Ultrasound Microvessel Imaging of the Human Placenta Demonstrates Altered Vessel Densities in Fetal Growth Restriction With Vascular and Immune Pathologies

Objectives Fetal growth restriction (FGR) is commonly associated with placental dysfunction, increasing perinatal morbidity and mortality. Visualizing placental vessels in utero would be advantageous for identifying functional FGR cause and determining proper management strategies. We aimed to utilize high‐sensitivity ultrasound microvessel imaging (HUMI) for quantifying placental vessel density (VD) in pregnancies diagnosed with FGR. Methods This pilot case–control study enrolled subjects in the third trimester with a diagnosis of FGR (n = 40) and gestational age‐matched controls with normal fetal growth (n = 20) at a 2:1 ratio, respectively. The Verasonics Vantage ultrasound system was used to perform HUMI on each participant at one timepoint. Scanning involved randomized singular value decomposition‐based clutter filtering to identify the villous tree, followed by step‐by‐step scanning to acquire 3‐dimensional‐like data. Mean VD was calculated from three ultrasound measurements per subject. Additional clinical and pathology data were also collected and compared. Results Sixteen participants were utilized to establish the scanning protocol and 2 met exclusion criteria at delivery. Thus, VD was successfully measured on 42 pregnancies scanned at 35 weeks 5 days on average. In FGR (n = 24), placental VD was significantly reduced compared to controls ( P < .01). VD measures were as good at predicting FGR as systolic/diastolic (S/D) ratios (area under the curve 0.86 versus 0.80). In a smaller cohort, VD in placentas with a diagnosis of inflammatory villitis (n = 10) by histology showed an increase in VD compared to those without inflammation ( P = .01). Low VD was correlated with increased S/D ratios ( P = .03). Conclusions HUMI is useful for identifying altered placental vascularization in utero for FGR. VD may be a valuable indicator for placental health and could lead to improved risk stratification methods considering underlying biology.