Pelvic venous insufficiency is triggered by aberrant venous flow that can result in pain, thrombosis, and embolism. While these pathologies are well recognized, current routine non-invasive diagnostic techniques (static CT & MR venogram, conventional venogram) are unreliable, and depend on static anatomic features rather than on the dynamic flow underlying the disease state. Computational Fluid Dynamics (CFD) informed by 4D Flow MRI has the potential to non-invasively evaluate new diagnostic parameters and better plan intervention approaches for patients with venous disease. CFD has the potential to address the issues of limited temporal and spatial resolution in 4D flow MRI. However, flow in the venous system is complex, with velocity profiles drastically different from parabolic shapes. In this study, we developed a method to integrate arbitrarily complex flow profiles collected from dual-VENC 4D flow MRI imaging [1] of the pelvic venous system into CFD models. In the setting of left iliac vein compression, we integrate flow profiles from 4D flow MRI into CRIMSON, an open-source software for cardiovascular simulation [2]. While B-spline mapping of flow profiles from 2D phase contrast MRI has previously been described, the integration of 4D flow data allows for retrospective sampling creating increased flexibility and accuracy in informing CFD model creation [3]. Furthermore, in patients with asymptomatic left iliac vein compression (also known as May-Thurner), we investigated the impact of hydration status on hemodynamics before and after drinking 0.5 L of water. We compared metrics relevant to the stenosis such as wall shear stress and pressure gradients, as well as metrics relevant to thrombosis such as relative residence time. Lastly, flow patterns from the CFD model are qualitatively and quantitatively compared against flow patterns and measurements in 4D flow MRI. REFERENCES [1] S. Schnell et al., “Accelerated dual-venc 4D flow MRI for neurovascular applications,” J Magn Reson Imaging, vol. 46, no. 1, pp. 102–114, July 2017, doi: 10.1002/jmri.25595. [2] C. J. Arthurs et al., “CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation,” PLoS Comput Biol, vol. 17, no. 5, p. e1008881, May 2021, doi: 10.1371/journal.pcbi.1008881. [3] A. Gomez et al., “Optimal B-Spline Mapping of Flow Imaging Data for Imposing Patient-Specific Velocity Profiles in Computational Hemodynamics,” IEEE Trans. Biomed. Eng., vol. 66,