IEEE Trans Med Imaging. 2022 Apr;41(4):925-936. doi: 10.1109/TMI.2021.3128743. Epub 2022 Apr 1.
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.
我们提出了一种基于体网格的算法，用于将胎盘参数化到一个展开的模板上，以实现对局部解剖结构和功能的有效可视化。MRI 作为一种研究工具具有潜力，因为它提供了与胎盘功能直接相关的信号。然而，由于胎盘在体内的形状是弯曲的且高度可变，因此解释和可视化这些图像具有一定的难度。我们通过将胎盘映射到类似于熟悉的离体形状来解决解释挑战。我们将参数化表示为将体网格表示的胎盘形状映射到展开模板的优化问题。我们使用对称狄利克雷能量来控制整个体积内的局部变形。在梯度下降优化过程中，通过受限的直线搜索来强制映射中的局部单值性。我们使用从 BOLD MRI 图像中提取的 111 个胎盘形状的研究来验证我们的方法。我们的映射在匹配模板时达到了亚像素精度，同时在整个体积内保持低变形。我们展示了胎盘的这种展开如何改善解剖结构和功能的可视化。我们的代码可在 https://github.com/mabulnaga/placenta-flattening 上免费获得。
