Non-invasive Coronary Microvascular Flow Velocity Measurement via Multi-cycle Phase Clustering Color Doppler Flow
Objective:
Coronary microvascular dysfunction is an underlying mechanism of ischemia with no obstructive coronary arteries, yet it remains severely under-diagnosed. Accurate assessment of coronary microvascular flow velocity is crucial for early intervention, but clinical practice currently lacks an effective, non-invasive imaging technique. To address this issue, this study proposes a novel ultrafast ultrasound-based clustering color Doppler flow imaging (cCDFI) method designed to measure in vivo coronary microvascular flow velocity throughout the full cardiac cycle amid vigorous cardiac motion.
Methods:
cCDFI utilizes a multi-cycle phase clustering strategy via the motion information inherent in B-mode, and performs clutter filtering and auto-correlation on frames of the same phase across multiple cycles. Subsequently, by employing an effective mask, cCDFI achieves high blood flow sensitivity in coronary microvascular flow velocity measurements.
Results:
In vivo experiments demonstrated that cCDFI velocity estimations were highly consistent with tracking-based velocity measurement benchmarks, with the velocity fluctuations that align with phasic coronary hemodynamic characteristics successfully captured. Additionally, this study confirms the capability of contrast-free cCDFI imaging throughout the full cardiac cycle.
Conclusion:
The proposed cCDFI method provides a high-precision digital quantification tool for in vivo coronary microvascular flow velocity research, demonstrating significant potential for the early diagnosis and therapeutic intervention of coronary microvascular dysfunction due to its non-invasive, safe and accessible nature.
