Placental Vascular Function Across Imaging Scales: Comparative Insights from Contemporaneous MRI and Doppler Ultrasound

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Patel, Jon Detterich, John Wood, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8334089/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background : The placenta plays a critical role in fetal development. This study investigates correlations between contemporaneous Doppler ultrasound (US) and magnetic resonance imaging (MRI) measures of placental and fetal vascular function in pregnancies with normally developing fetuses. Method : In this prospective study, 56 pregnant women at 20–39 weeks' gestation underwent Doppler US (uterine artery, umbilical artery and vein, middle cerebral artery, ductus venosus) and placental MRI (spatial variance, temporal variance, vascular reactivity) within 30 minutes of each other. Pearson's correlation assessed associations between US and MRI measures, and regression models tested the impact of maternal risk factors on these correlations. Results : Umbilical vein (UV) velocity correlated positively with MRI spatial and temporal variance (r=0.45 and r=0.33, p<0.05), and umbilical artery pulsatility index (UA-PI) negatively correlated with spatial variance (r=-0.35, p<0.05). These correlations persisted in the presence of maternal risk factors except for maternal diabetes. Higher pregravid BMI was associated with increased UA-PI (p=0.039) and diabetes with lower UV velocity (p=0.036). Conclusion : Correlations between UA-PI, UV velocity, and MRI spatial and temporal variance suggest that these interrelated metrics capture complementary aspects of feto-placental micro- and macrovascular function and may improve assessment of placental health. Placental magnetic resonance imaging placental ultrasound placental vascula function Figures Figure 1 BACKGROUND The placenta is a dynamic organ that supports fetal growth through tightly regulated metabolic, endocrine, and oxygen transport functions at the maternal–fetal circulatory interface. Because placental dysfunction underlies many adverse pregnancy outcomes, improving noninvasive methods for assessing placental function in utero is a clinical priority. Yet much remains to be learned about the capabilities and limitations of different imaging techniques in this role. Ultrasound (US) is a safe, accessible modality routinely used in obstetric care. Maternal placental perfusion is reflected in the uterine artery (UtA), which progressively vasodilates through gestation. Disturbances in this process may manifest as elevated UtA pulsatility index (PI) suggestive of higher vascular resistance as is reported in preeclampsia or fetal growth restriction [ 1 – 3 ]. The fetoplacental circulation is evaluated by umbilical artery (UA) US measurements where abnormalities manifest as absent or reversed end diastolic flow indicating increased vascular resistance, as seen in placental insufficiency [ 4 , 5 ]. Despite its ubiquity, US is operator-dependent and image quality can be limited by factors such as fetal position, maternal body habitus, and gestational age. Furthermore, conventional Doppler US primarily reflects macrovascular blood flow in vessels supplying and draining the placenta but cannot directly assess placental vasculature. Notably, studies suggest that UA PI does not become abnormal until over 60% of the placental vascular bed is obliterated [ 6 ], underscoring the limited sensitivity of standard US to mild or early placental vascular dysfunction. Magnetic resonance imaging (MRI) is another safe imaging modality, though less routinely used, that offers unique insights into placental morphology, microstructure, and function. Imaging techniques like dynamic contrast-enhanced, arterial spin labelled, diffusion-weighted and blood oxygen level dependent (BOLD) MRIs have been utilized to quantify spatiotemporal patterns in placental oxygen delivery [ 7 , 8 ]. Newer methods have assessed specific functional properties such as placental vascular reactivity (PLVR) by measuring placental BOLD MRI responses to maternal carbon dioxide changes elicited via coached breathing [ 9 ]. Magnetic resonance imaging (MRI) is another safe imaging modality, though less routinely used, that offers unique insights into placental morphology, microstructure, and function. Imaging techniques like dynamic contrast-enhanced, arterial spin labelled, diffusion-weighted and blood oxygen level dependent (BOLD) MRIs have been utilized to quantify spatiotemporal patterns in placental oxygen delivery [ 7 , 8 ]. Newer methods have assessed specific functional properties such as placental vascular reactivity (PLVR) by measuring placental BOLD MRI responses to maternal carbon dioxide changes elicited via coached breathing [ 9 ]. Ultrasound primarily characterizes blood flow in vessels proximal (UtA) or distal (UA) to the placenta whereas MRI can interrogate the intervillous and microvascular environment within the placenta. Given these complementary strengths, it is important to clarify how findings from the two modalities relate to each other. Defining normal MRI-US relationships is a necessary foundation for interpreting MRI deviations in pathologic pregnancies. Prior studies have examined the correlation between 2D US measurements and placental MRI volume measurements [ 10 ] and have evaluated both MRI and US measures in conditions of placental dysfunction [ 11 ]. However, in these studies US and MRI measurements were not contemporaneous, making it difficult to draw conclusions when scans were performed weeks apart. This study prospectively examined the relationship between Doppler US indices and MRI-based measures of placental structure and function, obtained in close temporal proximity. We hypothesized that MRI-derived metrics would correlate with Doppler indices, reflecting shared dependence on placental vascular health. Secondarily, we explored how maternal conditions such as diabetes, hypertension, and obesity, known to influence placental vasculature (without necessarily resulting in abnormal Doppler US indices or fetal outcomes) might alter this relationship. By establishing normative MRI–US correlations and identifying how maternal risk factors modify them, this work provides a foundation for integrating MRI as a complementary tool for detecting subtle or early placental dysfunction beyond the reach of conventional US. METHODS Subject Demographics : Pregnant mothers residing in and around Los Angeles County were recruited from May 2021 to April 2024 through flyers, self-referrals, and community partner clinics at Children’s Hospital Los Angeles (CHLA). Inclusion criteria were normally developing singleton pregnancies without congenital or genetic anomalies in women aged 20–43 years, with gestational ages (GA) between 20 and 39 weeks. Exclusion criteria were fetal anatomic or genetic anomalies, fetal growth restriction, congenital infections, multiple pregnancies, and maternal MRI contraindications. Also excluded were pre-gestational insulin-dependent maternal diabetes and pre-gestational hypertension or preeclampsia. Informed consent was obtained under an Institutional Review Board (IRB) approved protocol at CHLA. Consented participants provided a self-report of prenatal health history and demographics including the following maternal risks: pregravid maternal BMI, presence of any form of diabetes mellitus during pregnancy, any form of hypertensive disorder during pregnancy, parity, gravidity, and maternal age on day of study visit. All fetuses were normally developing with no known anomalies; however, these were not uniformly “healthy” pregnancies due to the presence of minor maternal health risks. We intentionally included such pregnancies because while they are known to affect placental function their impact on fetal outcomes and US measures is mixed making them important confounders to represent in our cohort. Ultrasound Acquisition and Measurements : Within thirty minutes of the initiation or completion of the placental MRI, transabdominal US measures using 2D, color Doppler and pulsed wave Doppler were prospectively collected by a single operator with over 5 years of experience (SW). Either the Philips Epiq ultrasound machine with the curvilinear C5-1 or C9-2 transducers or the GE Voluson E10 ultrasound machine with the RM6C 4D transducer was used. Following guidelines on Doppler interrogation, measurements were acquired at an insonation angle of < = 20° for pulsed-wave Doppler per established practice guidelines [ 12 ]. Vessels interrogated included the umbilical artery (UA), umbilical vein (UV), middle cerebral artery (MCA), ductus venosus (DV), and maternal uterine artery (UtA). Doppler waveform patterns were noted, with an abnormal pattern defined as absence or reversal of end diastolic flow (AREDF) for the UA, AREDF or increased diastolic flow for the MCA, notching or pulsations for the UV, and absence or reversal of flow during atrial systole (A wave) for the DV. The following measurements were performed where possible: peak systolic velocity (PSV), end diastolic velocity (EDV), and time averaged maximal velocity (TAMAX) for the UA and UtA for those without AREDF; the UV mean velocity; the S wave velocity, A wave velocity, and TAMAX for the DV for those without absence or reversal of the A wave; and the PSV, EDV, and TAMAX for the MCA for those without AREDF. For the UA and MCA, the PI was calculated as (PSV − EDV)/ TAMAX. The cerebroplacental ratio (CPR) was calculated as MCA-PI/UA-PI. For quantitative assessment of the DV, the PI of veins (PIV) was calculated as PIV = (S wave velocity − early diastolic A wave velocity)/ TAMAX [ 13 – 17 ]. MRI Acquisition and Analysis : All participants underwent a placental MRI on a 3T Philips Ingenia scanner. Participants were placed feet-first and in supine position to avoid positional variability in blood flow due to gravity. A structural image was acquired for localization of placental anatomy. First, a BOLD MRI of the placenta was acquired for 7–8 minutes with the following parameters: relaxation time = 3000 ms, echo time = 45 ms; flip angle = 90°, resolution = 3.5 mm 3 , slices = 65–85, MB factor = 2, number of dynamics = 150. To measure PLVR, another BOLD MRI with the exact same parameters was obtained while the participant followed a coached breathing protocol involving a 15-second breath hold and three sighs per minute. Participant end-tidal carbon dioxide level (EtCO 2 ) was determined from expired CO 2 measured via a nasal cannula connected to the Respsense Capnogram (Nonin Medical Inc, Plymouth, MN, USA). Placental Spatiotemporal Variance: Placental spatial and temporal variance were computed using previously validated methods [ 18 ]. All raw data were manually reviewed for large motion artifacts and fetal-maternal motion correction was performed by an inter-volume non-rigid registration followed by non-linear registration. Temporal variance quantifies the natural physiological fluctuations in blood inflow and outflow from the intervillous space. For each placenta, we first manually defined a mask to isolate the placenta from surrounding tissues. Within this mask, we extracted the mean BOLD signal time series at each voxel. The temporal variance was then calculated as the standard deviation of this time series. This value was normalized by the overall mean BOLD signal to allow for comparison between subjects. In parallel, spatial variance quantifies the heterogeneity of blood flow and oxygenation across the placental anatomy. First, the BOLD signal for each voxel at each time point, within the manually delineated mask, was normalized by the mean signal of the entire placenta at each dynamic. Next, the standard deviation of these normalized values across all placental voxels at a single time point was calculated. The final spatial variance value for each subject was then determined by averaging this measure over the entire scan, providing a comprehensive view of the heterogeneity. Placental Vascular Reactivity (PLVR): To calculate PLVR, both voxel-wise BOLD signal within the placental mask and maternal EtCO 2 were converted to the frequency domain using fast-fourier transform. Next, PLVR was calculated as the sum of the ratio of the BOLD to EtCO2 signal magnitudes across all frequencies. This ratio was weighted by the spectral coherence between the two signals to reduce noise. A frequency-dependent scaling factor was also applied to account for the delay in the vascular response to CO2 changes. Finally, a global PLVR value was determined by averaging the voxel-wise PLVR results within the manually defined placental volume of interest. PLVR quantifies the placental response to transient changes in maternal CO 2 . This reflects the functional capacity of placental vasculature to match blood supply to fetal demand, which is known to be regulated by placental endothelial-derived mediators [ 19 , 20 ]. Statistical Methods : All statistical analyses were performed using Python 3.9. A p-value of less than 0.05 was considered statistically significant. Descriptive statistics for clinical and demographic characteristics of the study cohort are presented as mean ± standard deviation for continuous variables and as percentages for categorical variables. To consistently account for gestational age and fetal sex in all models, raw ultrasound values rather than published z-scores (which do not account for fetal sex) were used in this analysis. Spearman’s correlation, selected due to the non-parametric nature of the data and its robustness to outliers, was employed to assess associations between ultrasound and MRI measures. Statistical significance was determined using a two-tailed p-value. The associations between various maternal risk factors and US or MRI measures was examined using generalized linear model models (gamma regression with logit link function) to account for the likely skewed distribution of the US/MRI measures. For each combination of a US/MRI measure (dependent variable) and a maternal risk factor (independent variable), a separate model was fitted. All models included gestational age at MRI scan as a covariate. Robust standard errors (HC1) were utilized to address potential heteroscedasticity. Subsequently, maternal risk factors with a significant relationship to US or MRI placental measures were included in a partial correlation analysis that evaluated the impact of each maternal risk factor on the correlation between ultrasound and MRI placental measures. RESULTS Clinical and Demographic Characteristics of Study Populatio n: Clinical and demographic characteristics of participants included in the study are shown in Table 1. A total of 56 pregnant mothers were recruited for this study, with 55 mother-fetus dyads completing the MR imaging session. After imaging, all 56 subjects completed an ultrasound. As a result, 55 subjects had both analyzable MRI and US data and were used for further analysis. Figures showing distribution of z-scores for all US values, calculated from published norms [13-17], are included in the Supplementary Figure 1(a-d). Correlation between placental MRI and ultrasound measures: A heatmap generated to visualize statistically significant correlations between placental MRI and ultrasound measures is seen in Figure 1. Warm colors represent positive correlations, and cool colors represent negative correlations. Our findings showed significant positive correlations between UV mean velocity and both placental spatial variance (r=0.45) and placental temporal variance (r=0.33) on MRI suggesting that increased UV velocity and flow is associated with greater spatial and temporal variance on MRI. Additionally, UA-PI was negatively correlated with placental spatial variance (r=-0.35) indicating that higher umbilical artery resistance is associated with less spatial variance. Correlation analysis amongst the ultrasound measures revealed a negative correlation between UA-PI and UV mean velocity (r=-0.43) suggesting higher resistance in the umbilical artery is associated with lower umbilical vein flow. There was also an expected negative correlation between UA-PI and CPR (r=-0.38) since the latter factors in the UA-PI into its calculation. Ductus venosus measures and uterine PI showed no significant correlations with either other ultrasound measures or MRI measures. Correlation analysis amongst the MRI measures revealed strong positive correlations between all three measures, specifically placental spatial variance and temporal variance (r=0.59), placental spatial variance and PLVR (r=0.79), and placental temporal variance and PLVR (r=0.5). This demonstrates a consistent pattern across different aspects of placental MRI assessment. Maternal Risk Factors and placental ultrasound measures : Table 2 shows the regression coefficients and p-values for regression analysis of the relationship between maternal risk factors and placental ultrasound measures. Significant decreases in UV mean velocity were observed in the presence of maternal diabetes (β = -1.24, 95% CI: -2.49, -0.051, p = 0.05), higher parity (β = -0.5701, 95% CI: -1.045, -0.095, p = 0.025), and higher gravidity (β = -0.5661, 95% CI: -1.041, -0.091, p = 0.026). A significant positive association was found between pregravid BMI and UA-PI (β = 0.0056, 95% CI: 0.000, 0.011, p = 0.036), indicating increased vascular resistance in pregnancies with higher maternal BMI. The presence of maternal hypertension was associated with a reduced CPR (β = -0.426, 95% CI: -0.664, -0.189, p = 0.000), while both higher parity (β = 0.1020, 95% CI: 0.040, 0.163, p = 0.001) and higher gravidity (β = 0.1023, 95% CI: 0.041, 0.164, p = 0.001) were positively associated with CPR. Maternal Risk Factors and placental MRI measures : Table 3 shows the regression coefficients and p-values for regression analysis of the relationship between maternal risk factors and placental MRI measures. There was a significant negative association between maternal diabetes and placental spatial variance (β=−1.76, 95% CI: -2.304 to -1.226, p = 0.000), placental temporal variance (β=−1.518, 95% CI: -2.301 to -0.735, p = 0.00) as well as between maternal diabetes and placental vascular reactivity (β= -0.91, 95% CI: -1.52 to -0.303, p=0.003). There was also a negative association between maternal hypertension and placental spatial variance (β=−1.662, 95% CI: -2.074 to -1.25, p=0.000). Other maternal risk factors such as maternal BMI, maternal age, and maternal parity/gravidity did not show significant associations with MRI placental measures. Effect of maternal risk factors on US and MRI correlations: In the presence of maternal hypertension, higher maternal age, or higher BMI, the negative correlation between UA-PI and placental MRI spatial variance (r=-0.36) as well as positive correlation between UV mean velocity and spatial variance (r=0.39) persisted. However, when evaluating the effect of maternal diabetes, the only correlation that remained was the positive correlation between UV mean velocity and spatial variance (r=0.34) while UA-PI correlation was no longer significant. DISCUSSION Our findings show that intrinsic functional characteristics of the placenta measured using MRI are correlated to umbilical Doppler hemodynamics in normally developing fetuses. Higher UV velocity on Doppler US correlated with increased temporal and spatial variance on MRI. As the sole vessel through which all blood passes from the placenta to the fetus, the UV flow and velocity measures are largely determined by placental vascular characteristics [ 21 ]. Efficient placental microvascular function—manifested as high spatial (vascular integrity) and temporal (dynamic adaptation) variance—is physiologically expected to coincide with increased UV flow velocity. In contrast, impaired placental perfusion, as seen in intrauterine growth restriction or severe congenital heart disease, is associated with abnormal umbilical artery Doppler measures [ 22 , 23 ]. Elevated UA pulsatility index (PI) indicates increased vascular impedance due to reduced fetal inflow or impaired gas exchange [ 24 , 25 ]; its inverse relationship with placental temporal variance is corroborated by ex vivo experiments demonstrating increased fetoplacental vascular resistance in response to impaired uteroplacental circulation [ 26 ]. Additionally, UA-PI decreases with GA, whereas both placental spatial and temporal variances increase with GA further explaining their inverse correlation [ 27 ]. PLVR showed no correlations with US measures likely because it is a response elicited from a coached breathing stimulus, which was not performed during US acquisition. It is interesting to note that MRI measures of placental function did not correlate with ductus venosus or middle cerebral artery US measures. This result is not surprising as DV and MCA abnormalities are usually late findings and is likely explained by the complex interactions of the DV and MCA flow with factors other than just the placenta (ie, DV reflects cardiac preload and central venous pressure and MCA reflects cerebrovascular autoregulation). Since all placental MRI measures leveraged vascular and oxygenation properties of the placenta, there was a high correlation amongst them even in the presence of maternal risk factors. Our MRI–US correlations align with prior studies using arterial spin labeling (ASL), intravoxel incoherent motion (IVIM), T2/T2*, and 4D flow MRI [ 28 ]. Reduced perfusion on ASL and IVIM correlates with elevated UA-PI in small-for-gestational-age pregnancies [ 29 , 30 ], while lower placental T2 and T2* relaxation times—indicating decreased oxygenation—are inversely related to uterine artery PI. These studies support a consistent pattern: MRI-derived metrics probing various aspects of placental function (perfusion, oxygenation, or structure) correspond to Doppler indices of vascular resistance. Together, they reinforce MRI’s role as a complementary tool that provides microvascular insight beyond the macroscopic flow information from Doppler US. In our study, maternal diabetes was associated with reduced placental spatial and temporal variance, PLVR, and UV velocity, indicating vascular dysfunction. Prior animal and histopathologic studies show that diabetes disrupts vasculogenesis and increases villous immaturity [ 31 – 33 ]. Other Doppler work found no consistent UV differences between diabetic and non-diabetic pregnancies, suggesting fetal size rather than diabetic status drives UV variation [ 34 ]. Consistent with prior reports, diabetes did not significantly affect UA-PI in our cohort. When accounting for diabetes, correlations between MRI and UA-PI were no longer significant, likely reflecting heterogeneous placental and cord adaptations influenced by diabetes type, onset, and glycemic control [ 35 – 39 ]. Although UA-PI typically remains normal unless severe vasculopathy or growth restriction develops [ 40 , 41 ], diabetic placentas often exhibit microvascular remodeling and villous immaturity [ 42 , 43 ]. The loss of correlation between UA-PI and MRI measures underscores ultrasound’s limited sensitivity to early vascular changes that MRI may be able to detect—changes that may contribute to adverse outcomes even in otherwise normal-appearing pregnancies with normal US values [ 44 ]. While the effect of hypertension on placental and fetal circulation was in line with other studies, we caution against drawing conclusions from this study based on limited incidence of hypertension in our cohort. The positive association between high BMI and increased resistance in the UA is well-supported by other studies [ 45 , 46 ]. The lack of associations between maternal pregravid BMI and placental MRI measures could be due to the impact of maternal BMI on placental metabolic function through mechanisms such as hyperlipidemia [ 47 ]. Since MRI is not feasible for routine screening, defining its relationship to standard Doppler measures is clinically important. MRI directly quantifies placental vascular and oxygenation properties, allowing detection of subclinical or localized dysfunction that may precede Doppler changes. Combining MRI and US provides a more comprehensive assessment of placental health and could possibly identify with greater sensitivity when deviations become pathologic. Future longitudinal studies are needed to explore the relationship between MRI–US metrics with perinatal outcomes, in both physiologic and pathologic conditions. Larger studies are warranted to establish normative reference curves and define MRI’s sensitivity and sepcificity for detecting early placental dysfunction. Our study has several strengths, including contemporaneous acquisition of US and MRI within 30 minutes which minimizes the impact of confounding factors on these measurements, such as the mom’s physical state or changes inherent to different gestational ages. Secondly, by exclusively recruiting pregnant mothers with normally developing fetuses rather than including pathological conditions, the study is able to focus on intermodality correlations of typical placental function. Operator variability is significantly reduced as all US measurements were consistently performed prospectively by the same operator. Despite these strengths, our study has certain limitations. The reliance on a single operator for all US measurements, while reducing variability, may limit the generalizability of the findings to other settings where multiple operators are involved; however, US measures have been shown to be highly reproducible when acquired according to standard guidelines. Another limitation is that our study is cross-sectional in nature and cannot provide causal inference into the relationship between placental imaging measures and perinatal outcomes. Finally, our cohort had an uneven distribution in the prevalence of maternal risks, warranting future studies focusing on specific maternal conditions and long term postnatal follow up to truly define the impact of an abnormal maternal environment on fetal health. CONCLUSION This study is the first to report correlations between placental US and MRI measures obtained in close temporal proximity in a prospective cohort of pregnancies with normally developing fetuses. Our findings highlight that MRI-derived placental oxygenation characteristics correlate with umbilical artery and vein hemodynamics. Significant correlations between ultrasound umbilical artery PI and umbilical vein velocity, and MRI measures of spatial and temporal variance suggest that these metrics, measured on different scales, reflect micro- and macrovascular function in the feto-placental system. The presence of maternal diabetes modifies the associations between US and MRI measures, further highlighting its impact on placental vascular function. These results highlight the complementary roles of US and MRI in assessing placental function. Declarations Ethics approval and consent to participate: Study approval statement: This study protocol was reviewed and approved by the Children’s Hospital Los Angeles Institutional Review Board (IRB): approval number CHLA-17-00292. Date of approval 9/14/2017 (updated 6/9/2025). Consent to participate statement: Written consent was obtained from all participating pregnant women. Consent for publication: Not applicable Availability of data and materials statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests statement The authors have no conflicts of interest to declare. Funding Sources This study was not supported by any sponsor or funder. Author Contributions: VR, RP: patient recruitment and data acquisition, review and approval; JD, JW, JVS, BG: interpretation of data, review and approval. 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Placenta . 2020;91:52-58. doi:10.1016/j.placenta.2020.01.009 Leach L, Taylor A, Sciota F. Vascular dysfunction in the diabetic placenta: causes and consequences. J Anat . 2009;215(1):69-76. doi:10.1111/j.1469-7580.2009.01098.x Huynh J, Dawson D, Roberts D, Bentley-Lewis R. A systematic review of placental pathology in maternal diabetes mellitus. Placenta . 2015;36(2):101-114. doi:10.1016/j.placenta.2014.11.021 Carrasco-Wong I, Moller A, Giachini FR, et al. Placental structure in gestational diabetes mellitus. Biochim Biophys Acta Mol Basis Dis . 2020;1866(2):165535. doi:10.1016/j.bbadis.2019.165535 To WW, Mok CK. Fetal umbilical arterial and venous Doppler measurements in gestational diabetic and nondiabetic pregnancies near term. J Matern Fetal Neonatal Med . 2009;22(12):1176-1182. doi:10.3109/14767050903042546 Koskinen A, Lehtoranta L, Laiho A, Laine J, Kääpä P, Soukka H. Maternal diabetes induces changes in the umbilical cord gene expression. Placenta . 2015;36(7):767-774. doi:10.1016/j.placenta.2015.04.004 Tenaw Goshu B. Histopathologic Impacts of Diabetes Mellitus on Umbilical Cord During Pregnancy. Pediatric Health Med Ther . 2022;13:37-41. Published 2022 Feb 18. doi:10.2147/PHMT.S323812 Leach L. Placental vascular dysfunction in diabetic pregnancies: intimations of fetal cardiovascular disease?. Microcirculation . 2011;18(4):263-269. doi:10.1111/j.1549-8719.2011.00091.x Starikov R, Inman K, Chen K, et al. Comparison of placental findings in type 1 and type 2 diabetic pregnancies. Placenta . 2014;35(12):1001-1006. doi:10.1016/j.placenta.2014.10.008 Kapustin RV, Kopteyeva EV, Tral TG, Tolibova GK. Placental morphology in different types of diabetes mellitus. Journal of Obstetrics and Women’s Diseases . 2021;70(2):13-26. doi:10.17816/JOWD57149 Reece EA, Homko CJ, Wiznitzer A. Doppler velocimetry and the assessment of fetal well-being in normal and diabetic pregnancies. Ultrasound Obstet Gynecol . 1994;4(6):508-514. doi:10.1046/j.1469-0705.1994.04060508. Hong J, Crawford K, Cavanagh E, et al. The relationship between abnormal fetoplacental Dopplers, angiogenic markers of placental dysfunction and adverse perinatal outcomes in diabetic pregnancies with small fetuses - A prospective study. Placenta . 2025;160:51-59. doi:10.1016/j.placenta.2024.12.025 Aldahmash WM, Alwasel SH, Aljerian K. Gestational diabetes mellitus induces placental vasculopathies. Environ Sci Pollut Res Int . 2022;29(13):19860-19868. doi:10.1007/s11356-021-17267-y Huynh J, Dawson D, Roberts D, Bentley-Lewis R. A systematic review of placental pathology in maternal diabetes mellitus. Placenta . 2015;36(2):101-114. doi:10.1016/j.placenta.2014.11.021 Scholtens DM, Kuang A, Lowe LP, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): Maternal Glycemia and Childhood Glucose Metabolism. Diabetes Care . 2019;42(3):381-392. doi:10.2337/dc18-2021 Sarno L, Maruotti GM, Saccone G, Morlando M, Sirico A, Martinelli P. Maternal body mass index influences umbilical artery Doppler velocimetry in physiologic pregnancies. Prenat Diagn . 2015;35(2):125-128. doi:10.1002/pd.4499 Cody F, Mullers S, Flood K, et al. Correlation of maternal body mass index with umbilical artery Doppler in pregnancies complicated by fetal growth restriction and associated outcomes. Int J Gynaecol Obstet . 2021;154(2):352-357. doi:10.1002/ijgo.13586 Myatt L, Maloyan A. Obesity and Placental Function. Semin Reprod Med . 2016;34(1):42-49. doi:10.1055/s-0035-157002 Tables Table 1: Study participant demographic and clinical characteristics Characteristic N = 56 Maternal Age Mean (SD) 31.5 (5.1) Range 20-43 Gestational age at MRI Mean (SD) 30.6 (5.2) Range 20.1-39.4 Gravidity Mean (SD) 2.8 (1.7) Range 1-8 Parity Mean (SD) 1.8 (1.7) Range 0-7 Maternal Diabetes Yes 7/56 (12.5%) Pregravid Maternal BMI Mean (SD) 28 ± 7.1 Range 13.4-54.2 Maternal Hypertension Yes 2/56 (3.6%) Race and Ethnicity Hispanic 40/56 (71.4%) Non-Hispanic White 9/56 (16.1%) Non-Hispanic Asian 3/56 (5.4%) Non-Hispanic Black 1/56 (1.8%) Unknown 3/56 (5.4%) Fetal Sex Female 31/56 (55.4%) Male 25/56 (44.6%) Table 2: Associations between Doppler ultrasound measures of placental function and maternal risk factors. Regression coefficients, confidence intervals, and p-value of models are shown. Statistically significant relationships are denoted with an asterisk*. Maternal Risks Uterine PI UA PI UV Mean CPR β (95% CI) p β (95% CI) p β (95% CI) p β (95% CI) p Diabetes 0.0613 (-0.063, 0.185) 0.332 0.0978 (-0.047, 0.243) 0.186 -1.8479 (-3.598,-0.098) 0.039* 0.0135 (-0.399, 0.426) 0.949 Pregravid BMI 0.0082 (-0.003, 0.020) 0.155 0.0056 (0.000, 0.011) 0.036* 0.0228 (-0.079, 0.124) 0.660 -0.0095 (-0.030, 0.011) 0.375 Hypertension 0.8399 (-0.277, 1.957) 0.141 0.3575 (-0.044, 0.758) 0.081 -1.8095 (-4.250, 0.631) 0.146 -0.6369 (-0.943, -0.331) 0.000* Age -0.0107 (-0.022, 0.001) 0.072 0.0001 (-0.008, 0.008) 0.978 0.0202 (-0.172, 0.212) 0.837 0.0147 (-0.005, 0.034) 0.136 Parity -0.0019 (-0.055, 0.052) 0.944 -0.0092 (-0.040, 0.022) 0.560 -0.5701 (-1.045, -0.095) 0.019* 0.1020 (0.040, 0.163) 0.001* Gravidity -0.0021 (-0.055, 0.051) 0.939 -0.0089 (-0.040, 0.022) 0.572 0.5661 (-1.041, -0.091) 0.020* 0.1023 (0.041, 0.164) 0.001* Table 3: Associations between magnetic resonance imaging measures of placental function and maternal risk factors. Regression coefficients, confidence intervals, and p-value of models are shown. Statistically significant relationships are denoted with an asterisk*. Maternal Risks Placenta Spatial Variance Placenta Temporal Variance Placental Vascular Reactivity β (95% CI) p β (95% CI) p β (95% CI) p Diabetes -1.2186 (-1.965, -0.472) 0.001* -143.3930 (-332.763, 45.977) 0.138 -0.0218 (-0.038, -0.006) 0.007* Pregravid BMI 0.0112 (-0.069, 0.091) 0.784 1.3524 (-7.195, 9.900) 0.756 0.0003 (-0.002, 0.002) 0.766 Hypertension -1.1654 (-2.329, -0.002) 0.050* -79.5066 (-294.982, 135.968) 0.470 -0.0229 (-0.053, 0.007) 0.137 Age 0.0280 (-0.110, 0.166) 0.691 -16.9213 (-53.897, 20.054) 0.370 0.0017 (-0.001, 0.004) 0.190 Parity 0.1212 (-0.303, 0.546) 0.575 57.6291 (-64.936, 180.194) 0.357 0.0027 (-0.006, 0.011) 0.534 Gravidity 0.1209 (-0.303, 0.544) 0.576 57.4048 (-64.547, 179.357) 0.356 0.0027 (-0.006, 0.011) 0.533 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers invited by journal 06 Jan, 2026 Editor invited by journal 12 Dec, 2025 Editor assigned by journal 11 Dec, 2025 Submission checks completed at journal 11 Dec, 2025 First submitted to journal 11 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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10:24:53","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137068,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8334089/v1/1782603c0697496874830eca.html"},{"id":100036448,"identity":"fa1fad11-6612-4270-af3c-b60982d99538","added_by":"auto","created_at":"2026-01-12 10:24:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":220852,"visible":true,"origin":"","legend":"\u003cp\u003eThis heatmap shows statistically significant correlation coefficients between Doppler ultrasound and magnetic resonance imaging measures of placental function. Warm colors represent positive correlations and cool colors represent negative correlations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8334089/v1/654ebfed79dd24d2090f900e.png"},{"id":100036617,"identity":"158c130e-f1ae-45dc-a745-c73719c6dde5","added_by":"auto","created_at":"2026-01-12 10:25:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":891157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8334089/v1/7f0bb67b-ea23-43dd-99e5-3352a82f086e.pdf"},{"id":100036452,"identity":"301d549c-5e89-4daa-a32e-7f8621e5a418","added_by":"auto","created_at":"2026-01-12 10:24:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":134535,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8334089/v1/12e08bee0dfbd7ab2102900e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Placental Vascular Function Across Imaging Scales: Comparative Insights from Contemporaneous MRI and Doppler Ultrasound","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe placenta is a dynamic organ that supports fetal growth through tightly regulated metabolic, endocrine, and oxygen transport functions at the maternal\u0026ndash;fetal circulatory interface. Because placental dysfunction underlies many adverse pregnancy outcomes, improving noninvasive methods for assessing placental function in utero is a clinical priority. Yet much remains to be learned about the capabilities and limitations of different imaging techniques in this role.\u003c/p\u003e \u003cp\u003eUltrasound (US) is a safe, accessible modality routinely used in obstetric care. Maternal placental perfusion is reflected in the uterine artery (UtA), which progressively vasodilates through gestation. Disturbances in this process may manifest as elevated UtA pulsatility index (PI) suggestive of higher vascular resistance as is reported in preeclampsia or fetal growth restriction [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The fetoplacental circulation is evaluated by umbilical artery (UA) US measurements where abnormalities manifest as absent or reversed end diastolic flow indicating increased vascular resistance, as seen in placental insufficiency [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite its ubiquity, US is operator-dependent and image quality can be limited by factors such as fetal position, maternal body habitus, and gestational age. Furthermore, conventional Doppler US primarily reflects macrovascular blood flow in vessels supplying and draining the placenta but cannot directly assess placental vasculature. Notably, studies suggest that UA PI does not become abnormal until over 60% of the placental vascular bed is obliterated [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], underscoring the limited sensitivity of standard US to mild or early placental vascular dysfunction. Magnetic resonance imaging (MRI) is another safe imaging modality, though less routinely used, that offers unique insights into placental morphology, microstructure, and function. Imaging techniques like dynamic contrast-enhanced, arterial spin labelled, diffusion-weighted and blood oxygen level dependent (BOLD) MRIs have been utilized to quantify spatiotemporal patterns in placental oxygen delivery [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Newer methods have assessed specific functional properties such as placental vascular reactivity (PLVR) by measuring placental BOLD MRI responses to maternal carbon dioxide changes elicited via coached breathing [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging (MRI) is another safe imaging modality, though less routinely used, that offers unique insights into placental morphology, microstructure, and function. Imaging techniques like dynamic contrast-enhanced, arterial spin labelled, diffusion-weighted and blood oxygen level dependent (BOLD) MRIs have been utilized to quantify spatiotemporal patterns in placental oxygen delivery [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Newer methods have assessed specific functional properties such as placental vascular reactivity (PLVR) by measuring placental BOLD MRI responses to maternal carbon dioxide changes elicited via coached breathing [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUltrasound primarily characterizes blood flow in vessels proximal (UtA) or distal (UA) to the placenta whereas MRI can interrogate the intervillous and microvascular environment within the placenta. Given these complementary strengths, it is important to clarify how findings from the two modalities relate to each other. Defining normal MRI-US relationships is a necessary foundation for interpreting MRI deviations in pathologic pregnancies. Prior studies have examined the correlation between 2D US measurements and placental MRI volume measurements [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and have evaluated both MRI and US measures in conditions of placental dysfunction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, in these studies US and MRI measurements were not contemporaneous, making it difficult to draw conclusions when scans were performed weeks apart.\u003c/p\u003e \u003cp\u003eThis study prospectively examined the relationship between Doppler US indices and MRI-based measures of placental structure and function, obtained in close temporal proximity. We hypothesized that MRI-derived metrics would correlate with Doppler indices, reflecting shared dependence on placental vascular health. Secondarily, we explored how maternal conditions such as diabetes, hypertension, and obesity, known to influence placental vasculature (without necessarily resulting in abnormal Doppler US indices or fetal outcomes) might alter this relationship. By establishing normative MRI\u0026ndash;US correlations and identifying how maternal risk factors modify them, this work provides a foundation for integrating MRI as a complementary tool for detecting subtle or early placental dysfunction beyond the reach of conventional US.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSubject Demographics\u003c/span\u003e: Pregnant mothers residing in and around Los Angeles County were recruited from May 2021 to April 2024 through flyers, self-referrals, and community partner clinics at Children\u0026rsquo;s Hospital Los Angeles (CHLA). Inclusion criteria were normally developing singleton pregnancies without congenital or genetic anomalies in women aged 20\u0026ndash;43 years, with gestational ages (GA) between 20 and 39 weeks. Exclusion criteria were fetal anatomic or genetic anomalies, fetal growth restriction, congenital infections, multiple pregnancies, and maternal MRI contraindications. Also excluded were pre-gestational insulin-dependent maternal diabetes and pre-gestational hypertension or preeclampsia. Informed consent was obtained under an Institutional Review Board (IRB) approved protocol at CHLA. Consented participants provided a self-report of prenatal health history and demographics including the following maternal risks: pregravid maternal BMI, presence of any form of diabetes mellitus during pregnancy, any form of hypertensive disorder during pregnancy, parity, gravidity, and maternal age on day of study visit. All fetuses were normally developing with no known anomalies; however, these were not uniformly \u0026ldquo;healthy\u0026rdquo; pregnancies due to the presence of minor maternal health risks. We intentionally included such pregnancies because while they are known to affect placental function their impact on fetal outcomes and US measures is mixed making them important confounders to represent in our cohort.\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eUltrasound Acquisition and Measurements\u003c/span\u003e: Within thirty minutes of the initiation or completion of the placental MRI, transabdominal US measures using 2D, color Doppler and pulsed wave Doppler were prospectively collected by a single operator with over 5 years of experience (SW). Either the Philips Epiq ultrasound machine with the curvilinear C5-1 or C9-2 transducers or the GE Voluson E10 ultrasound machine with the RM6C 4D transducer was used. Following guidelines on Doppler interrogation, measurements were acquired at an insonation angle of \u0026lt;\u0026thinsp;=\u0026thinsp;20\u0026deg; for pulsed-wave Doppler per established practice guidelines [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Vessels interrogated included the umbilical artery (UA), umbilical vein (UV), middle cerebral artery (MCA), ductus venosus (DV), and maternal uterine artery (UtA).\u003c/p\u003e \u003cp\u003eDoppler waveform patterns were noted, with an abnormal pattern defined as absence or reversal of end diastolic flow (AREDF) for the UA, AREDF or increased diastolic flow for the MCA, notching or pulsations for the UV, and absence or reversal of flow during atrial systole (A wave) for the DV. The following measurements were performed where possible: peak systolic velocity (PSV), end diastolic velocity (EDV), and time averaged maximal velocity (TAMAX) for the UA and UtA for those without AREDF; the UV mean velocity; the S wave velocity, A wave velocity, and TAMAX for the DV for those without absence or reversal of the A wave; and the PSV, EDV, and TAMAX for the MCA for those without AREDF. For the UA and MCA, the PI was calculated as (PSV\u0026thinsp;\u0026minus;\u0026thinsp;EDV)/ TAMAX. The cerebroplacental ratio (CPR) was calculated as MCA-PI/UA-PI. For quantitative assessment of the DV, the PI of veins (PIV) was calculated as PIV = (S wave velocity\u0026thinsp;\u0026minus;\u0026thinsp;early diastolic A wave velocity)/ TAMAX [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMRI Acquisition and Analysis\u003c/span\u003e: All participants underwent a placental MRI on a 3T Philips Ingenia scanner. Participants were placed feet-first and in supine position to avoid positional variability in blood flow due to gravity. A structural image was acquired for localization of placental anatomy. First, a BOLD MRI of the placenta was acquired for 7\u0026ndash;8 minutes with the following parameters: relaxation time\u0026thinsp;=\u0026thinsp;3000 ms, echo time\u0026thinsp;=\u0026thinsp;45 ms; flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, resolution\u0026thinsp;=\u0026thinsp;3.5 mm\u003csup\u003e3\u003c/sup\u003e, slices\u0026thinsp;=\u0026thinsp;65\u0026ndash;85, MB factor\u0026thinsp;=\u0026thinsp;2, number of dynamics\u0026thinsp;=\u0026thinsp;150. To measure PLVR, another BOLD MRI with the exact same parameters was obtained while the participant followed a coached breathing protocol involving a 15-second breath hold and three sighs per minute. Participant end-tidal carbon dioxide level (EtCO\u003csub\u003e2\u003c/sub\u003e) was determined from expired CO\u003csub\u003e2\u003c/sub\u003e measured via a nasal cannula connected to the Respsense Capnogram (Nonin Medical Inc, Plymouth, MN, USA).\u003c/p\u003e \u003cp\u003ePlacental Spatiotemporal Variance: Placental spatial and temporal variance were computed using previously validated methods [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. All raw data were manually reviewed for large motion artifacts and fetal-maternal motion correction was performed by an inter-volume non-rigid registration followed by non-linear registration. Temporal variance quantifies the natural physiological fluctuations in blood inflow and outflow from the intervillous space. For each placenta, we first manually defined a mask to isolate the placenta from surrounding tissues. Within this mask, we extracted the mean BOLD signal time series at each voxel. The temporal variance was then calculated as the standard deviation of this time series. This value was normalized by the overall mean BOLD signal to allow for comparison between subjects.\u003c/p\u003e \u003cp\u003eIn parallel, spatial variance quantifies the heterogeneity of blood flow and oxygenation across the placental anatomy. First, the BOLD signal for each voxel at each time point, within the manually delineated mask, was normalized by the mean signal of the entire placenta at each dynamic. Next, the standard deviation of these normalized values across all placental voxels at a single time point was calculated. The final spatial variance value for each subject was then determined by averaging this measure over the entire scan, providing a comprehensive view of the heterogeneity.\u003c/p\u003e \u003cp\u003ePlacental Vascular Reactivity (PLVR): To calculate PLVR, both voxel-wise BOLD signal within the placental mask and maternal EtCO\u003csub\u003e2\u003c/sub\u003e were converted to the frequency domain using fast-fourier transform. Next, PLVR was calculated as the sum of the ratio of the BOLD to EtCO2 signal magnitudes across all frequencies. This ratio was weighted by the spectral coherence between the two signals to reduce noise. A frequency-dependent scaling factor was also applied to account for the delay in the vascular response to CO2 changes. Finally, a global PLVR value was determined by averaging the voxel-wise PLVR results within the manually defined placental volume of interest. PLVR quantifies the placental response to transient changes in maternal CO\u003csub\u003e2\u003c/sub\u003e. This reflects the functional capacity of placental vasculature to match blood supply to fetal demand, which is known to be regulated by placental endothelial-derived mediators [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical Methods\u003c/span\u003e: All statistical analyses were performed using Python 3.9. A p-value of less than 0.05 was considered statistically significant. Descriptive statistics for clinical and demographic characteristics of the study cohort are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and as percentages for categorical variables. To consistently account for gestational age and fetal sex in all models, raw ultrasound values rather than published z-scores (which do not account for fetal sex) were used in this analysis. Spearman\u0026rsquo;s correlation, selected due to the non-parametric nature of the data and its robustness to outliers, was employed to assess associations between ultrasound and MRI measures. Statistical significance was determined using a two-tailed p-value. The associations between various maternal risk factors and US or MRI measures was examined using generalized linear model models (gamma regression with logit link function) to account for the likely skewed distribution of the US/MRI measures. For each combination of a US/MRI measure (dependent variable) and a maternal risk factor (independent variable), a separate model was fitted. All models included gestational age at MRI scan as a covariate. Robust standard errors (HC1) were utilized to address potential heteroscedasticity. Subsequently, maternal risk factors with a significant relationship to US or MRI placental measures were included in a partial correlation analysis that evaluated the impact of each maternal risk factor on the correlation between ultrasound and MRI placental measures.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cu\u003eClinical and Demographic Characteristics of Study Populatio\u003c/u\u003en: Clinical and demographic characteristics of participants included in the study are shown in Table 1. A total of 56 pregnant mothers were recruited for this study, with 55 mother-fetus dyads completing the MR imaging session. After imaging, all 56 subjects completed an ultrasound. As a result, 55 subjects had both analyzable MRI and US data and were used for further analysis. Figures showing distribution of z-scores for all US values, calculated from published\u0026nbsp;norms\u0026nbsp;[13-17], are included in the Supplementary Figure 1(a-d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCorrelation between placental MRI and ultrasound measures:\u003c/u\u003e\u0026nbsp; A heatmap generated to visualize statistically significant correlations between placental MRI and ultrasound measures is seen in Figure 1. Warm colors represent positive correlations, and cool colors represent negative correlations. Our findings showed significant positive correlations between UV mean velocity and both placental spatial variance (r=0.45) and placental temporal variance (r=0.33) on MRI suggesting that increased UV velocity and flow is associated with greater spatial and temporal variance on MRI. Additionally, UA-PI was negatively correlated with placental spatial variance (r=-0.35) indicating that higher umbilical artery resistance is associated with less spatial variance.\u003c/p\u003e\n\u003cp\u003eCorrelation analysis amongst the ultrasound measures revealed a negative correlation between UA-PI and UV mean velocity (r=-0.43) suggesting higher resistance in the umbilical artery is associated with lower umbilical vein flow. There was also an expected negative correlation between UA-PI and CPR (r=-0.38) since the latter factors in the UA-PI into its calculation. Ductus venosus measures and uterine PI showed no significant correlations with either other ultrasound measures or MRI measures.\u003c/p\u003e\n\u003cp\u003eCorrelation analysis amongst the MRI measures revealed strong positive correlations between all three measures, specifically placental spatial variance and temporal variance (r=0.59), placental spatial variance and PLVR (r=0.79), and placental temporal variance and PLVR (r=0.5). This demonstrates a consistent pattern across different aspects of placental MRI assessment.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMaternal Risk Factors and placental ultrasound measures\u003c/u\u003e: Table 2 shows the regression coefficients and p-values for regression analysis of the relationship between maternal risk factors and placental ultrasound measures. Significant decreases in UV mean velocity were observed in the presence of maternal diabetes (\u0026beta; = -1.24, 95% CI: -2.49, -0.051, p = 0.05), higher parity (\u0026beta; = -0.5701, 95% CI: -1.045, -0.095, p = 0.025), and higher gravidity (\u0026beta; = -0.5661, 95% CI: -1.041, -0.091, p = 0.026). A significant positive association was found between pregravid BMI and UA-PI (\u0026beta; = 0.0056, 95% CI: 0.000, 0.011, p = 0.036), indicating increased vascular resistance in pregnancies with higher maternal BMI. The presence of maternal hypertension was associated with a reduced CPR (\u0026beta; = -0.426, 95% CI: -0.664, -0.189, p = 0.000), while both higher parity (\u0026beta; = 0.1020, 95% CI: 0.040, 0.163, p = 0.001) and higher gravidity (\u0026beta; = 0.1023, 95% CI: 0.041, 0.164, p = 0.001) were positively associated with CPR.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMaternal Risk Factors and placental MRI measures\u003c/u\u003e: Table 3 shows the regression coefficients and p-values for regression analysis of the relationship between maternal risk factors and placental MRI measures. There was a significant negative association between maternal diabetes and placental spatial variance (\u0026beta;=\u0026minus;1.76,\u0026nbsp;95% CI: -2.304 to -1.226, p\u0026nbsp;=\u0026nbsp;0.000), placental temporal variance (\u0026beta;=\u0026minus;1.518,\u0026nbsp;95% CI: -2.301 to -0.735, p = 0.00) as well as between maternal diabetes and placental vascular reactivity (\u0026beta;= -0.91, 95% CI: -1.52 to -0.303, p=0.003). There was also a negative association between maternal hypertension and placental spatial variance (\u0026beta;=\u0026minus;1.662, 95% CI: -2.074 to -1.25, p=0.000). Other maternal risk factors such as maternal BMI, maternal age, and maternal parity/gravidity did not show significant associations with MRI placental measures.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEffect of maternal risk factors on US and MRI correlations:\u0026nbsp;\u003c/u\u003eIn the presence of maternal hypertension, higher maternal age, or higher BMI, the negative correlation between UA-PI and placental MRI spatial variance (r=-0.36) as well as positive correlation between UV mean velocity and spatial variance (r=0.39) persisted. However, when evaluating the effect of maternal diabetes, the only correlation that remained was the positive correlation between UV mean velocity and spatial variance (r=0.34) while UA-PI correlation was no longer significant.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings show that intrinsic functional characteristics of the placenta measured using MRI are correlated to umbilical Doppler hemodynamics in normally developing fetuses. Higher UV velocity on Doppler US correlated with increased temporal and spatial variance on MRI. As the sole vessel through which all blood passes from the placenta to the fetus, the UV flow and velocity measures are largely determined by placental vascular characteristics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Efficient placental microvascular function\u0026mdash;manifested as high spatial (vascular integrity) and temporal (dynamic adaptation) variance\u0026mdash;is physiologically expected to coincide with increased UV flow velocity. In contrast, impaired placental perfusion, as seen in intrauterine growth restriction or severe congenital heart disease, is associated with abnormal umbilical artery Doppler measures [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Elevated UA pulsatility index (PI) indicates increased vascular impedance due to reduced fetal inflow or impaired gas exchange [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; its inverse relationship with placental temporal variance is corroborated by ex vivo experiments demonstrating increased fetoplacental vascular resistance in response to impaired uteroplacental circulation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, UA-PI decreases with GA, whereas both placental spatial and temporal variances increase with GA further explaining their inverse correlation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. PLVR showed no correlations with US measures likely because it is a response elicited from a coached breathing stimulus, which was not performed during US acquisition. It is interesting to note that MRI measures of placental function did not correlate with ductus venosus or middle cerebral artery US measures. This result is not surprising as DV and MCA abnormalities are usually late findings and is likely explained by the complex interactions of the DV and MCA flow with factors other than just the placenta (ie, DV reflects cardiac preload and central venous pressure and MCA reflects cerebrovascular autoregulation). Since all placental MRI measures leveraged vascular and oxygenation properties of the placenta, there was a high correlation amongst them even in the presence of maternal risk factors.\u003c/p\u003e \u003cp\u003eOur MRI\u0026ndash;US correlations align with prior studies using arterial spin labeling (ASL), intravoxel incoherent motion (IVIM), T2/T2*, and 4D flow MRI [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Reduced perfusion on ASL and IVIM correlates with elevated UA-PI in small-for-gestational-age pregnancies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], while lower placental T2 and T2* relaxation times\u0026mdash;indicating decreased oxygenation\u0026mdash;are inversely related to uterine artery PI. These studies support a consistent pattern: MRI-derived metrics probing various aspects of placental function (perfusion, oxygenation, or structure) correspond to Doppler indices of vascular resistance. Together, they reinforce MRI\u0026rsquo;s role as a complementary tool that provides microvascular insight beyond the macroscopic flow information from Doppler US.\u003c/p\u003e \u003cp\u003eIn our study, maternal diabetes was associated with reduced placental spatial and temporal variance, PLVR, and UV velocity, indicating vascular dysfunction. Prior animal and histopathologic studies show that diabetes disrupts vasculogenesis and increases villous immaturity [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Other Doppler work found no consistent UV differences between diabetic and non-diabetic pregnancies, suggesting fetal size rather than diabetic status drives UV variation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Consistent with prior reports, diabetes did not significantly affect UA-PI in our cohort. When accounting for diabetes, correlations between MRI and UA-PI were no longer significant, likely reflecting heterogeneous placental and cord adaptations influenced by diabetes type, onset, and glycemic control [\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Although UA-PI typically remains normal unless severe vasculopathy or growth restriction develops [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], diabetic placentas often exhibit microvascular remodeling and villous immaturity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The loss of correlation between UA-PI and MRI measures underscores ultrasound\u0026rsquo;s limited sensitivity to early vascular changes that MRI may be able to detect\u0026mdash;changes that may contribute to adverse outcomes even in otherwise normal-appearing pregnancies with normal US values [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the effect of hypertension on placental and fetal circulation was in line with other studies, we caution against drawing conclusions from this study based on limited incidence of hypertension in our cohort. The positive association between high BMI and increased resistance in the UA is well-supported by other studies [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The lack of associations between maternal pregravid BMI and placental MRI measures could be due to the impact of maternal BMI on placental metabolic function through mechanisms such as hyperlipidemia [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince MRI is not feasible for routine screening, defining its relationship to standard Doppler measures is clinically important. MRI directly quantifies placental vascular and oxygenation properties, allowing detection of subclinical or localized dysfunction that may precede Doppler changes. Combining MRI and US provides a more comprehensive assessment of placental health and could possibly identify with greater sensitivity when deviations become pathologic. Future longitudinal studies are needed to explore the relationship between MRI\u0026ndash;US metrics with perinatal outcomes, in both physiologic and pathologic conditions. Larger studies are warranted to establish normative reference curves and define MRI\u0026rsquo;s sensitivity and sepcificity for detecting early placental dysfunction.\u003c/p\u003e \u003cp\u003eOur study has several strengths, including contemporaneous acquisition of US and MRI within 30 minutes which minimizes the impact of confounding factors on these measurements, such as the mom\u0026rsquo;s physical state or changes inherent to different gestational ages. Secondly, by exclusively recruiting pregnant mothers with normally developing fetuses rather than including pathological conditions, the study is able to focus on intermodality correlations of typical placental function. Operator variability is significantly reduced as all US measurements were consistently performed prospectively by the same operator. Despite these strengths, our study has certain limitations. The reliance on a single operator for all US measurements, while reducing variability, may limit the generalizability of the findings to other settings where multiple operators are involved; however, US measures have been shown to be highly reproducible when acquired according to standard guidelines. Another limitation is that our study is cross-sectional in nature and cannot provide causal inference into the relationship between placental imaging measures and perinatal outcomes. Finally, our cohort had an uneven distribution in the prevalence of maternal risks, warranting future studies focusing on specific maternal conditions and long term postnatal follow up to truly define the impact of an abnormal maternal environment on fetal health.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study is the first to report correlations between placental US and MRI measures obtained in close temporal proximity in a prospective cohort of pregnancies with normally developing fetuses. Our findings highlight that MRI-derived placental oxygenation characteristics correlate with umbilical artery and vein hemodynamics. Significant correlations between ultrasound umbilical artery PI and umbilical vein velocity, and MRI measures of spatial and temporal variance suggest that these metrics, measured on different scales, reflect micro- and macrovascular function in the feto-placental system. The presence of maternal diabetes modifies the associations between US and MRI measures, further highlighting its impact on placental vascular function. These results highlight the complementary roles of US and MRI in assessing placental function.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy approval statement: This study protocol was reviewed and approved by the Children’s Hospital Los Angeles Institutional Review Board (IRB): approval number CHLA-17-00292. Date of approval 9/14/2017 (updated 6/9/2025).\u003cbr\u003eConsent to participate statement: Written consent was obtained from all participating pregnant women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials statement:\u003c/strong\u003e The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not supported by any sponsor or funder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e VR, RP: patient recruitment and data acquisition, review and approval; JD, JW, JVS, BG: interpretation of data, review and approval. SW, VR: study design, data acquisition, analysis of data, interpretation of data, drafting the work and approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBarati M, Shahbazian N, Ahmadi L, Masihi S. Diagnostic evaluation of uterine artery Doppler sonography for the prediction of adverse pregnancy outcomes. \u003cem\u003eJ Res Med Sci\u003c/em\u003e. 2014;19(6):515-519.\u003c/li\u003e\n \u003cli\u003eOnwudiwe N, Yu CK, Poon LC, Spiliopoulos I, Nicolaides KH. Prediction of pre-eclampsia by a combination of maternal history, uterine artery Doppler and mean arterial pressure. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2008;32(7):877-883. doi:10.1002/uog.6124\u003c/li\u003e\n \u003cli\u003eLlurba E, Carreras E, Gratac\u0026oacute;s E, et al. 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Non-invasive in-utero quantification of vascular reactivity in human placenta. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2024;63(4):481-488. doi:10.1002/uog.27512\u003c/li\u003e\n \u003cli\u003eSagberg K, Eskild A, Sommerfelt S, Halle TK, Hillestad V, Haavaldsen C. Two-dimensional (2D) placental ultrasound measurements - The correlation with placental volume measured by magnetic resonance imaging (MRI). \u003cem\u003ePlacenta\u003c/em\u003e. 2024;149:7-12. doi:10.1016/j.placenta.2024.02.010\u003c/li\u003e\n \u003cli\u003eNakao KK, Kido A, Fujimoto K, et al. Placental functional assessment and its relationship to adverse pregnancy outcome: comparison of intravoxel incoherent motion (IVIM) MRI, T2-relaxation time, and umbilical artery Doppler ultrasound. \u003cem\u003eActa Radiol\u003c/em\u003e. 2023;64(1):370-376. doi:10.1177/02841851211060410\u003c/li\u003e\n \u003cli\u003eBhide A, Acharya G, Bilardo CM, et al. ISUOG practice guidelines: use of Doppler ultrasonography in obstetrics. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2013;41(2):233-239. doi:10.1002/uog.12371\u003c/li\u003e\n \u003cli\u003eTuran OM, Turan S, Sanapo L, et al. Reference ranges for ductus venosus velocity ratios in pregnancies with normal outcomes. \u003cem\u003eJ Ultrasound Med\u003c/em\u003e. 2014;33(2):329-336. doi:10.7863/ultra.33.2.329\u003c/li\u003e\n \u003cli\u003eAcharya G, Wilsgaard T, Berntsen GK, Maltau JM, Kiserud T. Doppler-derived umbilical artery absolute velocities and their relationship to fetoplacental volume blood flow: a longitudinal study. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2005;25(5):444-453. doi:10.1002/uog.1880\u003c/li\u003e\n \u003cli\u003eFlo K, Wilsgaard T, Acharya G. Longitudinal reference ranges for umbilical vein blood flow at a free loop of the umbilical cord. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2010;36(5):567-572. doi:10.1002/uog.7730\u003c/li\u003e\n \u003cli\u003eEbbing C, Rasmussen S, Kiserud T. Middle cerebral artery blood flow velocities and pulsatility index and the cerebroplacental pulsatility ratio: longitudinal reference ranges and terms for serial measurements. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2007;30(3):287-296. doi:10.1002/uog.4088\u003c/li\u003e\n \u003cli\u003eG\u0026oacute;mez O, Figueras F, Fern\u0026aacute;ndez S, et al. Reference ranges for uterine artery mean pulsatility index at 11-41 weeks of gestation. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2008;32(2):128-132. doi:10.1002/uog.5315\u003c/li\u003e\n \u003cli\u003eRajagopalan V, Schmithorst V, El-Ali A, et al. Associations between Maternal Risk Factors and Intrinsic Placental and Fetal Brain Functional Properties in Congenital Heart Disease. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2022;23(23):15178. Published 2022 Dec 2. doi:10.3390/ijms232315178\u003c/li\u003e\n \u003cli\u003eHitzerd E, Broekhuizen M, Neuman RI, et al. Human Placental Vascular Reactivity in Health and Disease: Implications for the Treatment of Pre-eclampsia. \u003cem\u003eCurr Pharm Des\u003c/em\u003e. 2019;25(5):505-527. doi:10.2174/1381612825666190405145228\u003c/li\u003e\n \u003cli\u003eSferruzzi-Perri AN, Lopez-Tello J, Salazar-Petres E. Placental adaptations supporting fetal growth during normal and adverse gestational environments. \u003cem\u003eExp Physiol\u003c/em\u003e. 2023;108(3):371-397. doi:10.1113/EP090442\u003c/li\u003e\n \u003cli\u003eNajafzadeh A, Dickinson JE. Umbilical venous blood flow and its measurement in the human fetus. \u003cem\u003eJ Clin Ultrasound\u003c/em\u003e. 2012;40(8):502-511. doi:10.1002/jcu.21970\u003c/li\u003e\n \u003cli\u003eSebire NJ, Sepulveda W. Correlation of placental pathology with prenatal ultrasound findings. \u003cem\u003eJ Clin Pathol\u003c/em\u003e. 2008;61(12):1276-1284. doi:10.1136/jcp.2008.055251\u003c/li\u003e\n \u003cli\u003eWang S, Freud LR, Detterich J, et al. Extracardiac Doppler indices predict perinatal mortality in fetuses with Ebstein anomaly and tricuspid valve dysplasia. \u003cem\u003ePrenat Diagn\u003c/em\u003e. 2021;41(3):332-340. doi:10.1002/pd.5873\u003c/li\u003e\n \u003cli\u003eHernandez-Andrade E, Huntley ES, Bartal MF, et al. Doppler evaluation of normal and abnormal placenta. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2022;60(1):28-41. doi:10.1002/uog.24816\u003c/li\u003e\n \u003cli\u003eMeler E, Mart\u0026iacute;nez J, Boada D, Mazarico E, Figueras F. Doppler studies of placental function.\u0026nbsp;\u003cem\u003ePlacenta\u003c/em\u003e. 2021;108:91-96. doi:10.1016/j.placenta.2021.03.014\u003c/li\u003e\n \u003cli\u003eLang U, Baker RS, Khoury J, Clark KE. Fetal umbilical vascular response to chronic reductions in uteroplacental blood flow in late-term sheep. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e. 2002;187(1):178-186. doi:10.1067/mob.2002.122849\u003c/li\u003e\n \u003cli\u003eAcharya G, Sonesson SE, Flo K, R\u0026auml;s\u0026auml;nen J, Odibo A. Hemodynamic aspects of normal human feto-placental (umbilical) circulation. \u003cem\u003eActa Obstet Gynecol Scand\u003c/em\u003e. 2016;95(6):672-682. doi:10.1111/aogs.12919\u003c/li\u003e\n \u003cli\u003eHwuang E, Wu PH, Rodriguez-Soto A, et al. Cross-modality and in-vivo validation of 4D flow MRI evaluation of uterine artery blood flow in human pregnancy. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2021;58(5):722-731. doi:10.1002/uog.23112\u003c/li\u003e\n \u003cli\u003eDerwig I, Lythgoe DJ, Barker GJ, Poon L, Gowland P, Yeung R, Zelaya F, Nicolaides K. Association of placental perfusion, as assessed by magnetic resonance imaging and uterine artery Doppler ultrasound, and its relationship to pregnancy outcome. Placenta. 2013 Oct;34(10):885-91. doi: 10.1016/j.placenta.2013.07.006.\u003c/li\u003e\n \u003cli\u003eKristi B A, Ditte N H, Caroline H, et al. Placental diffusion-weighted MRI in normal pregnancies and those complicated by placental dysfunction due to vascular malperfusion. \u003cem\u003ePlacenta\u003c/em\u003e. 2020;91:52-58. doi:10.1016/j.placenta.2020.01.009\u003c/li\u003e\n \u003cli\u003eLeach L, Taylor A, Sciota F. Vascular dysfunction in the diabetic placenta: causes and consequences. \u003cem\u003eJ Anat\u003c/em\u003e. 2009;215(1):69-76. doi:10.1111/j.1469-7580.2009.01098.x\u003c/li\u003e\n \u003cli\u003eHuynh J, Dawson D, Roberts D, Bentley-Lewis R. A systematic review of placental pathology in maternal diabetes mellitus. \u003cem\u003ePlacenta\u003c/em\u003e. 2015;36(2):101-114. doi:10.1016/j.placenta.2014.11.021\u003c/li\u003e\n \u003cli\u003eCarrasco-Wong I, Moller A, Giachini FR, et al. Placental structure in gestational diabetes mellitus. \u003cem\u003eBiochim Biophys Acta Mol Basis Dis\u003c/em\u003e. 2020;1866(2):165535. doi:10.1016/j.bbadis.2019.165535\u003c/li\u003e\n \u003cli\u003eTo WW, Mok CK. Fetal umbilical arterial and venous Doppler measurements in gestational diabetic and nondiabetic pregnancies near term. \u003cem\u003eJ Matern Fetal Neonatal Med\u003c/em\u003e. 2009;22(12):1176-1182. doi:10.3109/14767050903042546\u003c/li\u003e\n \u003cli\u003eKoskinen A, Lehtoranta L, Laiho A, Laine J, K\u0026auml;\u0026auml;p\u0026auml; P, Soukka H. Maternal diabetes induces changes in the umbilical cord gene expression. \u003cem\u003ePlacenta\u003c/em\u003e. 2015;36(7):767-774. doi:10.1016/j.placenta.2015.04.004\u003c/li\u003e\n \u003cli\u003eTenaw Goshu B. Histopathologic Impacts of Diabetes Mellitus on Umbilical Cord During Pregnancy. \u003cem\u003ePediatric Health Med Ther\u003c/em\u003e. 2022;13:37-41. Published 2022 Feb 18. doi:10.2147/PHMT.S323812\u003c/li\u003e\n \u003cli\u003eLeach L. Placental vascular dysfunction in diabetic pregnancies: intimations of fetal cardiovascular disease?. \u003cem\u003eMicrocirculation\u003c/em\u003e. 2011;18(4):263-269. doi:10.1111/j.1549-8719.2011.00091.x\u003c/li\u003e\n \u003cli\u003eStarikov R, Inman K, Chen K, et al. Comparison of placental findings in type 1 and type 2 diabetic pregnancies. \u003cem\u003ePlacenta\u003c/em\u003e. 2014;35(12):1001-1006. doi:10.1016/j.placenta.2014.10.008\u003c/li\u003e\n \u003cli\u003eKapustin RV, Kopteyeva EV, Tral TG, Tolibova GK. Placental morphology in different types of diabetes mellitus. \u003cem\u003eJournal of Obstetrics and Women\u0026rsquo;s Diseases\u003c/em\u003e. 2021;70(2):13-26. doi:10.17816/JOWD57149\u003c/li\u003e\n \u003cli\u003eReece EA, Homko CJ, Wiznitzer A. Doppler velocimetry and the assessment of fetal well-being in normal and diabetic pregnancies. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 1994;4(6):508-514. doi:10.1046/j.1469-0705.1994.04060508.\u003c/li\u003e\n \u003cli\u003eHong J, Crawford K, Cavanagh E, et al. The relationship between abnormal fetoplacental Dopplers, angiogenic markers of placental dysfunction and adverse perinatal outcomes in diabetic pregnancies with small fetuses - A prospective study. \u003cem\u003ePlacenta\u003c/em\u003e. 2025;160:51-59. doi:10.1016/j.placenta.2024.12.025\u003c/li\u003e\n \u003cli\u003eAldahmash WM, Alwasel SH, Aljerian K. Gestational diabetes mellitus induces placental vasculopathies. \u003cem\u003eEnviron Sci Pollut Res Int\u003c/em\u003e. 2022;29(13):19860-19868. doi:10.1007/s11356-021-17267-y\u003c/li\u003e\n \u003cli\u003eHuynh J, Dawson D, Roberts D, Bentley-Lewis R. A systematic review of placental pathology in maternal diabetes mellitus. \u003cem\u003ePlacenta\u003c/em\u003e. 2015;36(2):101-114. doi:10.1016/j.placenta.2014.11.021\u003c/li\u003e\n \u003cli\u003eScholtens DM, Kuang A, Lowe LP, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): Maternal Glycemia and Childhood Glucose Metabolism. \u003cem\u003eDiabetes Care\u003c/em\u003e. 2019;42(3):381-392. doi:10.2337/dc18-2021\u003c/li\u003e\n \u003cli\u003eSarno L, Maruotti GM, Saccone G, Morlando M, Sirico A, Martinelli P. Maternal body mass index influences umbilical artery Doppler velocimetry in physiologic pregnancies. \u003cem\u003ePrenat Diagn\u003c/em\u003e. 2015;35(2):125-128. doi:10.1002/pd.4499\u003c/li\u003e\n \u003cli\u003eCody F, Mullers S, Flood K, et al. Correlation of maternal body mass index with umbilical artery Doppler in pregnancies complicated by fetal growth restriction and associated outcomes. \u003cem\u003eInt J Gynaecol Obstet\u003c/em\u003e. 2021;154(2):352-357. doi:10.1002/ijgo.13586\u003c/li\u003e\n \u003cli\u003eMyatt L, Maloyan A. Obesity and Placental Function. \u003cem\u003eSemin Reprod Med\u003c/em\u003e. 2016;34(1):42-49. doi:10.1055/s-0035-157002\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Study participant demographic and clinical characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eMaternal Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e31.5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e20-43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eGestational age at MRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e30.6 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Range \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e20.1-39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eGravidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e2.8 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.8 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eMaternal Diabetes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e7/56 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003ePregravid Maternal BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e28 \u0026plusmn; 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e13.4-54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eMaternal Hypertension\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e2/56 (3.6%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eRace and Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Hispanic \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e40/56 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e9/56 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Non-Hispanic Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e3/56 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Non-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1/56 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e3/56 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003eFetal Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e31/56 (55.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 311px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e25/56 (44.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: Associations between Doppler ultrasound measures of placental function and maternal risk factors. Regression coefficients, confidence intervals, and p-value of models are shown. Statistically significant relationships are denoted with an asterisk*.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eMaternal Risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eUterine PI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003eUA PI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eUV Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eCPR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0613 (-0.063, 0.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0978 (-0.047, 0.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e-1.8479 (-3.598,-0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0135 (-0.399, 0.426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003ePregravid BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0082 (-0.003, 0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0056 (0.000, 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0228 (-0.079, 0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.0095 (-0.030, 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.8399 (-0.277, 1.957)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.3575 (-0.044, 0.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e-1.8095 (-4.250, 0.631)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.6369 (-0.943, -0.331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.0107 (-0.022, 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0001 (-0.008, 0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0202 (-0.172, 0.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0147 (-0.005, 0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.0019 (-0.055, 0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.0092 (-0.040, 0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e-0.5701 (-1.045, -0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.1020 (0.040, 0.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eGravidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.0021 (-0.055, 0.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.0089 (-0.040, 0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.5661 (-1.041, -0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.1023 (0.041, 0.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 3: Associations between magnetic resonance imaging measures of placental function and maternal risk factors. Regression coefficients, confidence intervals, and p-value of models are shown. Statistically significant relationships are denoted with an asterisk*.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMaternal Risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003ePlacenta Spatial Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 216px;\"\u003e\n \u003cp\u003ePlacenta Temporal Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 194px;\"\u003e\n \u003cp\u003ePlacental Vascular Reactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e-1.2186 (-1.965, -0.472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003e-143.3930 (-332.763, 45.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e-0.0218 (-0.038, -0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePregravid BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.0112 (-0.069, 0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003e1.3524 (-7.195, 9.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.0003 (-0.002, 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e-1.1654 (-2.329, -0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.050*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003e-79.5066 (-294.982, 135.968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e-0.0229 (-0.053, 0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.0280 (-0.110, 0.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003e-16.9213 (-53.897, 20.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.0017 (-0.001, 0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.1212 (-0.303, 0.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003e57.6291 (-64.936, 180.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.0027 (-0.006, 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eGravidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.1209 (-0.303, 0.544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003e57.4048 (-64.547, 179.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.0027 (-0.006, 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Placental magnetic resonance imaging, placental ultrasound, placental vascula function ","lastPublishedDoi":"10.21203/rs.3.rs-8334089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8334089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/u\u003e: The placenta plays a critical role in fetal development. This study investigates correlations between contemporaneous Doppler ultrasound (US) and magnetic resonance imaging (MRI) measures of placental and fetal vascular function in pregnancies with normally developing fetuses.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/u\u003e: In this prospective study, 56 pregnant women at 20–39 weeks' gestation underwent Doppler US (uterine artery, umbilical artery and vein, middle cerebral artery, ductus venosus) and placental MRI (spatial variance, temporal variance, vascular reactivity) within 30 minutes of each other. Pearson's correlation assessed associations between US and MRI measures, and regression models tested the impact of maternal risk factors on these correlations.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/u\u003e: Umbilical vein (UV) velocity correlated positively with MRI spatial and temporal variance (r=0.45 and r=0.33, p\u0026lt;0.05), and umbilical artery pulsatility index (UA-PI) negatively correlated with spatial variance (r=-0.35, p\u0026lt;0.05). These correlations persisted in the presence of maternal risk factors except for maternal diabetes. Higher pregravid BMI was associated with increased UA-PI (p=0.039) and diabetes with lower UV velocity (p=0.036).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/u\u003e: Correlations between UA-PI, UV velocity, and MRI spatial and temporal variance suggest that these interrelated metrics capture complementary aspects of feto-placental micro- and macrovascular function and may improve assessment of placental health.\u003c/p\u003e","manuscriptTitle":"Placental Vascular Function Across Imaging Scales: Comparative Insights from Contemporaneous MRI and Doppler Ultrasound","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 10:23:46","doi":"10.21203/rs.3.rs-8334089/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"250714013696944275631580354121115895003","date":"2026-01-08T16:30:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225947886336265535318355075025808664207","date":"2026-01-06T16:30:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-06T16:16:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-12T11:40:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-11T23:23:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-11T23:22:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-12-11T07:45:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9afd9b14-89de-40af-afc4-881fe111df5e","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T10:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 10:23:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8334089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8334089","identity":"rs-8334089","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}