The energy resolving capabilities of Photon Counting Detectors (PCD) in Computed Tomography (CT) facilitate energy-sensitive measurements. The provided image-information can be processed with Dual Energy and Multi Energy algorithms. A research PCD-CT firstly allows acquiring images with a close to clinical configuration of both the X-ray tube and the CT-detector. In this study, two algorithms (Material Decomposition and Virtual Non-Contrast-imaging (VNC)) are applied on a data set acquired from an anesthetized rabbit scanned using the PCD-CT system. Two contrast agents (CA) are applied: A gadolinium (Gd) based CA used to enhance contrasts for vascular imaging, and xenon (Xe) and air as a CA used to evaluate local ventilation of the animal's lung. Four different images are generated: a) A VNC image, suppressing any traces of the injected Gd imitating a native scan, b) a VNC image with a Gd-image as an overlay, where contrast enhancements in the vascular system are highlighted using colored labels, c) another VNC image with a Xe-image as an overlay, and d) a 3D rendered image of the animal's lung, filled with Xe, indicating local ventilation characteristics. All images are generated from two images based on energy bin information. It is shown that a modified version of a commercially available dual energy software framework is capable of providing images with diagnostic value obtained from the research PCD-CT system.