Rapid inference of personalised left-ventricular meshes by deformation-based differentiable mesh voxelization
Rapid inference of personalised left-ventricular meshes by deformation-based differentiable mesh voxelization
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DOI：
10.1016/j.media.2022.102445
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发表时间：
2022-04-22
影响因子：
10.9
通讯作者：
Kozerke, Sebastian
中科院分区：
文献类型：
--
作者：
Joyce, Thomas;Buoso, Stefano;Kozerke, Sebastian
我们提出了一种基于形状模型变形的可微体积网格体素化技术，并证明了它可以直接从磁共振图像切片数据中预测左心室解剖结构。预测的解剖结构是适合直接包含在生物物理模拟中的体积网格。所提出的方法可以利用现有的（基于像素的）分割网络，并且不需要任何ground truth配对图像和网格训练数据。我们证明了这种方法可以从很少的切片中产生准确的预测，并且可以结合从不同视图中获得的图像信息（例如融合来自短轴和长轴切片的形状信息）。我们证明了所提出的方法比最先进的基于配准的方法快几倍。此外，我们证明了我们的方法可以纠正切片错位，并且对不完整和不准确的输入数据具有鲁棒性。我们进一步证明，通过对4D数据的每一帧拟合网格，我们可以确定射射分数，冲程体积和应变。(c) 2022作者。这是一篇基于CC by-nc-nd许可的开放获取文章（http://creativecommons.org/licenses/by-nc-nd/4.0/）
We propose a differentiable volumetric mesh voxelization technique based on deformation of a shapemodel, and demonstrate that it can be used to predict left-ventricular anatomies directly from magnetic resonance image slice data. The predicted anatomies are volumetric meshes suitable for direct inclusion in biophysical simulations. The proposed method can leverage existing (pixel-based) segmentation networks, and does not require any ground truth paired image and mesh training data. We demonstrate that this approach produces accurate predictions from few slices, and can combine information from images acquired in different views (e.g. fusing shape information from short axis and long axis slices). We demonstrate that the proposed method is several times faster than a state-of-the-art registration based method. Additionally, we show that our method can correct for slice misalignment, and is robust to incomplete and inaccurate input data. We further demonstrate that by fitting a mesh to every frame of 4D data we can determine ejection fraction, stroke volume and strain. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )