Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels.

Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life-threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient-specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in-house computational platform, InVascular, for image-based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with graphic processing unit parallel computing technology. The TPI-S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S = 0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in vivo measured ones and the systolic pressure is statistical equivalence to the in-vivo measurements with p < .001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical

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The use of left ventricular assist device (LVAD) is a proven therapy for end-stage heart failure either as bridge-to-transplant or destination therapy. The improvement of LVADs with greater reliability, durability, and miniaturization require corresponding innovations in surgical techniques to fully realize the better clinical outcomes, shorter length of stay, and lower cost to the health care system. One key direction in surgical advancement is the minimally invasive implantation techniques. Traditional LVAD implantation is most often performed with full sternotomy and anastomosis of the outflow graft (OG) to ascending aorta. Minimally invasive techniques will require the option of potential OG anastomosis to other arterial sites such as axillary artery or descending aorta. The consideration of a patient’s initial native cardiac function and subsequent evolution on LVAD support becomes more critical. This research objective of this work is to fill in a GAP between a critical need for clinical guidelines of optimal LVAD implant and a reliable pre-surgical assessment that is patient-specific and noninvasive. We present a parametric study using image-based computational modeling and analysis, Fig 1 (a), to non-invasively assess the blood supply to upper/low body through 15 combinations between LVAD pumping and native LV ejection (b). The flow and kinetic energy distribution from ascending aorta to upper/low body are shown in (c) and (d) respectively. It is found that more LV ejection results in more flow and kinetic energy going to the upper body. The variability of LVAD flow and delivery of kinetic energy has impact on end-organ perfusion and should be addressed in future optimization of LVAD implantation.