Background: smoking is the strongest risk factor of Aortic Abdominal Aneurysms (AAAs), an abnormal dilation of the aorta with subsequent weakening of the vessel wall; if ruptured, AAAs are life threatening. Atherosclerosis, an inflammatory vascular disease, is a known precursor of AAAs and is also associated with smoking. Measuring smoking induced inflammation in the aortic wall may play an important role in determining AAA development risk. Recent studies suggest that the use of Electronic Nicotine Delivery Systems (ENDS, meaning vaping) may generate similar inflammatory effects as combustible cigarettes. Because the popularity of these devices is a relatively new behavior, long term effects remain uncertain.  Therefore, quantifying the effects of ENDS could improve our understanding of its physiologic effects and provide clues to patient outcomes. Inducible Nitrogen Oxide Synthase (iNOS) is commonly expressed in epithelial tissue. iNOS plays a key role in mediating the body’s inflammatory response and increased iNOS expression is associated with inflammatory diseases. Positron Emission Tomography (PET) imaging using the [18F]NOS radiotracer, an iNOS analog, may be well suited for imaging vascular inflammation. Methods: the original cohort was composed of 23 patients: 12 healthy controls (HC) and 11 nicotine vapers (V).  3 HCs and 2 Vs were excluded from the kinetic modeling due to irregularities in the blood corrections. MIM was used for the segmentation of the VOIs. Segmentation of the ascending aorta (Asc. Aorta) and descending aorta (Desc. Aorta) was done at the level of the carina; the abdominal aorta (AA) was segmented between L1 and L3. The vessel walls were segmented in the same slices as the corresponding whole blood VOIs. Two different methods were used for the segmentation of the wall: the first one resulted in a width of 2.0 mm; the second one in a width of 3.0 mm. For reference, the normal width of the aortic wall varies between 1.0 mm and 3.0 mm.  Image Derived Input Functions (IDIFs) from the corresponding vessel walls were used for kinetic modeling of the tissue curves. The VOIs were transferred to PMOD for kinetic analysis; modeling was done using 1TC and 2TC models as well as a Logan Plot. Displacement Volume (VT), Non-Displaceable Binding Potential (BPnd) and Area Under the Curve (AUC) were extracted from the models, and p-values were computed using a Mann-Whitney U Test (α = 0.05). Akaike Information Criterion (AIC) values were used for model selection. We hypothesized that a difference in the PET signal, as quantified by VT, BPnd, and AUC, would be observed across the control and experimental groups. Results: Significant differences in VT were found across regions of interest; generally speaking, VT,AA > VT,Asc. Aorta > VT, Desc. Aorta. Additionally, BPnd values increased the further the VOI was from the heart. In view of this, we will repeat the kinetic modelling using an IDIF from the left ventricle (LV) or eroding the edges of the corresponding IDIFs to account for spillover from surrounding tissue.