered to the subject, wherein the second zero-contrast image is acquired prior to the acquisition of the low-contrast image;
• and
• train an artificial neural network with the received training set to generate at least one synthetic full-contrast image from zero-contrast and low-contrast images of a subject,
• wherein the training comprises:
• applying the first and second zero-contrast images from the set of images and the low-contrast images from the set of images as input to the artificial neural network, and
• using a cost function to compare the output of the artificial neural network with the full-contrast images from the set of images and training of parameters of the artificial neural network using backpropagation.

A non-transitory computer-readable storage medium comprising processor-executable instructions with which to perform an operation for training a prediction tool to generate at least one synthetic full-contrast image from zero-contrast and low-contrast images of a subject, the operation comprising:

• receiving a training set, the training set comprising a set of images of a set of subjects, the images of each subject of the set of subjects comprising:
• a full-contrast image acquired with a full-contrast agent dose administered to the subject,
• a low-contrast image acquired with a low-contrast agent dose administered to the subject, where the low-contrast agent dose is less than the full-contrast agent dose,
• a first zero-contrast image acquired with no contrast agent dose administered to the subject, wherein the first zero-contrast image is acquired prior to the acquisition of the full-contrast image, and
• a second zero-contrast image acquired with no contrast agent dose administered to the subject, wherein the second zero-contrast image is acquired prior to the acquisition of the low-contrast image;
• and
• training an artificial neural network with the received training set, wherein training comprises:
• applying the first and second zero-contrast images from the set of images and the low-contrast images from the set of images as input to the artificial neural network, and
• using a cost function to compare the output of the artificial neural network with the full-contrast images from the set of images to train parameters of the artificial neural network using backpropagation.