Methods and apparatus for deep learning based image attenuation correction

A non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• receiving first positron emission tomography (PET) measurement data from an image scanning system;
• determining a reference level of radiation of the image scanning system based on the first PET measurement data;
• receiving magnetic resonance (MR) measurement data and second PET measurement data from the image scanning system;
• generating a first attenuation map based on the first PET measurement data and the second PET measurement data;
• training a neural network with the first attenuation map and the MR measurement data;
• and
• storing the trained neural network in a memory device.

A system comprising:

• a database;
• and
• at least one processor communicatively coupled to the database and configured to:
• receive first positron emission tomography (PET) measurement data from an image scanning system;
• determine a reference level of radiation of the image scanning system based on the first PET measurement data;
• receive magnetic resonance (MR) measurement data and second PET measurement data from the image scanning system;
• generate a first attenuation map based on the first PET measurement data and the second PET measurement data;
• train a neural network with the first attenuation map and the MR measurement data;
• and
• store the trained neural network in a memory device.