Ferumoxytol dynamic contrast enhanced magnetic resonance imaging identifies altered placental cotyledon perfusion in rhesus macaques

Daniel P Seiter; Sydney M Nguyen; Terry K Morgan; Lu Mao; Dawn M Dudley; David H O’connor; Megan E Murphy; Kai D Ludwig; Ruiming Chen; Archana Dhyani; Ante Zhu; Michele L Schotzko; Kevin G Brunner; Dinesh M Shah; Kevin M Johnson; Thaddeus G Golos; Oliver Wieben

doi:10.1093/biolre/ioac168

. 2022 Aug 26;107(6):1517–1527. doi: 10.1093/biolre/ioac168

Ferumoxytol dynamic contrast enhanced magnetic resonance imaging identifies altered placental cotyledon perfusion in rhesus macaques^{^†}

Daniel P Seiter ^1,², Sydney M Nguyen ^2,^3,^4,², Terry K Morgan ⁵, Lu Mao ⁶, Dawn M Dudley ⁷, David H O’connor ^8,⁹, Megan E Murphy ^10,^11,¹², Kai D Ludwig ¹³, Ruiming Chen ¹⁴, Archana Dhyani ¹⁵, Ante Zhu ^16,¹⁷, Michele L Schotzko ¹⁸, Kevin G Brunner ¹⁹, Dinesh M Shah ²⁰, Kevin M Johnson ^21,²², Thaddeus G Golos ^23,^24,^25,^3,^✉, Oliver Wieben ^26,^27,³

¹ Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA

² Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, USA

³ Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA

⁴ Department of Obstetrics & Gynecology, University of Wisconsin-Madison, Madison, WI, USA

⁵ Department of Pathology, Oregon Health & Science University, Portland, OR, USA

⁶ Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA

⁷ Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA

⁸ Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA

⁹ Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA

¹⁰ Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, USA

¹¹ Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA

¹² Department of Obstetrics & Gynecology, University of Wisconsin-Madison, Madison, WI, USA

¹³ Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA

¹⁴ Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA

¹⁵ Department of Computer Science, University of Wisconsin-Madison, Madison, WI, USA

¹⁶ Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA

¹⁷ Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA

¹⁸ Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA

¹⁹ Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA

²⁰ Department of Obstetrics & Gynecology, University of Wisconsin-Madison, Madison, WI, USA

²¹ Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA

²² Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA

²³ Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, USA

²⁴ Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA

²⁵ Department of Obstetrics & Gynecology, University of Wisconsin-Madison, Madison, WI, USA

²⁶ Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA

²⁷ Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA

^✉

Correspondence: Thaddeus G. Golos, Ph.D., University of Wisconsin-Madison, Dept. of Comparative Biosciences, 1223 Capitol Ct., Madison, WI 53715-1299, USA. E-mail: golos@primate.wisc.edu

Daniel P. Seiter and Sydney M. Nguyen Co-first authors

Thaddeus G. Golos and Oliver Wieben Co-senior authors

PMCID: PMC9752971 PMID: 36018823

Abstract

Identification of placental dysfunction in early pregnancy with noninvasive imaging could be a valuable tool for assessing maternal and fetal risk. Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) can be a powerful tool for interrogating placenta health. After inoculation with Zika virus or sham inoculation at gestation age (GA) 45 or 55 days, animals were imaged up to three times at GA65, GA100, and GA145. DCE MRI images were acquired at all imaging sessions using ferumoxytol, an iron nanoparticle-based contrast agent, and analyzed for placental intervillous blood flow, number of perfusion domains, and perfusion domain volume. Cesarean section was performed at GA155, and the placenta was photographed and dissected for histopathology. Photographs were used to align cotyledons with estimated perfusion domains from MRI, allowing comparison of estimated cotyledon volume to pathology. Monkeys were separated into high and low pathology groups based on the average number of pathologies present in the placenta. Perfusion domain flow, volume, and number increased through gestation, and total blood flow increased with gestation for both low pathology and high pathology groups. A statistically significant decrease in perfusion domain volume associated with pathology was detected at all gestational ages. Individual perfusion domain flow comparisons demonstrated a statistically significant decrease with pathology at GA100 and GA145, but not GA65. Since ferumoxytol is currently used to treat anemia during human pregnancy and as an off-label MRI contrast agent, future transition of this work to human pregnancy may be possible.

Keywords: blood flow, contrast enhanced, cotyledon, ferumoxytol, gestation, MRI, magnetic resonance imaging, pathology, perfusion, perfusion domain, placenta, rhesus macaque, Zika virus

Ferumoxytol dynamic contrast-enhanced MRI can detect changes in placental perfusion associated with term placental pathology after Zika infection in early gestation.

Graphical Abstract

Introduction

Placental development plays a crucial role in a successful pregnancy. If the fetus does not receive sufficient oxygen and nutrients via the placenta, the pregnancy may result in fetal growth restriction, preterm birth, or miscarriage [1–4]. One cause of placental insufficiency is impaired flow of maternal blood to the placental intervillous space [5]. Placental disease or injury during pregnancy can impair blood flow, limiting oxygen and nutrient exchange with the fetus [6, 7]. The effects of disease often manifest as histopathological changes in the placental tissue, which generally cannot be studied until after delivery. While important for understanding placental dysfunction, term placental histopathology cannot offer information about the development of pathology or inform clinical decisions during pregnancy. Identification of placental dysfunction early in pregnancy with noninvasive imaging could be a valuable tool for assessing maternal and fetal risk [8].

Ultrasound is commonly used to evaluate pregnancy but does not provide adequate resolution for the identification of most placental pathology [9–12]. Placental pathology is often focal rather than homogeneous throughout the tissue [13–16], further hampering identification of pathology through the narrow ultrasound view. Recent work indicates that dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) can be a powerful tool for interrogating placenta health [17, 18]. DCE MRI uses a venously injected contrast agent to obtain functional images with high resolution. As the contrast bolus enters the imaging volume, tissue perfusion can be visualized and quantified. Identifying regions of high or low perfusion can offer insight into the physiological environment, and early identification of placental dysfunction could facilitate heightened surveillance of fetal well-being by ultrasound or placental function by, for example, assessment of soluble biomarkers. Gadolinium-based (Gd) contrast agents are most common for DCE MRI, and this approach has been used in animal model placenta studies [16, 19–21]. However, common Gd chelates cross the placenta into the fetus in detectable amounts [22], and there is uncertainty regarding the long-term consequences of Gd exposure in utero [23–26].

Instead, the current study used a ferumoxytol contrast agent. Ferumoxytol is a superparamagnetic iron oxide nanoparticle (SPION) used in the treatment of anemia in adults with renal insufficiency (Feraheme, AMAG Pharmaceuticals, Waltham, MA). Ferumoxytol has been used before as an off-label DCE MRI contrast agent, and is considered safe [27–29] by obstetricians to treat iron-deficiency anemia in pregnant women [30]. Previous studies demonstrate the feasibility of ferumoxytol imaging in a healthy rhesus macaque model [31–34]. and document its promising ability to quantify perfusion domains thought to represent placental cotyledons [35]. Cotyledons are the functional units of the placenta where maternal blood enters the intervillous space and bathes the fetal chorionic villi, allowing exchange of oxygen, nutrients, and wastes [36, 37]. The rhesus macaque placenta has many structural and functional similarities to that of humans, including a hemochorial villous structure segmented into cotyledons [36]. These similarities make the rhesus macaque pregnancy model an excellent option for study of potential applications in human maternal-fetal medicine. We hypothesize that ferumoxytol DCE MRI of the pregnant rhesus macaque can detect changes in placental structure and function associated with pathology by assessing intervillous perfusion throughout gestation. In this work, we present novel data matching perfusion domain maps generated with ferumoxytol DCE to histopathological analysis of cotyledons in term macaque placentae. Furthermore, we demonstrate trends in placenta structure associated with pathology throughout gestation.

Materials and methods

Care and use of macaques

This study was approved by the University of Wisconsin-Madison College of Letters and Sciences and Vice Chancellor Office for Research and Graduate Education Institutional Animal Care and Use Committee, protocols G005401, G005691, and G005263. Wisconsin National Primate Research Center (WNPRC) female rhesus macaques were housed with compatible males and monitored for breeding and menses. Date of conception was determined (±2 days) based on initiation of the menstrual cycle, observation of copulation and presence of ejaculate, and ultrasound measurements of fetus and gestational sac [38]. Full term in rhesus macaques is approximately 165 days [39]. Macaques were cared for by WNPRC staff as outlined in the Animal Welfare Act and the Guide for the Care and Use of Laboratory Animals.

Zika virus infection models

Zika virus (ZIKV)-inoculated animals from two separate experiments were recruited for this MRI study. All animals received Puerto Rican Zika virus/H.sapiens-tc/PUR/2015/PRVABC59_v3c2 (PR ZIKV) or mock inoculation with sterile saline but differed in inoculation method. A total of 14 animals were imaged for this study. A summary flow chart of their treatments is shown in Figure 1. Six animals received injections into the amniotic sac at gestational age (GA) day 55. Of these, two animals received 1 × 10⁴ PR ZIKV (n = 2), one received 1 × 10⁵ PR ZIKV (n = 1), and three controls received a saline injection (n = 3). The eight remaining animals received 10⁴ PR ZIKV by subcutaneous injection at ~GA45, followed by either no intervention (n = 1), intravenous infusions over 2–3 min of 50 mg/kg Zika-specific human immunoglobulin (n = 4) or nonspecific human immunoglobulin (n = 3) at 1 and 5 days post infection. Specific details about this latter study and fetal outcomes are reported in further detail elsewhere [40]. Briefly, although all dams experienced productive infection, there were no significant fetal anomalies noted in and no evidence of vertical transmission.

Magnetic resonance imaging

All animals were imaged up to three times, at GA65 ( Inline graphic d), GA100 ( d), and GA145 ( days), with 10 of the 14 receiving all three scans, and 13 of the 14 receiving the GA145 scan enabling the matching of MRI data to placental tissue pathology from delivery at GA155. Cesarean sections were done before term to ensure placenta recovery for histological analyses since some animals may deliver early and consume the placenta. Animals were food deprived 4–20 h prior to sedation for the imaging procedure. Sedation was completed by injection of up to 10 mg/kg ketamine followed by intubation and maintenance anesthesia by inhalation of a mixture of oxygen and 1.5% isoflurane. Imaging was conducted in the right-lateral position, and respiratory bellows were used to compensate for respiratory motion.

Magnetic resonance images were acquired on a clinical 3.0 T MRI system (Discovery MR750, GE Healthcare, Waukesha, WI) using a 32-channel torso coil (Neocoil, Pewaukee, WI). Four-dimensional (4D) DCE data sets were acquired before and during ferumoxytol infusion using a dynamic, 50% efficiency respiratory-gated T₁-weighted spoiled gradient echo product sequence (DISCO, TR = 4.8 ms, TE = 1.82 ms, 2.4 ms, spatial res. = 0.86 × 0.86 × 1.00 mm³, temporal resolution = 5.48 s, flip angle = 12°) [35]. Ferumoxytol was diluted 5:1 with saline and administered at 4 mg/kg body weight over a 20-s interval followed by a 20 ml saline flush at the same rate.

Calculation of perfusion domain maps

The placenta was segmented in Mimics (Materialise, NV) using time-averaged DCE images to visualize the full extent of contrast enhancement in the tissue (Figure 2A). Subsequently, the segmented DCE data was processed with customized in-house tools (Matlab, Mathworks, Natick, MA) similar to previous reports [19, 35]. As shown in Figure 2B, the average signal intensity of all placenta voxels is roughly sigmoidal. However, maximum voxel intensity and time of enhancement can vary widely across the placenta depending on proximity to blood inflow. Our algorithm used a per-voxel thresholding approach, calculating the contrast arrival time for each pixel as the time point when the signal intensity exceeded an empirically determined threshold of 22 times the standard deviation of the signal at initial time points, considered to be background noise without enhancement. The threshold was chosen by checking the intersection of threshold and average enhancement curve. Intersection at approximately 50% enhancement reliably produced the best results by rejecting noisy pixels but accepting inflow areas with proper sigmoid enhancement. The initial time points were determined using the enhancement inflection point of the entire placenta’s average enhancement. The resulting arrival time map is shown in Figure 2C. Finally, a watershed algorithm was applied to automatically segment perfusion domains, shown in Figure 2D. The total number of perfusion domains was calculated along with domain size (ml) and blood flow rate (ml/min) for each placenta. Blood flow to each perfusion domain was calculated using a linear fit of the cumulative total volume of enhanced voxels over time.

Image processing workflow. (A) Representative example of contrast inflow during a ferumoxytol DCE MRI scan in the rhesus macaque placenta at GA145 shown as maximum intensity projection (MIP) images at four time points relative to the start of the scan. (B) The signal intensity from DCE data averaged across the entire placenta with estimated contrast arrival time (enhancement inflection). After finding the average inflection, the standard deviation of signal in prior frames is used to estimate background noise per voxel. The threshold () is optimal for formation of the arrival time map (C), plotted with isovalues at 18, 45, 71, and 98 s (after inflection; shown as color guide on the right of panel C) to visualize the inflow and spreading of blood into the placenta. The perfusion domain map (D) shows the resulting segmentation of perfusion domains based on MRI signal, with a lower volume threshold of 0.5 cm³. Different colors represent distinct functional domains.

Inline graphic — Image processing workflow. (A) Representative example of contrast inflow during a ferumoxytol DCE MRI scan in the rhesus macaque placenta at GA145 shown as maximum intensity projection (MIP) images at four time points relative to the start of the scan. (B) The signal intensity from DCE data averaged across the entire placenta with estimated contrast arrival time (enhancement inflection). After finding the average inflection, the standard deviation of signal in prior frames is used to estimate background noise per voxel. The threshold () is optimal for formation of the arrival time map (C), plotted with isovalues at 18, 45, 71, and 98 s (after inflection; shown as color guide on the right of panel C) to visualize the inflow and spreading of blood into the placenta. The perfusion domain map (D) shows the resulting segmentation of perfusion domains based on MRI signal, with a lower volume threshold of 0.5 cm³. Different colors represent distinct functional domains.

Placental dissection

At GA155, macaques were intubated and sedated and the fetoplacental unit of each animal was collected via cesarean section.

The placenta was photographed and dissected. The photograph of the placenta was annotated electronically to identify the placental cotyledons, as determined visually upon inspection of the chorionic plate. The cotyledons were then dissected, and full-thickness sections (from chorionic plate to decidua) were collected. These sections were laid in tissue cassettes labeled to match the tissue biopsy with the annotated photograph. Tissues were fixed in 4% paraformaldehyde for 24 h before being placed in 70% EtOH until embedding in paraffin blocks. The paraffin embedded tissues were sectioned at 5 μm and stained with hematoxylin and eosin.

Histopathological analysis

Placental tissue was assessed by an experienced placental histopathologist (TKM) blinded to treatment and imaging data. Hematoxylin and eosin-stained cotyledon sections from all placentas in this study were evaluated for the presence of chronic histiocytic intervillositis (CHIV), placental infarctions (PI), and maternal decidual vasculitis (MDV) according to published criteria [39, 41]. Example images of pathology are shown in Figure 3. The presence or absence of CHIV, PI, MDV, and their combinations in each cotyledon was recorded for creating pathological rankings between the assessed placentas (Supplementary Table S1). To aid analysis between monkeys, an average pathology score per placenta was calculated by adding the total number of pathologies (0–3) in all placental cotyledons and dividing by the total number of cotyledons. This value provided a useful ranking of the relative degree of pathology between placentas. Three cotyledons from one animal (Animal 5 on Supplementary Table S1) were lost during processing; data on this tissue is not reported.

Examples of chronic histiocytic intervillositis (CHIV), infarctions, and leukocytoclastic vasculitis at the uteroplacental interface. (A) Normal chorionic villi near term compared with cases infected with ZIKV that had more areas of remote infarction (B) and patchy areas of CHIV involving floating villi (C) and beneath the subchorionic plate (D) that is unusual for TORCH infections. Compared with normal maternal decidual arteries (E), ZIKV-infected cases occasionally had leukocytoclastic vasculitis (F) with a mixture of infiltrating lymphocytes, neutrophils, plasma cells, and a few eosinophils (arrow).

Matching term tissue and MRI data

The annotated photos of the placentas were matched with the MRI perfusion domain map developed for the same placenta at the GA145 scan based on organ shape and location of large and small functional groups (Figure 4). One animal was not included in this comparison since it was not scanned at GA145. If a perfusion domain identified on MRI corresponded to multiple cotyledons, total volume was summed or distributed to the cotyledons based on their estimated size relative to one another. Once all perfusion domain data were matched to cotyledons, volume data were associated with the appropriate cotyledon pathological scoring as assessed by the pathologist (Supplementary Table S1). Flow rate data were not split according to matching since the flow rate is often not homogeneous within the perfusion domain.

Example of a perfusion domain map matched to fresh placental tissue. Good agreement is seen between the perfusion domain map at GA145 (left) and the photo of the chorionic plate of a term placenta collected at GA155 (right) of a particular animal in the same orientation. Perfusion domains on the left image are circled in red and labeled with red letters to match the cotyledons circled and labeled in red on the right image.

Statistical analysis: individual placenta comparisons

Only animals that received the GA145 MRI scan were included in this analysis (n = 13). After matching cotyledons with perfusion domains, volume values from pathological cotyledons were compared with cotyledons in the same animal without that pathology. Comparison between the pathological and nonpathological cotyledons was completed by two-way ANOVA, with factors of pathology and animal.

Statistical analysis: grouped animal comparisons

To test the impact of pathology on perfusion domain flow and volume, individual DCE MRI flow rates and volume values were compared between high and low pathology monkey placentas. Comparisons were made by binning animals into two groups using the total pathology score calculated from histopathological analyses. A term total average pathology score of ≥0.5 was defined to indicate high pathology placentas (n = 5), while <0.5 denoted low pathology placentas (n = 8). A pathology score of 0.5 was chosen because it indicates that, on average, half of the dissected cotyledons contained one of the three possible pathologies. The flow rate and volume values of the two bins were compared using a Wilcoxon rank sum test.

The average volume of matched cotyledons per monkey was also compared between high and low pathology cases using the Wilcoxon rank sum test. The use of matched cotyledon data allowed analysis of placenta cotyledon volume grouped by specific pathologies and their combinations. The pathology score cutoff for pathological placentas in the individual pathology analyses was based on the median individual pathology score value instead of 0.5 due to few monkeys being compared. Selecting the cutoff by median ensured that more than half of the cotyledons analyzed had that pathology score or lower.

Finally, regression analysis was performed to investigate the dependence of DCE MRI outputs on gestational age. Only monkeys scanned three times were included (n = 10) with data separated into high and low pathology groups according to the pathology score cutoff of 0.5. The relationship of perfusion domain number, volume, and total placental blood flow to gestational age was analyzed.

Results

Parameters from analysis of the ferumoxytol DCE-MRI data including number of perfusion domains, their volume, and blood flow at each gestational age, binned by pathology score, are summarized in Table 1. After matching perfusion domains with dissection cotyledon data, the low pathology bin had an error (mean Inline graphic sd) of 2.43.6 segmented domains. That is to say, on average, the algorithm overestimated the number of domains (as compared with the number observed with dissection at cesarean section) in low pathology placentas. Error for the high pathology bin was 0.02.1 segmented domains; thus, on average, the number of domains was correctly estimated. Matched domain data and pathology for all subjects are detailed in Supplementary Table S1. All ferumoxytol DCE MRI perfusion domain analysis throughout gestation is detailed in Supplementary Table S2.

Table 1.

DCE MRI results: total number of perfusion domains, total placental blood flow, and blood flow sorted by gestational age and binned by low [0, 0.5] or high [0.5, 1.5] pathology scores, where a bracket indicates that number is included in the range.

Gestational age (day) and pathology score	Average # perfusion domains	Total placental blood flow (ml/min)	Average perfusion domain blood flow (ml/min)
65, [0, 0.5), (n = 7)	7.7 3.8	22.9 6.9	3.6 1.1
65, [0.5, 1.5], (n = 4)	7.0 2.1	15.9 11.2	2.1 0.8
100, [0, 0.5), (n = 8)	14.8 3.7	50.6 13.3^^	3.5 0.9^*
100, [0.5,1.5], (n = 4)	12.5 1.5	27.3 7.3	2.2 0.7
145, [0, 0.5), (n = 8)	15.0 5.2	63.5 24.6	4.3 1.1
145, [0.5,1.5], (n = 5)	14.8 2.7	44.2 19.4	2.9 1.5

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Values are reported as mean ±standard deviation. Note that only monkeys scanned at all three time points were included in the binned statistical analysis over gestation. At term, the low pathology bin had a mean error of 2.38 Inline graphic 3.60 segmented domains. Mean error for the high pathology bin was 0.00 2.10 segmented domains. An asterisk (^*) indicates statistical evidence for a difference between pathology bins at the 0.05 level, while two asterisks indicate evidence for a difference at the 0.01 level.

Comparisons across gestation binned by pathology

The level of pathology varied substantially in both control and Zika virus-exposed animals, ranging from 0 to 1.4 (Supplementary Figure S1). There were no statistically significant differences in average pathology score among the control or Zika-exposed groups. For this reason, we did not compare MRI data from Zika with control (saline/placebo) pregnancies, but rather binned by pathology score to compare MRI data. To test if perfusion domain volume and flow were different in placentas with higher pathology, placentas were binned by pathology score and compared at all three scan time points. A statistically significant decrease in perfusion domain volume (Figure 5) at all gestational ages with pathology was measured (50% decrease in median volume at GA65, P = 0.015, 62% decrease at GA100, P < 0.01, and 49% decrease at GA145, P < 0.01). Individual perfusion domain flow comparisons (not shown) demonstrated a statistically significant decrease in flow with pathology at GA100 (33% decrease in median flow, P < 0.01) and GA145 (36% decrease, P < 0.01), but not GA65 (P = 0.42). See Supplementary Table S2 for individual perfusion domain flow and volume values.

Box-and-whisker plots illustrating the statistically significant decrease in perfusion domain volume with pathology between high and low pathology bins at all three gestational stages. Analysis revealed a 50% decrease in median volume at GA65, P = 0.015, 62% decrease at GA100, P < 0.01, and 49% decrease at GA145, P < 0.01. Domain volumes are separated into two bins by binning monkeys according to pathology score, with one plot per gestational age. A bracket indicates that number is included in the range.

Placental function with gestation

To test the dependence of total placental flow rate, perfusion domain volume, and number of perfusion domains on GA, linear regression analysis was performed. Figure 6 illustrates the statistically significant dependence of the total placental blood flow rate (Figure 6A) and number of cotyledons (Figure 6B) on gestational age. Flow, volume, and number all increase as the fetus grows: monkeys below the 0.5 pathology score cutoff exhibited strong correlation of perfusion domain volume with gestation (R² = 0.067, P < 0.01) and number of perfusion domains with gestation (R² = 0.268, P = 0.028). High pathology placentas had weaker dependence on volume (R² = 0.040, P = 0.020) but stronger response to the number of cotyledons (R² = 0.614, P < 0.01). Total blood flow rate increased with gestation for both the low pathology bin (R² = 0.403, P = <0.01) and the high pathology bin (R² = 0.369, P = 0.03).

(A) Regression analysis showing the statistically significant increase in total blood flow with gestation for both low pathology bin (R² = 0.403, P = <0.01) and the high pathology bin (R² = 0.369, P = 0.03) monkeys (binned using the 0.5 pathology score cutoff). (B) Statistically significant increase in number of perfusion domain with gestational data: HP: R² = 0.614, P = 2.58E-3, LP: R² = 0.268, P = 0.028. The dashed lines show trends per subject. The points at GA155 are cotyledons counted during dissection for comparison with the final scan at GA145. In both plots, saline injected controls are represented by triangles.

Term matched perfusion domain volumes by specific pathology

To investigate whether specific pathologies led to detectable decreases in average cotyledon volume, matched cotyledons were binned by pathology. Figure 7 shows statistically significant evidence for a decrease in cotyledon volume with pathology (P = 0.011). When comparisons were split by specific pathology, only PI showed evidence for a similar decrease (P = 0.008). All other matched comparisons by pathology at term were nonsignificant.

Grouped perfusion domain volume (mL) analysis sorted by specific pathology. Perfusion domain volume values at GA145 were averaged per animal and compared in animals with a pathology against those without. Analysis was completed for each pathology separately: Total pathology score, CHIV, PI, MDV, CHIV and PI, CHIV and MDV, and PI and MDV. Total pathology score is denoted as pathological when above 0.5. All other pathologies have cutoffs based on median volume value. P-values are specific to the pathology listed.

Finally, we assessed whether pathological cotyledon volume was different from healthy cotyledons in the same placenta. No statistically significant evidence was found in any of the individual placentas for any of the pathologies or their combinations when factors of animal and pathology score were used.

Discussion

In this study, we used dynamic contrast-enhanced MRI with ferumoxytol to noninvasively track rhesus macaque placental structure and function at multiple gestational time points and correlated these measures with pathology in the late third trimester placenta. Zika virus infection produced a varied level of pathology between cases, but our use of a quantitative pathology score allowed comparisons by severity of effect on the placenta. MRI enabled the assessment of volume and blood flow rate of placental perfusion domains, both of which increased through gestation. The number of perfusion domains from late gestation MRI (GA145) analysis favorably matched with the number of cotyledons confirmed in late preterm dissection (GA155). It was notable that a statistically significant decrease in rhesus macaque placental perfusion domain volume associated with overall pathology identified at the end of gestation is detectable via MRI as early as day 65 of gestation. Such an early difference post infection suggests that physiological or histopathological changes that occur early in gestation persist through gestation to term and can alter future development of histopathology in the perfusion domains. The decrease in blood flow to cotyledons was found to be significant only at day 100 and 145, suggesting changes in cotyledon volume which may occur before the effects on flow are manifest. When the analysis was limited to specific pathologies, only placental infarctions demonstrated a statistically significant association with reduced perfusion domain volume. These results show that the presence of infarctions contribute to factors which alter cotyledon volume in the placenta.

Imaging-based assessment of perfusion domains allows the tracking of placental functional units in vivo through gestation, thereby providing novel insights into placental development and health. Values of cotyledon volume and number of cotyledons are similar to published data using Gd DCE MRI (Table 1), giving confidence in the reproducibility of this approach regardless of contrast agent used [16, 19, 35, 42]. Our perfusion domain flow rate calculation used a simple linear fit of enhanced voxels over time in order to avoid uncertainties introduced by estimation of spiral artery location and contrast wavefront surface area. Our calculated total placenta flow rates are significantly smaller than reported values using the alternative fitting method (controls: 63.5 Inline graphic 24.6 mL/min at GA145 versus 559190 mL/min at GA135) [43], but match 4D flow MRI estimates of macaque uterine blood supply [32]. Calculation of placental flow is highly model dependent, affected by both segmentation and flow fitting method. Differences in pulse sequence and contrast agent may also contribute to the difference in flow rate estimate. Further study is needed.

This pilot study provides insight into the capabilities of DCE MRI applied to the identification of placental pathology. Many factors affect placental function and development, such as diabetes, obesity, and fetal sex [44, 45]. Although the modest sample size in the current study did not allow assessment of these factors, we provided evidence that pathology may be linked to cotyledon blood flow and volume. To be clinically useful, additional data are needed to define the range of normal average cotyledon flow and volume throughout gestation. Ultimately, extending similar observations to the human placenta will be needed to confirm the utility of these imaging approaches for potentially considering development of future therapeutic interventions.

To our knowledge, this is the first study to report the increase in cotyledon number and volume through gestation. By allowing noninvasive characterization of blood flow, ferumoxytol DCE MRI may be useful in future investigations of the physiology underpinning placental development and pathology. One insight provided by this study is that the number of perfusion domains (cotyledons) increased in all placentas between GA65 and GA100, but then was generally unchanged between GA100 and GA145. One possibility is that this represents morphological development of the placenta in relation to the architecture of perfusion and growth of cotyledons that have an optimal perfusion environment. Further studies with additional imaging time points may provide some insight to this question. We have previously shown that there was no impact of ferumoxytol imaging on placental or fetal histopathology or fetal growth [31], or evidence of ferumoxytol accumulation in fetal tissues as detected by MRI [31, 33] or by histochemical analyses or fetal iron content [31], supporting its potential for human use. One limitation of our experiment was the use of two different infection cohorts, amniotic vs subcutaneous injection. This may have influenced outcomes; however, evaluating the actual pathology in each cotyledon provides insight into the mechanisms of disease regardless of source.

Conclusions

Our study provides evidence that pathology caused by early gestation infection decreases cotyledon blood flow and volume in the rhesus macaque placenta. Currently, MRI in pregnancy is reserved for clinical indications where the benefit outweighs the possible risks (e.g., diagnosis of appendicitis, delivery planning for certain fetal anomalies). Ferumoxytol DCE MRI holds great potential and may be useful to noninvasively identify the effects of placental pathologies before irreparable damage has occurred to placenta, fetus, or mother. This assessment may provide additional clinical information to assess the risk to the pregnancy or may determine need for further intervention or management through additional antenatal monitoring or delivery of the fetus. Most pregnancies require supplemental iron regardless of anemia status; since ferumoxytol is already used as a large dose iron supplement to treat anemia during human pregnancy and as an off-label MRI contrast agent, future transition of this work to the maternal–fetal interface in human pregnancy may be possible.

Supplementary Material

Supplementary_Fig_1_BIOLRE-2022-0217R1_ioac168

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Supplementary_table_1_SMN_DPS_ioac168

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Supplementary_table_2_SMN_DPS_ioac168

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Acknowledgments

We thank AMAG Pharmaceuticals for providing ferumoxytol used in our imaging procedures, and Logan T. Keding for preparing the graphical abstract. We also thank the Wisconsin National Primate Research Center (WNPRC) Veterinary, Scientific Protocol Implementation, and Animal Services staff for providing animal care, and assisting in procedures including breeding, pregnancy monitoring, and sample collection.

Conflicts of interest. The authors have declared that no conflict of interest exists.

Footnotes

^† Grant Support: The work was supported by the National Institutes of Health Grants U01 HD087216 (to O.W. and D.M.S.), R21 AI129308 to (T.G.G.), and R01 HD103443 (to T.G.G., O.W., and D.M.S.), supplement to R01 Al116382 (to D.H.O.); Endocrinology-Reproductive Physiology Training Grant T32 HD041921 (to S.M.N.) and P51 OD011106 (to the W.N.P.R.C.). The content of this manuscript is solely the responsibility of the authors and does not represent the official views of the NIH. Thank you to GE Healthcare who provides research support to the University of Wisconsin-Madison and AMAG Pharmaceuticals for providing ferumoxytol for this study. These funding sources had no involvement in study design; collection, analysis, or interpretation of data; writing of the report; or in the decision to submit this research for publication.

Contributor Information

Daniel P Seiter, Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.

Sydney M Nguyen, Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Obstetrics & Gynecology, University of Wisconsin-Madison, Madison, WI, USA.

Terry K Morgan, Department of Pathology, Oregon Health & Science University, Portland, OR, USA.

Lu Mao, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.

Dawn M Dudley, Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA.

David H O’connor, Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA.

Megan E Murphy, Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Obstetrics & Gynecology, University of Wisconsin-Madison, Madison, WI, USA.

Kai D Ludwig, Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.

Ruiming Chen, Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.

Archana Dhyani, Department of Computer Science, University of Wisconsin-Madison, Madison, WI, USA.

Ante Zhu, Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.

Michele L Schotzko, Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA.

Kevin G Brunner, Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA.

Dinesh M Shah, Department of Obstetrics & Gynecology, University of Wisconsin-Madison, Madison, WI, USA.

Kevin M Johnson, Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.

Thaddeus G Golos, Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Obstetrics & Gynecology, University of Wisconsin-Madison, Madison, WI, USA.

Oliver Wieben, Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.

Authors’ contributions

T.G.G., O.W., and D.M.S. contributed to the conception and design of the study. S.M.N., D.P.S., G.J.W., T.K.M., M.E.M., K.D.L., R.C., A.D., A.Z. T.G.G., O.W., and K.M.J. collected and analyzed the data. S.M.N., D.P.S., T.G.G., D.M.S., and O.W. drafted the manuscript. M.L.S. and K.G.B. are in the Wisconsin National Primate Research Center Animal Services Division who coordinated and performed the animal procedures. L.M. and D.P.S. conducted statistical analysis of the data. T.G.G., O.W., D.M.S., D.M.D., and D.H.O. obtained funding for the study.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

References

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6. Fox H. The histopathology of placental insufficiency. J Clin Pathol 1976; 29:1–8. [PMC free article] [PubMed] [Google Scholar]
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10. Kofinas AD, Penry M, Nelson LH, Meis PJ, Swain M. Uterine and umbilical artery flow velocity waveform analysis in pregnancies complicated by chronic hypertension or preeclampsia. South Med J 1990; 83:150–155. [DOI] [PubMed] [Google Scholar]
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary_Fig_1_BIOLRE-2022-0217R1_ioac168

Click here for additional data file.^{(33.1KB, docx)}

Supplementary_table_1_SMN_DPS_ioac168

Click here for additional data file.^{(71.2KB, docx)}

Supplementary_table_2_SMN_DPS_ioac168

Click here for additional data file.^{(79.4KB, docx)}

Data Availability Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

[ref1] 1. Alexander BT. Placental insufficiency leads to development of hypertension in growth-restricted offspring. Hypertension 2003; 41:457–462. [DOI] [PubMed] [Google Scholar]

[ref2] 2. Brosens I, Pijnenborg R, Vercruysse L, Romero R. The ‘great Obstetrical Syndromes’ are associated with disorders of deep placentation. Am J Obstet Gynecol 2011; 204:193–201. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref3] 3. Krishna U, Bhalerao S. Placental insufficiency and fetal growth restriction. J Obstet Gynecol India 2011; 61:505–511. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref4] 4. Roberts DJ, Post MD. The placenta in pre-eclampsia and intrauterine growth restriction. J Clin Pathol 2008; 61:1254–1260. [DOI] [PubMed] [Google Scholar]

[ref5] 5. Naeye RL. Pregnancy hypertension, placental evidences of low uteroplacental blood flow, and spontaneous premature delivery. Hum Pathol 1989; 20:441–444. [DOI] [PubMed] [Google Scholar]

[ref6] 6. Fox H. The histopathology of placental insufficiency. J Clin Pathol 1976; 29:1–8. [PMC free article] [PubMed] [Google Scholar]

[ref7] 7. Roberts DJ. Placental pathology, a survival guide. Arch Pathol Lab Med 2008; 132:641–651. [DOI] [PubMed] [Google Scholar]

[ref8] 8. Guttmacher AE, Spong CY. The human placenta project: it’s time for real time. Am J Obstet Gynecol 2015; 213:S3–S5. [DOI] [PubMed] [Google Scholar]

[ref9] 9. Newnham JP, O’dea MR, Reid KP, Diepeveen DA. Doppler flow velocity waveform analysis in high risk pregnancies: a randomized controlled trial. BJOG An Int J Obstet Gynaecol 1991; 98:956–963. [DOI] [PubMed] [Google Scholar]

[ref10] 10. Kofinas AD, Penry M, Nelson LH, Meis PJ, Swain M. Uterine and umbilical artery flow velocity waveform analysis in pregnancies complicated by chronic hypertension or preeclampsia. South Med J 1990; 83:150–155. [DOI] [PubMed] [Google Scholar]

[ref11] 11. Fadl S, Moshiri M, Fligner CL, Katz DS, Dighe M. Placental imaging: normal appearance with review of pathologic findings1. Radiographics 2017; 37:979–998. [DOI] [PubMed] [Google Scholar]

[ref12] 12. Hashem LB, Salem DS, Hamed ST, Hussein AM. Role of MRI versus ultras ound in the assessment of placental abnormalities and diseases. Egypt J Radiol Nucl Med 2016; 47:641–658. [Google Scholar]

[ref13] 13. Vázquez-Boland JA, Krypotou E, Scortti M. Listeria placental infection. MBio 2017; 8:8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref14] 14. Tabata T, Petitt M, Fang-Hoover J, Zydek M, Pereira L. Persistent cytomegalovirus infection in amniotic membranes of the human placenta. Am J Pathol 2016; 186:2970–2986. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref15] 15. McDonagh S, Maidji E, Ma W, Chang HT, Fisher S, Pereira L. Viral and bacterial pathogens at the maternal-fetal interface. J Infect Dis 2004; 190:826–834. [DOI] [PubMed] [Google Scholar]

[ref16] 16. Hirsch AJ, Roberts VHJ, Grigsby PL, Haese N, Schabel MC, Wang X, Lo JO, Liu Z, Kroenke CD, Smith JL, Kelleher M, Broeckel R et al. Zika virus infection in pregnant rhesus macaques causes placental dysfunction and immunopathology. Nat Commun 2018; 9:263. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref17] 17. Millischer AE, Deloison B, Silvera S, Ville Y, Boddaert N, Balvay D, Siauve N, Cuenod CA, Ttsatsaris V, Sentilhes L, Salomon LJ. Dynamic contrast enhanced MRI of the placenta: a tool for prenatal diagnosis of placenta accreta? Placenta 2017; 53:40–47. [DOI] [PubMed] [Google Scholar]

[ref18] 18. Deloison B, Arthuis C, Benchimol G, Balvay D, Bussieres L, Millischer A-EE, Grévent D, Butor C, Chalouhi G, Mahallati H, Hélénon O, Tavitian B et al. Human placental perfusion measured using dynamic contrast enhancement MRI. PLoS One 2021; 16:e0256769. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref19] 19. Frias AE, Schabel MC, Roberts VHJ, Tudorica A, Grigsby PL, Oh KY, Kroenke CD. Using dynamic contrast-enhanced MRI to quantitatively characterize maternal vascular organization in the primate placenta. Magn Reson Med 2015; 73:1570–1578. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref20] 20. Chalouhi GE, Deloison B, Siauve N, Aimot S, Balvay D, Cuenod CA, Ville Y, Clément O, Salomon LJ. Dynamic contrast-enhanced magnetic resonance imaging: definitive imaging of placental function? Semin Fetal Neonatal Med 2011; 16:22–28. [DOI] [PubMed] [Google Scholar]

[ref21] 21. Deloison B, Siauve N, Aimot S, Balvay D, Thiam R, Cuenod CA, Ville Y, Clement O, Salomon LJ. SPIO-enhanced magnetic resonance imaging study of placental perfusion in a rat model of intrauterine growth restriction. BJOG An Int J Obstet Gynaecol 2012; 119:626–633. [DOI] [PubMed] [Google Scholar]

[ref22] 22. Prola-Netto J, Woods M, Roberts VHJ, Sullivan EL, Miller CA, Frias AE, Oh KY. Gadolinium chelate safety in pregnancy: barely detectable gadolinium levels in the juvenile nonhuman primate after in utero exposure. Radiology 2018; 286:122–128. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref23] 23. Rogosnitzky M, Branch S. Gadolinium-based contrast agent toxicity: a review of known and proposed mechanisms. Biometals 2016; 29:365–376. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref24] 24. Oh KY, Roberts VHJ, Schabel MC, Grove KL, Woods M, Frias AE. Gadolinium chelate contrast material in pregnancy: fetal biodistribution in the nonhuman primate. Radiology 2015; 276:110–118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref25] 25. Ray JG, Vermeulen MJ, Bharatha A, Montanera WJ, Park AL. Association between MRI exposure during pregnancy and fetal and childhood outcomes. JAMA 2016; 316:952–961. [DOI] [PubMed] [Google Scholar]

[ref26] 26. Siauve N, Chalouhi GE, Deloison B, Alison M, Clement O, Ville Y, Salomon LJ. Functional imaging of the human placenta with magnetic resonance. Am J Obstet Gynecol 2015; 213:S103–S114. [DOI] [PubMed] [Google Scholar]

[ref27] 27. Ahmad F, Treanor L, Mcgrath TA, Walker D, Mcinnes MDF, Schieda N. Safety of off-label use of ferumoxtyol as a contrast agent for MRI: a systematic review and meta-analysis of adverse events. J Magn Reson Imaging 2020; 53:840–858. [DOI] [PubMed] [Google Scholar]

[ref28] 28. Vasanawala SS, Nguyen KL, Hope MD, Bridges MD, Hope TA, Reeder SB, Bashir MR. Safety and technique of ferumoxytol administration for MRI. Magn Reson Med 2016; 75:2107–2111. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref29] 29. Toth GB, Varallyay CG, Horvath A, Bashir MR, Choyke PL, Daldrup-Link HE, Dosa E, Finn JP, Gahramanov S, Harisinghani M, Macdougall I, Neuwelt A et al. Current and potential imaging applications of ferumoxytol for magnetic resonance imaging. Kidney Int 2017; 92:47–66. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref30] 30. Gerb J, Strauss W, Derman R, Short V, Mendelson B, Bahrain H, Auerbach M. Ferumoxytol for the treatment of iron deficiency and iron-deficiency anemia of pregnancy. Ther Adv Hematol 2021; 12:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref31] 31. Nguyen SM, Wiepz GJ, Schotzko M, Simmons HA, Mejia A, Ludwig KD, Zhu A, Brunner K, Hernando D, Reeder SB, Wieben O, Johnson K et al. Impact of ferumoxytol magnetic resonance imaging on the rhesus macaque maternal-fetal interface†. Biol Reprod 2020; 102:434–444. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref32] 32. Macdonald JA, Corrado PA, Nguyen SM, Johnson KM, Francois CJ, Magness RR, Shah DM, Golos TG, Wieben O. Uteroplacental and fetal 4D flow MRI in the pregnant rhesus macaque. J Magn Reson Imaging 2019; 49:534–545. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref33] 33. Zhu A, Reeder SB, Johnson KM, Nguyen SM, Golos TG, Shimakawa A, Muehler MR, Francois CJ, Bird IM, Fain SB, Shah DM, Wieben O, et al. Evaluation of a motion-robust 2D chemical shift-encoded technique for R2^* and field map quantification in ferumoxytol-enhanced MRI of the placenta in pregnant rhesus macaques. 2020; 51:580–592. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref34] 34. Zhu A, Reeder SB, Johnson KM, Nguyen SM, Fain SB, Bird IM, Golos TG, Wieben O, Shah DM, Hernando D. Magn Reson Imaging 2019. [DOI] [PMC free article] [PubMed]

[ref35] 35. Ludwig KD, Fain SB, Nguyen SM, Golos TG, Reeder SB, Bird IM, Shah DM, Wieben OE, Johnson KM. Perfusion of the placenta assessed using arterial spin labeling and ferumoxytol dynamic contrast enhanced magnetic resonance imaging in the rhesus macaque. Magn Reson Med 2019; 81:1964–1978. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref36] 36. de Rijk EPCT, Van Esch E. The macaque placenta—a mini-review. Toxicol Pathol 2008; 36:108S–118S. [Google Scholar]

[ref37] 37. Stouffer RL, Woodruff TK. Nonhuman primates: a vital model for basic and applied research on female reproduction, prenatal development, and women’s health. ILAR J 2017; 58:281–294. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref38] 38. Tarantal AF. Ultrasound imaging in rhesus (Macaca mulatta) and longtailed (Macaca fascicularis) macaques: reproductive and research applications. In: Wolfe-Coote S (ed.), The Laboratory Primate. London: Elsevier; 2005: 317–352. [Google Scholar]

[ref39] 39. Barry PA, Lockridge KM, Salamat S, Tinling SP, Yue Y, Zhou SS, Gospe SM, Britt WJ, Tarantal AF. Nonhuman primate models of intrauterine cytomegalovirus infection. ILAR J 2006; 47:49–64. [DOI] [PubMed] [Google Scholar]

[ref40] 40. Dudley DM, Koenig MR, Stewart LM, Semler MR, Newman CM, Shepherd PM, Yamamoto K, Breitbach ME, Schotzko M, Kohn S, Antony KM, Qiu H et al. Human immune globulin treatment controls Zika viremia in pregnant rhesus macaques. PLoS One 2022; 17:e0266664. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Ferumoxytol dynamic contrast enhanced magnetic resonance imaging identifies altered placental cotyledon perfusion in rhesus macaques†

Daniel P Seiter

Sydney M Nguyen

Terry K Morgan

Lu Mao

Dawn M Dudley

David H O’connor

Megan E Murphy

Kai D Ludwig

Ruiming Chen

Archana Dhyani

Ante Zhu

Michele L Schotzko

Kevin G Brunner

Dinesh M Shah

Kevin M Johnson

Thaddeus G Golos

Oliver Wieben