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Medicine

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

doi: 10.3791/61953 Published: January 7, 2021
Datta Singh Goolaub1,2, Davide Marini3,4, Mike Seed4,5, Christopher K. Macgowan1,2

Abstract

Magnetic resonance imaging (MRI) is an important tool for the clinical assessment of cardiovascular morphology and heart function. It is also the recognized standard-of-care for blood flow quantification based on phase contrast MRI. While such measurement of blood flow has been possible in adults for decades, methods to extend this capability to fetal blood flow have only recently been developed.

Fetal blood flow quantification in major vessels is important for monitoring fetal pathologies such as congenital heart disease (CHD) and fetal growth restriction (FGR). CHD causes alterations in the cardiac structure and vasculature that change the course of blood in the fetus. In FGR, the path of blood flow is altered through the dilation of shunts such that the oxygenated blood supply to the brain is increased. Blood flow quantification enables assessment of the severity of the fetal pathology, which in turn allows for suitable in utero patient management and planning for postnatal care.

The primary challenges of applying phase contrast MRI to the human fetus include small blood vessel size, high fetal heart rate, potential MRI data corruption due to maternal respiration, unpredictable fetal movements, and lack of conventional cardiac gating methods to synchronize data acquisition. Here, we describe recent technical developments from our lab that have enabled the quantification of fetal blood flow using phase contrast MRI, including advances in accelerated imaging, motion compensation, and cardiac gating.

Introduction

Comprehensive assessment of the fetal circulation is necessary for monitoring fetal pathologies such as fetal growth restriction (FGR) and congenital heart disease (CHD)1,2,3. In utero, patient management and planning for postnatal care depend on the severity of the fetal pathology4,5,6,7. Feasibility of fetal blood flow quantification with MRI and its applications in assessing fetal pathologies have recently been demonstrated3,8,9. The imaging method, however, faces challenges, such as increased imaging times to achieve high spatiotemporal resolution, lack of cardiac synchronization methods, and unpredictable fetal motion10.

Fetal vasculature comprises small structures (~5 mm diameter for major blood vessels that comprise the descending aorta, ductus arteriosus, ascending aorta, main pulmonary artery, and superior vena cava11,12,13).To resolve these structures and to quantify flow, imaging at high spatial resolution is required. Moreover, the fetal heart rate is about twice that of an adult. A high temporal resolution is thus also required to resolve dynamic cardiac motion and blood flow across the fetal cardiac cycle. Conventional imaging at this high spatiotemporal resolution requires relatively long acquisition times. To address this issue, accelerated fetal MRI14,15,16 has been introduced. Briefly, these acceleration techniques involve undersampling in the frequency domain during data acquisition and retrospective high-fidelity reconstruction using iterative techniques. One such approach is compressed sensing (CS) reconstruction, which allows reconstruction of images from heavily undersampled data when the reconstructed image is sparse in a known domain and undersampling artifacts are incoherent17.

Motion in fetal imaging presents a major challenge. Motion corruption can arise from maternal respiratory motion, maternal bulk motion or gross fetal movement. Maternal respiration leads to periodic translations of the fetus, whereas fetal movements are more complex. Fetal movements can be classified as localized or gross10,18. Localized movements involve motion of only segments of the body. They typically last for about 10-14 s and their frequency increases with gestation (~90 per hour at term)10. These movements generally cause small corruptions and do not affect the imaging area of interest. However, gross fetal movements can lead to severe image corruption with through plane motion components. These movements are whole body movements mediated by the spine and last for 60-90 s.

To avoid artifacts from fetal motion, steps are first taken to minimize maternal motions. Pregnant women are made more relaxed using supportive pillows on the scanner bed and dressed in comfortable gowns and may have their partners present beside the scanner to reduce claustrophobia19,20. To mitigate effects of maternal respiratory motion, studies have performed fetal MR exams under maternal breath-hold21,22,23. However, such acquisitions must be short (~15 s) given the reduced breath-hold tolerance of pregnant subjects. Recently, retrospective motion correction methods have been introduced for fetal MRI14,15,16. These methods track fetal motion using registration toolkits and correct for motion or discard uncorrectable portions of acquired data.

Finally, postnatal cardiac MR images are conventionally acquired using electrocardiogram (ECG) gating to synchronize data acquisition to the cardiac cycle. Without gating, cardiac motion and pulsatile flow from throughout the cardiac cycle are combined, producing artifacts. Unfortunately, the fetal ECG signal suffers from interference from the maternal ECG signal24 and distortions from the magnetic field25. Hence, alternative non-invasive approaches to fetal cardiac gating have been proposed, including self-gating, metric optimized gating (MOG) and doppler ultrasound gating21,26,27,28.

As described in the following sections, our MRI approach to quantify fetal blood flow leverages a novel gating method, MOG, developed in our laboratory and combined with motion correction and iterative reconstruction of accelerated MRI acquisitions. The approach is based on a pipeline in a previously published study14 and is composed of the following five stages: (1) fetal blood flow acquisition, (2) real-time reconstructions, (3) motion correction, (4) cardiac gating, and (5) gated reconstructions.

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Protocol

All MRI scans were performed with informed consent from volunteers as part of a study approved by our institutional research ethics board.

NOTE: The methods described below have been used on a 3T MRI system. The acquisition is performed using a radial phase contrast MRI sequence. This sequence was prepared by modifying the readout trajectory (to achieve a stellate pattern) of the manufacturer's Cartesian phase contrast MRI. The sequence and sample protocols are available upon request through our C2P exchange platform. All reconstructions in this work were performed on a standard desktop computer with the following specifications: 32 GB memory, 3.40 GHz processor with 8 cores, and 2GB graphic card with 1024 compute unified device architecture (CUDA) cores. Image reconstruction was performed on MATLAB. Nonuniform fast Fourier transform (NUFFT)29 was performed on the graphics processing unit (GPU). Motion correction parameters were calculated using elastix30Figure 1 depicts the protocol in a chronological order, tracking how the acquired velocity encodes (color coded in Figure 1) are processed with representative images at each stage of reconstruction. The reconstruction code is available at https://github.com/datta-g/Fetal_PC_MRI. While we provide the steps in the protocol here, most of these algorithm steps are automated in our pipeline.

1. Subject positioning and localizer exams

  1. Assist the mother in positioning herself on the MRI table in her preferred comfortable position, usually supine or lateral decubitus positions, for the MRI exam.
  2. Place the cardiac coil over the abdominal region of the mother.
  3. Load the MRI table in the magnet bore and notify the mother that the scan is about to start.
  4. Run a localizer exam to locate the fetal body (resolution: 0.9 x 0.9 x 10 mm3, TE/TR: 5.0/15.0 ms, FOV: 450 x 450 mm2, slices: 6).
  5. Run a refined localizer exam to locate the fetal vasculature with the slice group centered on the fetal heart (resolution 1.1 x 1.1 x 6.0 mm3, TE/TR: 2.69/1335.4 ms, FOV: 350 x 350 mm2, slices: 10, orientation: axial to fetus).
  6. Repeat the refined localizers with sagittal and coronal orientations for a clearer view of the fetal vessels.
  7. Repeat the refined localizers in cases of gross fetal motion.

2. Acquisition of fetal blood flow data

  1. Locate fetal vessels using the localizer exams. For example, the descending aorta is a long straight vessel near the spine in the sagittal planes. The ascending aorta and main pulmonary arteries can be identified as vessels leaving the left and right ventricles, respectively. The ductus arteriosus can be tracked as a downstream segment of the main pulmonary artery proximal to the descending aorta. The superior vena cava can be identified from axial planes near the base of the fetal heart as the vessel adjacent to the ascending aorta.
  2. Prescribe a slice perpendicular to the axis of the fetal vessel of interest. Rotate and move the slice guideline on the MRI console computer such that it intersects the target vessel perpendicularly.
  3. Set the scan parameters (acquisition type: radial phase contrast MRI, resolution: 1.3 x 1.3 x 5.0 mm3, echo time (TE)/ repetition time (TR): 3.25/5.75 ms, field-of-view (FOV): 240 x 240 mm2, slice: 1, velocity encoding: 100-150 cm/s depending on vessel of interest, velocity encoding direction: through plane, radial views: 1500 per encode, free breathing).
  4. Run the scan and verify the prescription based on the initial time-averaged reconstruction performed and displayed on the MRI console computer. Repeat the localizer and phase contrast scans if the target vessel is absent or unidentifiable from the initial reconstruction. Acquired raw data is represented in the schematic in Figure 1A with the velocity compensated and through plane acquisitions color coded as red and blue, respectively.
  5. Repeat the fetal blood flow data acquisition for each target blood vessel.
    NOTE: The acquired raw data (format: DAT files) must be transferred for offline reconstruction. For example, on Siemens scanners, this can be performed by running 'twix'. The acquired raw data is right clicked from the list acquisitions and "copy total raid file" is chosen.

3. Motion correction of fetal measurements

  1. Reconstruct real-time series (temporal resolution: 370 ms, radial views: 64) from the acquired data using CS with 15 iterations of a conjugate gradient descent optimization exploiting spatial total variation (STV, weight: 0.008) and temporal total variation (TTV, weight: 0.08) regularization as represented by the schematic in Figure 1B.
  2. Select a region of interest (ROI) encompassing the vessel of interest from this first real-time reconstruction using a graphic user interface developed in MATLAB. In this step, the user must draw a contour that encloses the fetal anatomy, such as the target great vessels or the fetal heart.
  3. Perform rigid-body motion tracking with elastix30 (based on normalized mutual information with empirically optimized parameters: 4 pyramid levels, 300 iterations and translational transforms).
  4. Reject tracked real-time frames that share low mutual information (MI) with all other frames (whereby MI is less than 1.5x the interquartile range from the mean MI). These frames are deemed to be represented through plane motion or gross fetal motion.
  5. Use the MRI data corresponding to the longest series of continuous real-time frames (without gaps) from the remaining frames as the quiescent period used for further reconstruction.
  6. Interpolate translational motion correction parameters from the temporal resolution of the real-time series (370 ms) to the TR of the quiescent acquisition (5.75 ms).
  7. Apply interpolated parameters to the defined quiescent period of the MRI data by modulating the phase as in:
    Equation 1

    where s' is the motion corrected data, kx and ky are the coordinates in k-space, s is the acquired uncorrected data, Δx and Δy are the tracked displacements in space, and j represents Equation 3.
    NOTE: All numerical values of regularization coefficients in this work were optimized in earlier experiments. This was accomplished using a brute-force grid search to find the regularization coefficients that minimized the error between reconstructions of a highly sampled fetal reference dataset and retrospectively undersampled cases from the same dataset.

4. Solving for fetal heart rate

  1. Reconstruct a second real-time image series at a higher temporal resolution (temporal resolution: 46 ms, radial views: 8) using the acquired data using CS, again with 15 iterations of a conjugate gradient descent optimization with STV (weight: 0.008) and TTV (weight: 0.08) regularization as represented by the schematic in Figure 1C.
  2. Re-select an ROI encompassing the fetal vessel of interest.
  3. Run multiparameter MOG on the real-time series to derive the time-dependent fetal heart rate.
  4. Bin motion corrected MRI data into 15 cardiac phases using the derived heart rate waveform. In this step, the temporal boundaries of the cardiac phases are computed using the heart rate from the previous step. For instance, the boundaries for the ith phase in the kth heartbeat are given by:
    Equation 2a
    Equation 2b
    where HR(K) is the time at which the kth heartbeat occurs. The timestamp of the nth radial acquisition is given by (n x TR). Data with timestamps falling within the boundaries of a cardiac phase are assigned to that phase.
    NOTE: MOG is a gating technique26 that comprises iterative binning of the acquired data based on a multi-parameter fetal heart rate model to create CINE images that optimize an image metric over a region of interest.

5. Reconstruction of fetal CINEs

  1. Reconstruct fetal flow CINEs using the binned motion corrected MRI data and CS with 10 iterations of a conjugate gradient descent optimization with STV (weight: 0.025) and TTV (weight: 0.01) regularization. Two CINEs are produced at this step: one for the flow compensated acquisition, CFC, and one with the flow encoded data, CFE, as represented in the schematic in Figure 1D.
  2. Compute the velocity image given by the phase of the elementwise product of CFE and the complex conjugate of CFC.
  3. Apply background phase correction31 to correct for eddy current effects. Briefly, in this automatic step, a plane is fitted to the phase of static fetal and maternal tissues. The correction is performed by subtracting the plane from the velocity sensitive phase computed in 4.2.
  4. Write reconstructed data into DICOM files.
  5. Load DICOMs into flow analysis software, such as Segment v2.232.
  6. Draw an ROI encompassing the lumen of the blood vessel of interest using the anatomical and velocity sensitive images.
  7. Propagate the ROI to all cardiac phases and correct for changes in the vessel's diameter.
  8. Record flow measurements.

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Representative Results

In general, phase MRI examinations of flow target six major fetal vessels: the descending aorta, ascending aorta, main pulmonary artery, ductus arteriosus, superior vena cava, and umbilical vein. These vessels are of interest to the clinician as they are often implicated in CHD and FGR, influencing the distribution of blood throughout the fetus9. A typical scan duration with the radial phase contrast MRI is 17 s per vessel such that the scans are short while also allowing time for enough data acquisition for CINE reconstruction. The total acquisition time, including localizers and phase contrast MRI, for the representative results was 3 min. In this study, representative results are presented using flow acquisition data from the descending aorta in two human fetuses: Fetus 1 and Fetus 2 with gestational ages (week + days) of 35+4 and 37+3, respectively.

As in Figure 1, initial real-time reconstructions (temporal resolution: 370 ms) performed for motion tracking took 45 s per reconstructed slice. Translation motion tracking took 2 min for each slice. The extracted motion parameters for Fetus 1 (Figure 2 A1, maximum displacement: 1.6 mm) and Fetus 2 (Figure 2 A2, maximum displacement: 1.3 mm) depict the motion of the descending aorta over the duration of the scan. The shared mutual information of each real-time frame with all other co-registered frames are shown in Figure 2 B1 (Fetus 1) and Figure 2 B2 (Fetus 2). In these cases, all frames shared mutual information above the cut off criteria, so no data was rejected. The second real-time reconstructions (temporal resolution: 46 ms), used to derive cardiac gating information, took 10 min for each slice. MOG derived the fetal heartbeat (RR) intervals using a multiparameter model, as shown in Figure 2 C1 (Fetus 1, RR interval: 521 ± 20 ms) and Figure 2 C2 (Fetus 2, RR interval: 457 ± 9 ms).

Final CINE reconstructions using the retrospectively motion-corrected and gated data took 3 min per slice. The anatomical and velocity reconstructions for Fetus 1 and Fetus 2 at peak systole are shown in Figure 3. Reconstructions with motion correction show vessels with sharper walls. Without motion correction, the descending aorta is blurrier and less conspicuous. The measured flow curves from each fetus (Figure 4) show higher peak and mean flows in the reconstructions without motion correction ([peak mean]: Fetus 1 [25.2 9.8] ml/s, Fetus 2 [34.6 10.3] ml/s]) than in those with motion correction ([peak mean]: Fetus 1 [23.5 9.2] ml/s, Fetus 2 [28.7 9.7] ml/s]).

Figure 1
Figure 1: Pipeline to reconstruct fetal phase contrast MRI data. (A) Step 1: Golden-angle radial phase contrast MRI data (color coded as: flow compensation = red & through plane encode = blue). The alternating colors depict that the flow-compensated and through-plane encoded acquisitions occur at the same spatial frequencies. (B) Step 2: Temporal windows of 370 ms for real-time reconstruction using CS with sparsity constraints (STV and TTV). Motion correction and data rejection are performed. (C) Step 3: Temporal windows of 46 ms are created for real-time reconstruction with CS (with STV and TTV sparsity constraints) for MOG. (D) Step 4: The data is binned into cardiac phases (CP), and CS is used to create a fetal flow CINE, with sparsity constraints (STV and TTV). Representative reconstructions from each CS step are shown in the Reconstructions column. Reconstructions for steps 3 and 4 are shown for a time point corresponding to peak systole. Scale bars in the top left corner of the anatomical images denote 10 mm in the image. The time specifications, in seconds, highlighted in grey represent the durations of the corresponding steps. STV: spatial total variation, TTV: temporal total variation, CS: compressed sensing, MOG: metric optimized gating, CINE: gated dynamic reconstruction. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Representative displacement and heart rate curves. A1 and A2 depict retrospectively tracked displacement curve for the scans in Fetus 1 and Fetus 2, respectively. B1 and B2 show the sum of the mutual information of a given frame with all other frames for Fetus 1 and Fetus 2, respectively. The red dotted lines represent 1.5x interquartile range below which data is rejected. C1 and C2 depict the RR intervals derived with MOG in Fetus 1 and Fetus 2, respectively. RR interval: time between consecutive heartbeats, MOG: metric optimized gating. Please click here to view a larger version of this figure.

Figure3
Figure 3: Representative velocity sensitive CINE reconstructions at peak systole. Each quadrant depicts the anatomical and velocity reconstructions. The top row shows the CINE with motion correction in Fetus 1 and Fetus 2, respectively. The bottom row shows the CINE without motion correction in Fetus 1 and Fetus 2, respectively. The red and blue arrows depict the descending aorta. Scale bars in the top left corner of the anatomical images denote 10 mm. Please click here to view a larger version of this figure.

Figure 4
Figure 4: Representative flow curves in the fetal descending aorta. The solid and dashed data lines depict the flow curves obtained from CINE reconstructions with and without motion correction, respectively, in Fetus 1 (left) and Fetus 2 (right). Please click here to view a larger version of this figure.

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Discussion

This method enables the non-invasive measurement of blood flow in human fetal great vessels and allows for retrospective motion correction and cardiac gating by making use of iterative reconstruction techniques. Fetal blood flow quantification has been performed with MRI in the past1,3,8,9. These studies had a prospective approach to mitigate motion corruption whereby scans would be repeated if gross fetal motion was visually identified from an initial reconstruction on the scanner. The current protocol improves on this by retrospectively rejecting data corrupted by gross fetal motion and further corrects for in-plane displacements arising from subtle fetal movements or maternal respiratory motion.

This protocol makes use of a multiparameter model for MOG whereby the RR interval for each fetal heartbeat is calculated. Using a low parameter heart rate model (such as 2 parameters) is generally acceptable for short scans since the healthy fetal heart rate has a low variability33. However, low parameter models become problematic for longer scans or in cases of pathologies such as arrythmia. A multiparameter model in MOG can track these changing RR intervals, providing more accurate flows.

The current protocol allows for some modifications. First, third party software used in this study for motion tracking and flow analysis can be replaced by other available software packages. Second, the number of iterations in the conjugate gradient descent algorithms for CS can be increased. In this study, the number of iterations in each step was set at a value beyond which there were minimal improvements based on prior reconstructions. In this work, only third trimester pregnancies were scanned. In earlier pregnancies, the fetus is smaller and there may be more room for motion. However, since quiescent periods in the scan are identified retrospectively for CINE reconstructions, this protocol should be successful for flow imaging at these earlier ages. An increase in the resolution of the scans may be required to cater to smaller vessel diameters at lower gestational age. For this protocol, the reconstruction times reported in Figure 1 and the Results are heavily dependent on the computational power available. For example, with better GPUs and more powerful processors, reconstruction times can be significantly reduced.

The protocol has certain limitations. First, the quality of the CINE reconstruction depends on the amount of data rejected in the motion correction step. With increasing episodes of gross fetal movements during a scan, more data is rejected. Consequently, the resulting signal-to-noise ratio (SNR) in the CINE reconstructions will decrease. Low SNR increases the uncertainty in the velocity images34 and the resulting flow quantification. Performance will therefore improve with greater fetal quiescence. Second, the method depends on the definition of ROIs for motion correction and MOG. In the current implementation, this step is performed manually. We have found that the reconstruction is stable to small differences in ROI position but this process results in wait times between the data acquisition and CINE reconstructions (since there are two ROI placement steps between the three iterative reconstruction steps). This becomes more cumbersome when there is a large number of slices acquired. In future implementations of the protocol, ROI placement will be automated.

Currently, we are using the presented protocol in research studies with approval from the local ethics board. The protocol can also be used in cases in which motion is a potential problem during an MRI exam, such as in neonates or uncooperative subjects. Future directions of the method involve investigating spiral trajectories35,36, which provide more efficient sampling and a possibility for exploring real-time fetal flow.

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Disclosures

None.

Acknowledgments

None.

Materials

Name Company Catalog Number Comments
elastix Image Sciences Institute, University Medical Center Utrecht Image registration software
Geforce GTX 960  Nvidia  04G-P4-3967-KR
gpuNUFFT CAI²R Non-uniform fast Fourier transform
MAGNETOM Prisma Siemens 10849583
MATLAB MathWorks
Radial Phase Contrast MRI sequence Trajectory modification of manufacturer's Cartesian Phase Contrast sequence
Segment Medvisio Data analysis
VENGEANCE Corsair LPX DDR4-2666 

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Cite this Article

Goolaub, D. S., Marini, D., Seed, M., Macgowan, C. K. Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation. J. Vis. Exp. (167), e61953, doi:10.3791/61953 (2021).More

Goolaub, D. S., Marini, D., Seed, M., Macgowan, C. K. Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation. J. Vis. Exp. (167), e61953, doi:10.3791/61953 (2021).

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