This paper addresses the need for improved CT-guidance during needlebased liver procedures (i.e., tumor ablation), while reduces the need for contrast agent injection during such interventions. To achieve this objective, we augment the intraoperative CT with the preoperative vascular network deformed to match the current acquisition. First, a neural network learns local image features in a non-contrasted CT image by leveraging the known preoperative vessel tree geometry and topology extracted from a matching contrasted CT image. Then, the augmented CT is generated by fusing the labeled vascular tree and the non-contrasted intraoperative CT. Our method is trained and validated on porcine data, achieving an average dice score of 0.81 on the predicted vessel tree instead of 0.51 when a medical expert segments the non-contrasted CT. In addition, vascular labels can also be transferred to provide additional information. Source code of this work is publicly available at https://github.com/Sidaty1/Intraoperative_CT_augmentation.