Key result
In silico simulations show standing wave width and PWV reliably detect focal aortic soft inclusions.
In silico fluid-structure interaction simulations demonstrate that focal changes in aortic wall stiffness can be distinguished using specific qualitative and quantitative markers of pulse wave propagation.
Examining the change in regional stiffness of the arterial wall may prove as a reliable method for detecting various cardiovascular diseases. As suggested by Moens–Korteweg relationship, the pulse wave velocity (PWV) along the arteries has been shown to correlate to the stiffness of the arterial wall; the higher the stiffness, the higher the PWV. The current primary clinical practice of obtaining an average PWV between remote sites such as femoral and carotid arteries is not as clinically effective, since various cardiovascular diseases are shown to be accompanied by focal changes in stiffness. Therefore, methods to examine the PWVs focally are warranted. Extending on the findings of previous studies, pulse wave propagations along aortas with wall focal softening were addressed in this study using two-way coupled fluid–structure interaction (FSI) simulations of arterial pulsatile motions. Spatio-temporal maps of the wall displacement were used to evaluate the regional pulse wave propagations and velocities. In particular, soft wall inclusions of different number, size, and modulus were examined. The findings showed that the qualitative markers on the pattern of the wave propagations such as the existence of forward, reflected, and standing waves, as well as the quantitative markers such as PWV, linear coefficient of the propagating waves, and the width of the standing waves, provide a reliable tool to distinguish between the natures of the wall focal softening. Future studies are needed to include physiologically-relevant wall inhomogeneity in order to further implicate on the clinical potentials of the inverse problem for noninvasive diagnosis.
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Inga et al. (2015) studied Aortic focal softening (simulated). Focal soft inclusions (varying number, size, and modulus) vs. Homogeneous aortic wall model was evaluated on Pulse wave propagation characteristics (standing wave width, PWV). In silico simulations demonstrated that the width of the standing wave and pulse wave velocity can serve as reliable quantitative markers to detect and characterize focal soft inclusions in the aortic wall.
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