Low dose PET imaging with CT-aided cycle-consistent adversarial networks

Decreasing administered activity directly reduces radiation exposure to patients and medical staff, but meanwhile has adverse impacts on image quality and PET quantification accuracy. In this work, we propose to integrate multi-modality images and self-attention strategy into a cycle-consistent adversarial network (CycleGAN) framework to generate the full count PET image from low count PET and CT images. During the training stage, deep features are extracted by 3D patch fashion from low count PET and CT images, and are automatically highlighted with the most informative features by self-attention strategy. Then, the deep features are mapped to the full count PET image by using 3D CycleGAN. During the testing stage, the paired patches are extracted from a new arrival patient's low count PET and CT images, and are fed into the trained networks to obtain the synthetic full count PET image. This proposed algorithm was evaluated using 16 patients' data. Four-fold cross-validation was used to test the performance of the proposed method. The proposed method suppressed image noise significantly, and obtained images close to the diagnostic PET images. The organ boundaries can be better visualized on the PET images generated with the proposed method. We have investigated a method to estimate diagnostic PET image from low dose data. Experimental validation has been performed to demonstrate its clinical feasibility and accuracy. This technique could be a useful tool for low dose PET imaging.