Generation of virtual non-contrast (VNC) image from dual energy CT scans using deep learning

Dual energy CT acquisitions can produce two material basis images with high accuracy. From dual energy CT scans with contrast enhancement, one often attempts to generate the corresponding non-enhanced CT images for calcium scoring or for renal stone detection. However, to accurately generate the virtual non-contrast (VNC) images, three-material decomposition is needed. With dual energy CT images, one needs to introduce additional constraints such as the mass or volume conservation condition to approximately perform three-material decomposition. In this work, we present a deep learning strategy to generate VNC image from DECT material basis images. In this strategy, the needed constraint for three-material decomposition is accomplished by learning from the large amount of available training data. In practice, the supervised learning strategy requires matched training data, but this requirement is hard to satisfy in practice since the DECT and non-contrast CT images are acquired in two different CT scans and thus mis-registration often plagues the ordinary supervised learning strategies. In this paper, a new strategy was developed to enable the training of the proposed VNC-Net without using matched training data.