Methods and apparatus for pet image reconstruction using multi-view histo-images of attenuation correction factors

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 image measurement data;
• generating a first histo-image based on the image measurement data;
• receiving attenuation data associated with the image measurement data;
• generating an attenuation histo-image based on the attenuation data;
• applying a machine learning process to the first histo-image and the attenuation histo-image and, based on the application of the machine learning process to the first histo-image and the attenuation histo-image, generating output image data characterizing an image volume;
• obtaining training data comprising training histo-images and corresponding training attenuation histo-images;
• generating training features based on the training histo-images and the training attenuation histo-images;
• inputting the training features into an untrained machine learning model;
• receiving training output data from the untrained machine learning model;
• and
• determining the untrained machine learning model is trained based on the training output data, wherein applying the machine learning process to the first histo-image and the attenuation histo-image comprises inputting the first histo-image and the attenuation histo-image to the trained machine learning model.

A system comprising:

• a database;
• and
• at least one processor communicatively coupled to the database and configured to:
• receive image measurement data;
• generate a first histo-image based on the image measurement data;
• receive attenuation data associated with the image measurement data;
• generate an attenuation histo-image based on the attenuation data;
• apply a machine learning process to the first histo-image and the attenuation histo-image and, based on the application of the machine learning process to the first histo-image and the attenuation histo-image, generating output image data characterizing an image volume;
• store the output image data in the database;
• obtain training data comprising training histo-images and corresponding training attenuation histo-images;
• generating training features based on the training histo-images and the training attenuation histo-images;
• input the training features into an untrained machine learning model;
• receive training output data from the untrained machine learning model;
• and
• determine the untrained machine learning model is trained based on the training output data, wherein applying the machine learning process to the first histo-image and the attenuation histo-image comprises inputting the first histo-image and the attenuation histo-image to the trained machine learning model.