Longitudinal characterization of local perfusion of the rat placenta using contrast-enhanced ultrasound imaging

Abstract

The placenta performs many physiological functions critical for development. Insufficient placental perfusion, due to improper vascular remodelling, has been linked to many pregnancy-related diseases. To study longitudinal in vivo placental perfusion, we have implemented a pixel-wise time–intensity curve (TIC) analysis of contrast-enhanced ultrasound (CEUS) images. CEUS images were acquired of pregnant Sprague Dawley rats after bolus injections of gas-filled microbubble contrast agents. Conventionally, perfusion can be quantified using a TIC of contrast enhancement in an averaged region of interest. However, the placenta has a complex structure and flow profile, which is insufficiently described using the conventional technique. In this work, we apply curve fitting in each pixel of the CEUS image series in order to quantify haemodynamic parameters in the placenta and surrounding tissue. The methods quantified an increase in mean placental blood volume and relative blood flow from gestational day (GD) 14 to GD18, while the mean transit time of the microbubbles decreased, demonstrating an overall rise in placental perfusion during gestation. The variance of all three parameters increased during gestation, showing that regional differences in perfusion are observable using the pixel-wise TIC approach. Additionally, the high-resolution parametric images show distinct regions of high blood flow developing during late gestation. The developed methods could be applied to assess placental vascular remodelling during the treatment of the pathologies of pregnancy.

1. Introduction

The placenta is a complex vascular network that performs a wide range of physiological functions critical for development, the most basic of which is the exchange of gas and nutrients. Maternal blood from the uterine arteries is supplied via the spiral arteries to the intervillous space, a large lake of blood in the placenta where materno-fetal exchange occurs [1]. During normal placental development, loss of smooth muscle cells in the walls and elastic lamina of spiral arteries causes the vessels to dilate, increasing the delivery of maternal blood through a low-pressure, low-velocity placental bed. In the intervillous space, blood flows over the placental villi with little or no impedance, allowing for the exchange of oxygen and nutrients between maternal and fetal circulations [2,3]. Improper spiral artery remodelling contributes to abnormal placental development and reduced uteroplacental blood flow, which has been linked to several complications, including preeclampsia and intrauterine growth restriction (IUGR), two major causes of maternal and fetal morbidity and mortality [2,4,5].

Doppler ultrasound is used to assess uteroplacental blood flow in high-risk pregnancies [6]. A major limitation to standard Doppler ultrasound is that only blood flow approximately parallel to the transducer can be accurately measured [7]. Because of the size and complex shape of spiral arteries, unknown flow direction in the intervillous space and Doppler waveform interference from the maternal cardiovascular system, Doppler ultrasound cannot accurately measure uteroplacental perfusion. Additionally, Doppler ultrasound cannot measure flow in microcirculation because of the overlap in velocities of tissue motion and speed of blood flow through tissue. A non-invasive method to directly evaluate placental perfusion could provide critical insight into the pathogenesis of pregnancy-related diseases and aid in the evaluation of potential treatments.

Contrast-enhanced ultrasound (CEUS) is an imaging technique providing high contrast, high temporal resolution images of flow in vascularized tissues through the detection of microbubble contrast agents. Microbubbles (MBs), which are typically composed of a phospholipid shell encapsulating a low-solubility gas (i.e. perfluorocarbon), are approved for clinical use for specific applications [8,9] and have been used for perfusion imaging of disease [10–13]. Owing to their large compressibility, MBs oscillate nonlinearly in response to an ultrasound field; this can be isolated from the mostly linear tissue signal to give information on flow and vascularity [14,15].

Haemodynamic parameters can be quantified from CEUS images by using a theoretical model to fit variations in CEUS signal intensity over time, generating a time–intensity curve (TIC) [16,17]. As CEUS imaging suffers from noise due to speckle, motion and fluctuations in MB concentration, TIC analysis is typically performed by averaging the signal in a region of interest (ROI) [10,16,18]. The inherent limitations of this approach include the inability to detect spatial heterogeneity. Because of the complex structure of the placenta and the regional differences in blood flow across the placental bed, TIC analysis using an ROI-based approach provides an incomplete characterization of placental perfusion.

Recently, pixel-wise TIC analysis has been implemented to analyse local perfusion kinetics in several preclinical and clinical applications. Mischi et al. [19] developed a pixel-wise spatio-temporal analysis to quantify local dispersion kinetics related to tumour angiogenesis in the human prostate. Kogan et al. [20] investigated the effects of MB infusion rate and transducer orientation on perfusion measurements from a mono-exponential TIC fit in the rat kidney. Rognin et al. [17] used pixel-wise parametric imaging to characterize focal liver lesions in a clinical setting. Their analysis compares the instantaneous CEUS signal amplitude in each pixel with an averaged reference signal to generate a parametric image of dynamic vascular patterns indicative of malignant or benign tissue. Similarly, Ta et al. [18] used linear discriminant analysis to semi-automatically classify mammary tumours in a rat model from pixel-wise TIC perfusion parameters. As far as we know, none of these methods have been applied to CEUS imaging of the placenta. Using an ROI approach, both Arthuis et al. [21] and Zhou et al. [22] showed CEUS imaging for the safe, non-invasive monitoring of placental perfusion in a rat model.

Here, we apply a pixel-wise TIC analysis to a lognormal function to quantify haemodynamic parameters of placental perfusion in the rat placenta. The chosen lognormal function takes into consideration the complex architecture of the microvasculature [16,23]. Peak enhancement (PE), proportional to the local concentration of MBs and therefore blood volume, mean transit time (MTT), defined as the first moment of the lognormal distribution function, and relative blood flow (RBF) rate were calculated from CEUS images acquired longitudinally through a period of gestation. Furthermore, the parameters calculated in each pixel were used to generate high-resolution parametric images in order to quantify regional differences in placental blood flow.

2. Material and methods

2.1. Image acquisition

Imaging was performed using a Vevo 2100 ultrasound system (FUJIFILM, VisualSonics, Toronto, Ontario, Canada) with an LZ-250 transducer (256 elements, 13–24 MHz broadband frequency, 20 MHz centre frequency). A timed pregnant Sprague Dawley rat (n = 1) from a commercial vendor (Charles River Laboratories, Boston, MA) was anaesthetized with 3% isoflurane on gestational day (GD) 14, and the rat abdominal hair was removed with a depilatory cream. A tail vein catheter (30 µl total volume) was placed and secured with medical tape, and the animal was transferred to a heated physiological platform (FUJIFILM, VisualSonics) for imaging. Heart rate, respiration rate and body temperature were maintained throughout the imaging session.

B-mode ultrasound was used to locate one placenta for CEUS imaging. The selection criteria for imaging were a placenta with well-defined edges on the B-mode US images as well as a location low in the abdominal cavity to minimize motion due to respiration. The midline of the placenta was determined using the origin of the umbilical cord, visualized using colour Doppler ultrasound. To maximize the achievable frame rate, the width and depth of the CEUS imaging window were minimized (figure 1).

Figure 1.

(a) B-mode US image of the placental environment on GD 14. (b) After microbubble administration, CEUS signal can be identified in the spiral arteries (smallest ROI, in orange online), and central arterial canal (T-shaped ROI, in green online) of the placenta (kidney-shaped ROI, in blue online). (c) The CEUS image overlay shows the frame of peak contrast signal in the placenta (p) when microbubbles have filled the intervillous space. No CEUS signal was detected in the fetus (f). Scale bars indicate 3 mm. (Online version in colour.)

Vevo Target-Ready Micromarkers (FUJIFILM, VisualSonics) were used for CEUS imaging. MBs were conjugated with biotinylated l-arginine-glycine-glutamic acid-serine (RGES), an isotype control for targeted imaging to prevent MB adhesion to extracellular matrix proteins. A full video loop of nonlinear contrast images was acquired prior to MB injection to obtain a baseline measurement of background tissue structures. A 500 µl bolus of MBs was manually administered through the tail vein catheter followed by a 50 µl saline flush while simultaneously recording nonlinear CEUS images. CEUS video loops were saved continuously over a 10 min period after injection. Imaging was repeated every other day until GD18.

2.2. Pre-processing

The raw data files were exported from the Vevo 2100 for all data processing. Previous work has shown that, under certain conditions, video data can be used in place of raw radiofrequency (RF) data to generate perfusion parameters. To meet these conditions, the dynamic range of the log compression was set to be at least 45 dB to avoid oversaturation of the CEUS data and the video data were linearized to undo the log compression and compensate for nonlinear palette rendering [24]. We applied three pre-processing steps to each pixel: log compression of the raw data, low-pass spatial filtering and linearization and interpolation (figure 2). The definitions of all variables are summarized in table 1.

Figure 2.

Data processing sequence for the pixel-wise TIC analysis of placental perfusion. (Online version in colour.)

Table 1.

A list of the abbreviations used with explanations.

symbol	parameter	explanation
QL	quantized level	the log-compressed grey-level value used to export the raw RF data
EP	relative echo power	linearization of the QL signal to undo log compression and nonlinear palette rendering
V	echo amplitude	echo amplitude of the raw CEUS signal
I	intensity	intensity based on the lognormal fit of the raw CEUS signal over time
O	intensity offset	initial offset of the time–intensity curve from the start of image acquisition until the arrival of MBs; fit parameter for TIC analysis
AUC	area under the curve	area under the curve of the linear CEUS signal from t = arrival of MBs until the end of image acquisition; fit parameter for TIC analysis
μ	mean	mean of the natural log of time from the arrival of MBs in the imaging field until the end of image acquisition; fit parameter for TIC analysis
σ	standard deviation	standard deviation of the natural log of time from arrival of MBs in the imaging field until the end of image acquisition; fit parameter for TIC analysis
PE	peak enhancement	maximum value of the intensity of the fit TIC; representative of local MB concentration and relative blood volume
MTT	mean transit time	the mean time taken for MBs to pass through an ROI; mathematically, it is the first moment of the TIC
RBF	relative blood flow	mathematically, it is equal to PE/MTT or the relative blood volume over time

2.2.1. Logarithmic compression

The raw video data were first log compressed at a dynamic range of 45 dB and quantized as 8-bit values:

$$\mathrm{QL}(V)=255\times \frac{20\hspace{0.17em}\mathrm{dB}}{45\hspace{0.17em}\mathrm{dB}}\times \mathrm{lo}{\mathrm{g}}_{10}\left(\frac{V}{V_{\max }}{10}^{45\hspace{0.17em}\mathrm{dB}/20\hspace{0.17em}\mathrm{dB}}\right),$$

where V is the echo amplitude, and V_max is the maximum amplitude of the raw data, which was set to 2¹⁵ − 1 (the maximum positive amplitude of a signed 16-bit integer) [24]. Each frame of the log-compressed video data was then stored as a greyscale BMP image.

2.2.2. Low-pass spatial filtering

A first-order spatial low-pass Butterworth filter was applied to each frame of the image stack to remove speckle noise and the nonlinear signal from local tissue structures. Filter cut-off frequencies of one-half, one-quarter, one-eighth and one-sixteenth the sampling frequency were tested (data not shown). The one-sixteenth cut-off frequency used was chosen based on the minimization of the root-mean-squared error of the TIC fit while maximizing the mean of the haemodynamic parameters in the placenta.

2.2.3. Linearization and interpolation

The filtered data were then linearized to undo the 45 dB log compression implemented in step 1 and derive an echo-power signal with an amplitude proportional to the local MB concentration. Mathematically, this operation has the form [24]:

$$\mathrm{EP}(\mathrm{QL})={(V_{\max }\times {10}^{((\mathrm{QL}/255)-1)\times (45\mathrm{dB}/20\mathrm{dB})})}^2.$$

Since the image acquisition system has a limited buffer for capturing data, small gaps in the data occurred during the time the system took to save one video loop and start collecting the next. To compensate for this, the time missed was found between the start and end of sequential video loops and converted into the number of missing frames. Then, a linear interpolation of the length of the number of missed frames was calculated for each pixel between the last pixel value of the first video loop and the first pixel value of the next loop.

2.3. Perfusion analysis

TIC fitting was applied in a nonlinear least-squares manner to find the best fit of the lognormal function:

$$I(t)=O+\frac{\mathrm{AUC}}{\sqrt{2\pi }\times \sigma \times t}{e}^{(-{(\ln (t)-\mu )}^2/2{\sigma }^2)},$$

where I(t) is the CEUS signal intensity, as a function of time, and O is the baseline CEUS signal prior to MB arrival [16]. The area under the curve (AUC) is defined from the arrival of MB until the end of the video loop and µ and σ are the mean and standard deviation of the natural log of time. O, AUC, µ, and σ are all fit parameters. The lognormal function was chosen for our TIC analysis because of its consideration of the complex microvascular networks present in the placenta [16]. To limit the influence of recirculating MBs on the TIC fit, the temporal window was limited from the time of injection to the decline in CEUS signal after reaching PE. Figure 3 shows a representative TIC fit of the lognormal function to raw CEUS data.

Figure 3.

Example TIC fit of the lognormal function (blue line) to the linearized CEUS signal intensity. Intensity values (arb. units) have been normalized for display. (Online version in colour.)

The calculated TIC was then used to generate haemodynamic parameters in each pixel of the CEUS image. The PE, defined as the maximum amplitude of the TIC, is proportional to the concentration of local MBs and indicative of blood volume. MTT is defined as the first moment of the lognormal function [16,23].

Using these two parameters, the RBF rate was calculated as PE/MTT [16]. The parameters calculated in each pixel were then used to generate parametric images which were overlaid on the B-mode ultrasound images of anatomy. Additionally, the placenta was manually segmented using the B-mode ultrasound images and the mean and normalized spatial variance of the parameters in the placenta were calculated for each GD. Variance was selected instead of standard deviation in order to assess the dispersion of parameter values in the placenta.

To evaluate the accuracy of our approach, we compared our pixel-wise analysis with parameters from TIC fits in three kernel sizes. The CEUS signal was averaged in 2 × 2, 4 × 4 and 8 × 8 kernels, fitted to the lognormal function and haemodynamic parameters were calculated (electronic supplementary material).

3. Results

Using our described methods, we acquired CEUS images of Sprague Dawley rat placentas at GD 14, 16 and 18. After applying the pixel-wise fitting of the TICs, we generated images of the fit parameters. Parametric images were overlaid on the B-mode image of anatomy for each GD to display the spatial distribution of the parameters.

Figures 4 and 5 show the RBF and PE in the placenta and surrounding tissue. Both RBF and PE increased from GD 14 to GD 18, consistent with previously reported data from an ROI-based analysis [21,22]. The parametric images show the total blood volume of the placenta increasing during gestation as placental perfusion increases. Additionally, the central arterial canal and spiral arteries are more easily identified as distinct regions of high blood flow by GD 18. With a pixel size of 0.021 mm, the parametric images from the pixel-wise TIC fit provide a more accurate assessment of the parameters' spatial variation compared with the TIC fits from larger kernel sizes (0.14 mm/pixel for the 8 × 8 sized kernel analysis). Data are shown as mean ± s.e.m. Plots of mean RBF and PE (figures 4 and 5, respectively) are shown on a log scale for comparison.

Figure 4.

Parametric images of relative blood flow (RBF) superimposed on B-mode US images of the placental environment on GD 14 (a), GD 16 (b), and GD 18 (c). The rectangle (in yellow online) indicates the ROI where CEUS images were acquired. The bar graphs display the mean and normalized variance in blood flow increases in the placenta during gestation. Data shown as log scaled mean ± SEM. Scale bars = 3 mm. (Online version in colour.)

Figure 5.

Parametric images of peak enhancement (PE), representative of local blood volume, superimposed on B-mode US images of the placenta on GD 14 (a), GD 16 (b) and GD 18 (c). The rectangle (in yellow online) indicates the ROI where CEUS images were acquired. The bar graphs display the mean and normalized variance of PE in the placenta during gestation. Data shown as log scaled mean ± SEM. Scale bars = 3 mm. (Online version in colour.)

MTT (figure 6) was found to decrease in the placenta during late gestation, indicating an increase in circulation time of MBs through the placental microvasculature. Areas with slower transit times can be identified from the parametric images. These regions probably correspond to the placental labyrinth where materno-fetal exchange occurs [25]. The variance of all three haemodynamic parameters increased during gestation, probably reflecting the increased size and vascular complexity that develops in a maturing placenta.

Figure 6.

Parametric images of mean transit time (MTT) superimposed on B-mode US images of the placental environment on GD 14 (a), GD 16 (b) and GD 18 (c). The rectangle (in yellow online) indicates the ROI where CEUS images were acquired. The bar graphs display the mean and normalized variance of the MTT. The MTT decreases during gestation while variance increases, indicating that MBs circulate through placental microvasculature more quickly. Data shown as mean ± SEM. Scale bars = 3 mm. (Online version in colour.)

RBF, PE and MTT were also calculated from the TIC fit to the averaged CEUS signal in 2 × 2, 4 × 4 and 8 × 8 sized kernels. Mean and normalized variance were calculated in the placenta for each parameter and the parametric images were overlaid on the B-mode images of anatomy (electronic supplementary material).

4. Discussion

Abnormal placental perfusion, which is challenging to measure in vivo, could be an important indicator of the abnormal placental development linked to many pregnancy-related diseases, including fetal growth restriction and preeclampsia. We have implemented CEUS imaging as a non-invasive modality capable of providing in vivo perfusion analysis. Recent work has shown the safe use of CEUS imaging to quantify perfusion in the rat placenta [21,22]. However, a TIC analysis based on an ROI can provide only a general assessment of perfusion for the region selected. Applying curve fitting to every pixel provides the spatial distribution of the perfusion parameters, allowing for the distinction of regions with differing vascular kinetics. Our work here demonstrates a pixel-wise approach to longitudinally track perfusion in a pregnant rat model.

During human development, the growth of spiral artery diameter and maternal blood flow, coupled with a decrease in uterine artery resistance, leads to increased placental perfusion. Our study showed that parameters associated with placental perfusion increase during gestation, consistent with these physiological haemodynamic changes. Using a pixel-wise approach, we quantified the spatial variations of these parameters in the placenta and surrounding tissue. The normalized variance of the parameters in the placenta increased during gestation, which may be due to the increasing complexity of the blood flow through the maturing placenta. From the parametric images, we were able to identify distinct regions of high blood flow, including spiral arteries, the central arterial canal and the intervillous space. As the placenta becomes more perfused during late gestation, blood flow in these regions increases. Specifically, the central arterial canal, which is unidentifiable during earlier gestation, is clearly defined on GD 18. Whether placental perfusion is altered during disease could provide indications of the impact of physiological factors, such as blood speed or pressure of flow, on placental growth and remodelling. The high-resolution images resulting from the pixel-wise TIC analysis provide insight into the spatial variance of the haemodynamic parameters which could be an important indicator for assessing abnormal placental perfusion due to disease.

We also investigated the accuracy of our pixel-wise analysis by averaging the CEUS signal in three different kernel sizes prior to the TIC analysis. As would be expected, the spatial variation decreases with increasing kernel size due to the loss spatial resolution. The kernel size and filter tolerance should be chosen based on the sensitivity needed for the specific application of these methods. Here, a pixel-wise approach was chosen because the improved spatial resolution highlights the heterogeneous blood flow patterns in the placenta, and how those blood flow patterns change longitudinally over development. Recently, de Senneville et al. [26] reported a fluid dynamic model to quantify blood velocity in the placentas of normal pregnant and IUGR rats. This computational model quantifies microbubble transport through spatial and temporal variations in CEUS signal intensity. The addition of this model to TIC analysis would provide further insight into the velocity vectors associated with microbubble transport in the placenta.

The methods developed here for evaluating placental perfusion in a rat model could be applied to distinguish between conditions that are likely to impact placental flow, such as fetal growth restriction, gestational diabetes and preeclampsia. A limitation of the work shown here is that a single set of images, representing a single injection cycle, was used to develop our computational methods. Additionally, the high-frequency ultrasound imaging used limits the maximum achievable imaging field. In a clinical setting, a placenta located deep within tissue or on the posterior uterine wall probably could not be imaged using these methods. MBs are also not currently approved for use during human pregnancy. Our images did not detect any CEUS signal in the fetus or umbilical cord, indicating that MBs do not cross the placental barrier into the developing fetus and are likely to be confined to the maternal vasculature. While this provides reassurances for potential clinical implementation, it demonstrates a weakness of these methods, in that they are unable to measure fetal blood flow. MB concentration, frequency of imaging sessions and the postpartum health of both mother and offspring need to be investigated prior to clinical use.

In summary, this study demonstrates the use of a pixel-wise TIC approach to monitor and quantify placental perfusion in vivo. These methods could be adapted to the analysis of altered perfusion during preeclampsia, gestational diabetes or fetal growth restriction. Further, these methods could be combined with other multi-modal imaging technologies, such as spectral photoacoustic imaging [27], to greatly improve understanding of how placental perfusion and function are altered in diseases of pregnancy, and to better understand treatment mechanisms. Pixel-wise TIC fitting of CEUS images could also be applied after injecting targeted contrast agents to determine whether these methods could be adapted to indicate placental receptor expression. CEUS-based parametric imaging and perfusion quantification using a pixel-wise TIC approach is a promising technique for elucidating the pathogenesis of pregnancy-related diseases and evaluating potential treatments.

Ethics

Animal studies were conducted following protocols approved by the Institutional Animal Care and Use Committee at Tulane University, New Orleans, LA, USA.

Data accessibility

Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.23c646q.

Authors' contributions

D.J.L. performed all imaging experiments and data analysis, and drafted the manuscript. K.H. assisted with imaging experiments and revised the manuscript. C.L.B. designed and oversaw the execution of the study, including preparation of the final manuscript.

Competing interests

The authors declare no competing interests.

Funding

We thank the Louisiana Board of Regents Graduate Research Fellowship (D.J.L.) and NIH P20GM109036 (K.H., C.L.B.) for support.