Abstract Body: Background: Coronary artery disease develops preferentially at discrete arterial segments, implicating site-specific mechanical forces that conventional lipid-centric models do not fully explain. We hypothesize that endogenous hemodynamic pressure waves generated at the diastole-to-systole transition via a water hammer-like mechanism produce quasi-standing wave interference patterns capable of transiently retaining low-density lipoprotein particles at focal coronary sites through acoustic radiation force and secondary wave-mediated interactions.
Methods: A physics-based framework grounded in fluid mechanics and acoustic wave dynamics was developed and applied to coronary angiographic observations. Iodinated contrast dynamics, including delayed washout, oscillatory redistribution, and phase-locked accumulation, served as indirect surrogates for acoustic nodal zone identification. A machine learning protocol incorporating physics-informed feature engineering and a bidirectional long short-term memory neural network was designed for automated acoustic zone detection from dynamic coronary angiography. A prospective validation study enrolling patients with unstable angina and mild-to-moderate right coronary artery stenosis is planned.
Results: Angiographic observations identified focal regions of contrast persistence at diastole-to-systole transition sites, spatially corresponding to predicted compression antinodes on a coronary acoustic map. These sites coincide with established anatomical predilection sites for coronary plaque formation. Serial angiographic frames demonstrated reproducible contrast accumulation patterns consistent with wave interference zones, distinct from collision-based mechanisms observed in peripheral arteries.
Conclusions: This framework provides a mechanistic basis for identifying coronary sites at biomechanical risk before angiographically apparent disease develops. Artificial intelligence-guided acoustic zone mapping may complement hemodynamic risk stratification and enable earlier identification of biomechanically vulnerable coronary segments. Prospective validation will determine whether acoustic zone mapping predicts focal plaque progression and adverse coronary events.