METHODS AND APPARATUS FOR DEEP LEARNING BASED IMAGE ATTENUATION CORRECTION

摘要

Systems and methods for reconstructing medical images are disclosed. Measurement data, such as magnetic resonance (MR) data and positron emission tomography (PET) data, is received from an image scanning system. Attenuation maps are generated based on the PET data and a determined background level of radiation of the image scanning system. The background level of radiation can be caused by the radioactive decay of crystal material of the image scanning system. MR images are reconstructed based on the MR data. Further, a neural network, such as a deep learning neural network, is trained with the attenuation maps and the reconstructed MR images to determine attenuation map based on a reconstructed MR image. The trained neural network can be applied to MR data received for a patient to determine a corresponding attenuation map. A final image is generated based on PET data received for the patient and the determined attenuation map.