[Arterial-adaptive dilatation and Doppler velocimetry in normal fetuses with a single umbilical artery]

PURPOSE: In this study we examined the arterial-adaptive dilatation and Doppler velocimetry, especially RI values, in normal fetuses with a single umbilical artery (SUA). MATERIALS AND METHODS: We studied 195 fetuses from 18 to 39 weeks of gestational age with a prenatally identified SUA retrospectively. They were enrolled in this study if the following information applied: > 18 weeks of gestational age, no structural or chromosomal abnormalities, and histopathological confirmation of SUA. Sonographic examination included evaluation of the umbilical artery resistance and the cross-sectional area of the umbilical cord, and its vessels were measured in all cases. Small for gestational age (SGA) was diagnosed when the birth weight was below the 10th percentile for gestational age. Fetuses with intrauterine growth restriction were defined as those with biometric data below the 5th percentile. RESULTS: There were 119 cases of prenatally identified SUA which met the inclusion criteria. RI values were below the 10th percentile in 33/119 (27.33) and below the 50th percentile in 73/119 (61.33). RI values below the 10th percentile were significantly more likely to be in the normal collective than in the growth restricted collective [31/87 (35.63%) vs. 2/32 (6.25%); p = 0.001]. Even more significant differences became apparent when comparing the RI values below the 50th percentile of both groups. An umbilical artery diameter over the 90th percentile was found in 49 (41.9%) of cases and was significantly more likely to be present in normal growing fetuses than in the growth restricted group. CONCLUSION: Normal fetuses with SUA are at higher risk to be born as SGA. With our study results we can confirm the hypothesis that Doppler flow measurements and arterial diameter in SUA are different from those found in normal fetal umbilical arteries. RI values over the 50th percentile or a cross-sectional area of the artery below 95th percentile after 26th week of gestation significantly increases the risk of SGA.

Adaptive sensing for target tracking applications

In a statistical inference scenario, the estimation of target signal or its parameters is done by processing data from informative measurements. The estimation performance can be enhanced if we choose the measurements based on some criteria that help to direct our sensing resources such that the measurements are more informative about the parameter we intend to estimate. While taking multiple measurements, the measurements can be chosen online so that more information could be extracted from the data in each measurement process. This approach fits well in Bayesian inference model often used to produce successive posterior distributions of the associated parameter. We explore the sensor array processing scenario for adaptive sensing of a target parameter. The measurement choice is described by a measurement matrix that multiplies the data vector normally associated with the array signal processing. The adaptive sensing of both static and dynamic system models is done by the online selection of proper measurement matrix over time. For the dynamic system model, the target is assumed to move with some distribution and the prior distribution at each time step is changed. The information gained through adaptive sensing of the moving target is lost due to the relative shift of the target. The adaptive sensing paradigm has many similarities with compressive sensing. We have attempted to reconcile the two approaches by modifying the observation model of adaptive sensing to match the compressive sensing model for the estimation of a sparse vector.