Project Summary Asymptomatic pulmonary embolism (PE) are often incidentally discovered from contrast computed tomography (CT) scans that do not target PE. It has a mean prevalence of 2.6% among patients and associated with increased mortality rate and recurrence of PE. Currently non-contrast CT are not read by radiologists for PE, because the hyperintensity signal of thrombolysis on NCCT is weak. Hence, around 2.6% of the patients with NCCT can have asymptomatic PE but are not diagnosed at all, which is potentially a large population. We propose a deep learning-based automatic PE detection algorithm for single-energy NCCT to improve the cost-effectiveness to discover asymptomatic PE from NCCT. The algorithm will be used to identify patients with higher probability of PE and call for human reading or contrast CT scans. A major challenge is training data accumulation due to the relatively low prevalence of asymptomatic PE and hardness of reading NCCT. To overcome this challenge, we propose to utilize dual energy CT (DECT), which is becoming routinely used for PE diagnosis, to generate virtual non-contrast (VNC) images as training images. We propose to use deep learning algorithm for the VNC generation to fill the image quality gap between VNC images and real single-energy NCCT, which ensures that our PE detection algorithm trained on VNC images can be readily applied to real NCCT. The expected outcome of the project is (1) a deep learning algorithm to generate realistic VNC images from contrast DECT; (2) a deep learning algorithm to screen PE from NCCT with high sensitivity.