Systems and methods for image correction in positron emission tomography

A system for generating an attenuation correction model, comprising:

• at least one storage device including a set of instructions;
• and
• at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to direct the system to perform operations including:
• acquiring a plurality of training samples, each of the plurality of training samples including a sample positron-emission tomography (PET) image of a sample subject, a sample computed tomography (CT) image of the sample subject, and a sample attenuation-corrected PET image corresponding to the sample PET image;
• and
• for each of the plurality of training samples, generating a sample concatenated image based on the sample PET image and the sample CT image of the training sample;
• generating the attenuation correction model by training a cascaded neural network using the sample concatenated image and the sample attenuation-corrected PET image of each of the plurality of training samples.

A method for image correction in positron emission tomography (PET) implemented on a computing device having at least one processor and at least one storage device, the method comprising:

• acquiring a PET image and a computed tomography (CT) image of a subject;
• generating a concatenated image based on the PET image and the CT image;
• and
• generating, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model, wherein the attenuation correction model is a trained machine learning model.