Recent Advances in Velocity Gradient-based Vascular Wall Shear Stress Measurement

Wall shear stress (WSS) drives vascular pathologies (e.g., atherosclerosis and aneurysm progression), necessitating precise in vivo quantification for clinical diagnostics. This review systematically evaluates current and emerging in vivo WSS measurement modalities, analyzing the mathematical principles and technical frameworks of vector Doppler imaging (VDI), ultrasound imaging velocimetry (UIV), speckle decorrelation (SDC), four-dimensional flow magnetic resonance imaging (4D flow MRI), and deep learning-enhanced 4D flow MRI. Each method is benchmarked based on spatial resolution, temporal resolution, and boundary-layer estimation accuracy. Ultrasound-based techniques offer superior temporal resolution but suffer from limited acoustic windows. Conversely, standard 4D flow MRI provides comprehensive volumetric data but suffers from underestimation errors near vessel walls due to partial volume effects. Integrating physics-informed neural networks into 4D flow MRI enhances near-wall spatial resolution. However, precise vascular wall boundary detection remains the primary universal bottleneck limiting clinical translation. In summary, this work establishes a benchmarking framework mapping clinical diagnostic needs to optimal imaging modalities. By highlighting the synergistic potential of combining fluid dynamics physics with deep learning, this review provides actionable optimization pathways to overcome current resolution limits, accelerating non-invasive WSS assessment tools in personalized cardiovascular medicine.