Methods and apparatus for synthetic computed tomography image generation

Abstract

Systems and methods for generating attenuation maps for reconstructing medical images are disclosed. In some examples, measurement data, such as positron emission tomography (PET) data or single-photon emission computed tomography (SPECT) data, is received for a subject. A machine learning process is applied to the measurement data to generate initial synthetic images for multiple values of an imaging parameter. Further, patient data is received and classified to determine an object imaged with the subject. A second medical image is selected that includes the object, and a region-of-interest (ROI) of the initial synthetic images is determined. Further, based on the ROI an anatomical mask is generated for each initial synthetic image. A second image-to-image network process is applied to the patient data, the second medical image, a portion of each initial synthetic image that includes the ROI, and the corresponding anatomical mask to generate a final synthetic image.