Paddy J. Slator^{1}, Jana Hutter^{2,3}, Laura McCabe^{2}, Ana Dos Santos Gomes^{2}, Anthony N. Price^{2,3}, Mary A. Rutherford^{2}, Joseph V. Hajnal^{2,3}, and Daniel C. Alexander^{1}

Developmental abnormalities in placental vascular formation are associated with major complications of pregnancy, such as fetal growth restriction and early onset pre-eclampsia. Multi-shell diffusion MRI at 3T has been shown to be capable of capturing fine microstructural information in a wide variety of organs and disease states, and could prove valuable for studying the human placenta in-vivo. We develop a rich scanning protocol, and use the data to investigate the complexity of models supported by the placental diffusion MRI signal. We demonstrate the feasibility of using this non-invasive approach to quantify microstructure in the human placenta and surrounding tissue.

Introduction

Diffusion MRI (dMRI) of the placenta commonly uses apparent
diffusion coefficient (ADC)^{1–3}, intravoxel incoherent motion
(IVIM)^{4–9} or diffusion tensor^{10} models. IVIM is the more biologically
relevant model – the bi-exponential curve may reflect the two separate populations
of placental blood (residing in the intervillous space and fetal vasculature)^{11}. The IVIM perfusion fraction has
been shown to relate to pathologies such as fetal growth restriction^{4,6,8,9}. In other contexts, e.g.
brain and prostate^{12}, a range of models have shown
the ability to estimate more specific microstructural parameters. We compare a
variety of these models on a rich dMRI acquisition from the in-vivo human
placenta.

Methods

MRI was performed on three healthy pregnant subjects (gestational ages 27-37 weeks) using a
3T Philips Achieva scanner with a 32-channel cardiac coil. A single-shot echo-planar
dMRI protocol spanning a large range of b-values and gradient directions was used:
the 3 principal gradient directions were sampled at b=15, 25, 80, 115, 206, 346
s/mm^{2} and 8 directions were obtained at b=40, 400, 1000, 2000 s/mm^{2}.
Six b=0 images were also obtained. Other acquisition values were as follows: TR = 3792 ms, TE = 132 ms, FOV = 300
x 300 x 44 mm^{3}, 2.2 mm isotropic voxels, 20-25 contiguous
axial slices. Total scan duration was 3’55”. ROIs containing the placenta and
uterine wall were defined on the first b=0 image.

We fit the diffusion tensor model (using Camino^{13}) to each of the b=40, 400,
1000, 2000 s/mm^{2} shells individually. We also fit various isotropic
(ADC, IVIM, ball-sphere) and anisotropic (diffusion tensor, stick, ball-stick –
using the terminology of^{14}) models to the complete set
of diffusion-weighted measurements in each voxel. We fit each model using maximum
likelihood estimation of the parameters, assuming a Rician noise model^{14,15}.

Model selection was performed on a voxel-wise basis by
calculating the Bayesian information criterion (BIC). To assess the strength of
model preference between model pairs we calculated ∆BIC = BIC_{model1}
– BIC_{model2}. A difference of 10 or more between BIC values implies
“strong” evidence for the model with lower BIC^{16}.

Results

All subjects showed similar trends in their data. Figure 1
shows mean diffusivity (MD) maps across the b=40, 400, 1000 and 2000 s/mm^{2}
shells for one subject. At lower b values (b=40 s/mm^{2} and b=400 s/mm^{2}
shells) the MD in two subjects was much higher in the uterine wall than the
placenta (Figure 1) reflecting high levels of perfusion from maternal arteries
and veins. At high b-values the uterine wall and placenta are not well
differentiated by MD, although there is some structure remaining in spatial
maps (Figure 2).

We next examined, for isotropic models, the proportion of voxels where each model had the lowest BIC value (Figures 3a-b). For the two best models (ADC and IVIM) we visualized the areas of tissue where each performed best by plotting spatial maps of ∆BIC (Figures 3c-e), diffusivity (Figures 3f-h) and perfusion fraction (Figures 3i-k). Interestingly, ADC performed better than IVIM in the most of uterine wall voxels, and slightly better in placental voxels. The same analysis for anisotropic models (Figure 4) showed stick and ball-stick to be the best models, with stick generally better in areas surrounding the placenta.

We then directly compared two isotropic (ADC) and anisotropic (stick) models (Figure 5). ADC was clearly the best model within the placenta, whereas the stick model was preferred in some areas surrounding the placenta.

Discussion

We observed similar patterns in the placenta and uterine wall for parameter and ∆BIC maps in scans from three individuals. This suggests that dMRI is consistently sensitive to anatomically-linked microstructural differences.

Although the BIC comparison shows that an ADC model explains the signal better than IVIM in a majority of placental voxels (Figure 3), there are large patches of voxels where the bi-exponential IVIM explains the signal better. We hypothesize that these correspond to areas where the fetal vasculature and maternal blood (within the intervillous space) are both present.

When mapping the fractional anisotropy (Figures 4f-h), and comparing isotropic and anisotropic models directly (Figure 5), we observe that diffusion in the placenta is mainly isotropic, although there is evidence of anisotropy in the surrounding tissue. This is consistent with blood perfusing anisotropically into the placenta from the fetal and maternal sides.

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Histograms of mean diffusivity in the placenta and uterine wall. Each row corresponds to a diffusion scan on a different subject. The columns show the mean diffusivities derived from a diffusion tensor fit only to the images at b=0 and that nonzero b-value.

Mean diffusivity maps for 3 slices of a single scan. The columns b=40 s/mm^{2} to b=2000 s/mm^{2} show the mean diffusivities derived from a diffusion tensor fit only to the images at that nonzero b-value and b=0. Red and black outline the uterine wall and placenta ROIs respectively. Colour represents mean diffusivity in mm^{2}/s (note that the scale is a factor of 10 higher for the b=40 s/mm^{2} maps).

Comparison of isotropic models for the diffusion-weighted MRI signal (all b-values) in three scans. A-B: bar plots showing the proportion of voxels within ROIs where each model best fit the data. C-E: maps of BIC_{IVIM} - BIC_{ADC}, blue and red indicate preference for the IVIM and ADC models respectively. F-H: maps of mean diffusivity (in mm^{2}/s) derived from diffusion tensor fit to all b-values. I-K: maps of IVIM perfusion fraction.

Comparison of anisotropic models for the diffusion-weighted MRI signal (all b-values) in three scans. A-B: bar plots showing the proportion of voxels within ROIs where each model best fit the data. C-E: maps of BIC_{Stick} - BIC_{Ball-stick}, blue and red indicate preference for the stick and ball-stick models respectively. F-H: maps of fractional anisotropy values derived from diffusion tensor fit to all b-values.

Comparison of isotropic and anisotropic models in three scans. Maps of BIC_{iso} - BIC_{aniso}, where BIC_{iso} is the minimum value of the BIC for isotropic models (ADC, IVIM, ball-sphere), and BIC_{aniso} is the minimum value for anisotrophic models (stick, ball-stick, diffusion tensor). Rows contain slices from a single subject. Blue and red indicate preference for the isotropic and anisotropic model respectively.