Volumetric Parameterization of the Placenta to a Flattened Template
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2111.07900.pdf
Description
Accepted version
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7.2 MB
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Author(s)
Abulnaga, S Mazdak
Turk, Esra Abaci
Bessmeltsev, Mikhail
Grant, P Ellen
Solomon, Justin
Golland, Polina
Date Issued
2022
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Abulnaga, S Mazdak, Turk, Esra Abaci, Bessmeltsev, Mikhail, Grant, P Ellen, Solomon, Justin et al. 2022. "Volumetric Parameterization of the Placenta to a Flattened Template." IEEE Transactions on Medical Imaging, 41 (4).
Version
Author's final manuscript
Abstract
We present a volumetric mesh-based algorithm for parameterizing the placenta
to a flattened template to enable effective visualization of local anatomy and
function. MRI shows potential as a research tool as it provides signals
directly related to placental function. However, due to the curved and highly
variable in vivo shape of the placenta, interpreting and visualizing these
images is difficult. We address interpretation challenges by mapping the
placenta so that it resembles the familiar ex vivo shape. We formulate the
parameterization as an optimization problem for mapping the placental shape
represented by a volumetric mesh to a flattened template. We employ the
symmetric Dirichlet energy to control local distortion throughout the volume.
Local injectivity in the mapping is enforced by a constrained line search
during the gradient descent optimization. We validate our method using a
research study of 111 placental shapes extracted from BOLD MRI images. Our
mapping achieves sub-voxel accuracy in matching the template while maintaining
low distortion throughout the volume. We demonstrate how the resulting
flattening of the placenta improves visualization of anatomy and function. Our
code is freely available at https://github.com/mabulnaga/placenta-flattening .
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/TMI.2021.3128743
