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通过体积参数化实现胎盘扁平化

Placental Flattening via Volumetric Parameterization.

作者信息

Abulnaga S Mazdak, Turk Esra Abaci, Bessmeltsev Mikhail, Grant P Ellen, Solomon Justin, Golland Polina

机构信息

Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA.

Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.

出版信息

Med Image Comput Comput Assist Interv. 2019 Oct;11767:39-47. doi: 10.1007/978-3-030-32251-9_5. Epub 2019 Oct 10.

DOI:10.1007/978-3-030-32251-9_5
PMID:32432232
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7235151/
Abstract

We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied shape. We formulate our method as a map from the shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our implementation is freely available at https://github.com/mabulnaga/placenta-flattening.

摘要

我们提出了一种基于体网格的算法,用于将胎盘展平为标准模板,以实现对局部解剖结构和功能的有效可视化。监测胎盘功能有望支持孕期评估并改善护理结果。我们旨在缓解胎盘附着于弯曲子宫壁时其形状所带来的可视化和解读挑战。为此,我们将捕捉胎盘形状的体网格展平,使其类似于经过充分研究的形状。我们将我们的方法表述为从该形状到展平模板的映射,该映射使对称狄利克雷能量最小化,以控制整个体积内的变形。在梯度下降过程中,通过约束线搜索来强制局部单射性。在一项胎盘功能的临床研究中,我们对从MRI图像中提取的28个胎盘形状评估了所提出的方法。我们在将胎盘边界映射到模板时实现了亚体素精度,同时成功地控制了整个体积内的变形。我们展示了由此产生的胎盘映射如何增强对胎盘解剖结构和功能的可视化。我们的实现可在https://github.com/mabulnaga/placenta - flattening上免费获取。

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In Vivo Quantification of Placental Insufficiency by BOLD MRI: A Human Study.磁共振血氧水平依赖成像定量评估胎盘功能不全:一项人类研究
Sci Rep. 2017 Jun 16;7(1):3713. doi: 10.1038/s41598-017-03450-0.
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Placenta Maps: In Utero Placental Health Assessment of the Human Fetus.胎盘图谱:胎儿宫内胎盘健康评估。
IEEE Trans Vis Comput Graph. 2017 Jun;23(6):1612-1623. doi: 10.1109/TVCG.2017.2674938. Epub 2017 Feb 24.
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Changes in human fetal oxygenation during maternal hyperoxia as estimated by BOLD MRI.BOLD MRI 评估的母体高氧血症期间胎儿氧合的变化。
Prenat Diagn. 2013 Feb;33(2):141-5. doi: 10.1002/pd.4025. Epub 2012 Dec 12.
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