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Conventional color flow processing is associated with a high degree of operator dependence, often requiring the careful tuning of clutter filters and priority encoding to optimize the display and accuracy of color flow images. In a companion paper, we introduced a novel framework to adapt color flow processing based on local measurements of backscatter spatial coherence. Through simulation studies, the adaptive selection of clutter filters using coherence image quality characterization was demonstrated as a means to dynamically suppress weakly-coherent clutter while preserving coherent flow signal in order to reduce velocity estimation bias. In this study, we extend previous work to evaluate the application of coherence-adaptive clutter filtering (CACF) on experimental data acquired from both phantom and <italic>in vivo</italic> liver and fetal vessels. In phantom experiments with clutter-generating tissue, CACF was shown to increase the dynamic range of velocity estimates and decrease bias and artifact from flash and thermal noise relative to conventional color flow processing. Under <italic>in vivo</italic> conditions, such properties allowed for the direct visualization of vessels that would have otherwise required fine-tuning of filter cutoff and priority thresholds with conventional processing. These advantages are presented alongside various failure modes identified in CACF as well as discussions of solutions to mitigate such limitations.

Spatial Coherence Adaptive Clutter Filtering in Color Flow Imaging—Part II: Phantom and In Vivo Experiments