Method and apparatus for image attenuation correction based on deep learning

Systems and methods for reconstructing medical images are disclosed. Measurement data, such as magnetic resonance (MR) data and positron emission tomography (PET) data, are received from an image scanning system. An attenuation map is generated based on the PET data and the determined radiation background level of the image scanning system. The background level of radiation may be caused by radioactive decay of the crystalline material of the image scanning system. An MR image is reconstructed based on the MR data. Further, a neural network, such as a deep learning neural network, is trained with the attenuation map and the reconstructed MR image to determine the attenuation map based on the reconstructed MR image. A trained neural network may be applied to MR data received for a patient to determine a corresponding attenuation map. A final image is generated based on the PET data received for the patient and the determined attenuation map. 公开了用于重建医学图像的系统和方法。从图像扫描系统接收测量数据，例如磁共振(MR)数据和正电子发射断层摄影(PET)数据。基于PET数据和