Functional imaging features from computed tomography images
AssigneeOxford University Innovation Limited
InventorsRegent Lee, Anirudh Chandrashekar, Vicente Grau
Life of this patent
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Abstract
Methods, apparatus and computer readable media are provided for identifying functional features from a computed tomography (CT) image. The CT image may be a contrast-enhanced CT image or a non-contrast CT image. According to some examples, methods, apparatus and computer readable media are also provided for using machine learning to identify functional features from CT images. According to some examples, simulated functional image datasets such as simulated PET images or simulated SUV images are generated from a received CT image.
Claims19
A method for identifying one or more functional features in a computed tomography (CT) image, the method comprising:
- providing the CT image to a trained generator model, the generator model trained to translate an input CT image showing a target region of a subject to a positron emission tomography (PET) scan image indicating one or more functional features in the target region represented in the input CT image, wherein the generator model has been trained on a plurality of CT images and feedback from a discriminator;
- outputting, from the trained generator model, a PET scan image corresponding to the provided CT image, the PET scan image indicating one or more functional features in a target region represented in the provided CT image.
A method for training a generative adversarial network (GAN) to generate a simulated functional image dataset from a computed tomography (CT) image, the GAN comprising a generator network and a discriminator network, the method comprising:
- receiving a training set comprising:
- a plurality of CT images, each CT image showing a target region of a subject;
- and
- a plurality of functional image datasets, each functional image dataset indicating functional features in a target region of a subject;
- training the GAN, wherein training the GAN comprises:
- training the generator network, using the plurality of CT images and feedback from the discriminator network, to generate simulated functional image datasets;
- training the discriminator network, using the generated simulated functional image datasets and the plurality of functional image datasets, to classify received image datasets as simulated functional image datasets or genuine functional image datasets, and to provide feedback to the generator network;
- and
- outputting a trained generator model to translate an input CT image to a simulated functional image dataset indicating one or more functional features in the target region shown in the input CT image.
Dependencies are inferred from the claim text.