Factors affecting performance of fetal blood T2 measurements for noninvasive estimation of oxygen saturation.

Abstract

To ultimately make accurate and precise fetal noninvasive oxygen saturation (sO2 ) measurements by T2 -prepared bSSFP more widely available by systematically assessing error sources in order to potentially reduce perinatal mortality in cardiovascular malformations and fetal growth restriction. T2 -prepared bSSFP data were acquired in phantoms; in flowing blood in adults in the superior sagittal sinus, ascending and descending aorta, and main pulmonary artery; and in the fetal descending aorta and umbilical vein. T2 was assessed in relation to T2 two- or three-parameter curve-fitting techniques, SSFP readout, refocusing time delay (τ), constant and pulsatile blood flow, and impact of T1 recovery. Further, fetal T2 and sO2 variability were quantified in the descending aorta and umbilical vein in healthy fetuses and fetuses with cardiovascular malformation (gestational weeks 32-38). In phantoms, three-parameter fitting was accurate irrespective of phase FOV (<4 ms; i.e., <2%), and T2 was overestimated (up to 23 ms/10%; p = 0.001) beyond ±30 Hz off-resonance. In the adult aorta, T2 was underestimated during higher blood flow velocities and pulsatility for τ = 16 ms (-41 ms/-17%; p = 0.008). In fetuses, two-parameter fitting overestimated T2 compared with three-parameter fitting (+33 ms/+18%; p = 0.03). T2 variability was 18 ms/15% in the fetal descending aorta and 28 ms/14% in the umbilical vein. The resulting estimated sO2 variability was ∼10% (15% of sO2 value) in the fetal descending aorta. Errors due to T2 -fitting techniques, off-resonance, flow velocity, and insufficient T1 recovery between image acquisitions could be mitigated by using three-parameter fitting with included saturation-prepared images approximating infinite T2 -preparation time, adequate shimming covering the fetus and placenta, and by modifying acquisition parameters. Variability in fetal blood T2 and sO2 , however, indicate that it is currently not feasible to use these methods for prediction of disease.