Volumetric Parameterization of the Placenta to a Flattened Template
This work addresses visualization challenges for placental anatomy and function in medical imaging, but it is incremental as it applies existing parameterization techniques to a specific domain.
The authors tackled the problem of interpreting and visualizing curved placental shapes from MRI by developing a volumetric mesh-based algorithm that maps the placenta to a flattened template, achieving sub-voxel accuracy with low distortion in a validation study of 111 placental shapes.
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 .