发表机构
University of Texas at Arlington; New York University（德克萨斯大学阿灵顿分校; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对从稀疏心脏MRI数据重建四腔心的问题，Bi-PT通过双向点交叉注意力学习点特征，结合逐点语义标签改进对应估计，将变形场公式化为神经常微分方程，集成语义标签损失和添加平滑正则化，实验证明其性能准确且稳健。
AI 中文摘要
我们提出了Bi-PT，一种从临床稀疏采样的心脏磁共振成像（CMR）数据重建三维四腔人体心脏网格的流程。这项工作解决了从常规临床CMR协议中使用的二维长轴和短轴视图提取的稀疏点云（SPC）生成三维心脏形状时容易出错的问题。Bi-PT通过在图谱和SPC之间进行双向点交叉注意力学习鲁棒的点特征，以及使用逐点语义标签改进对应估计，从而能够从SPC准确推断四腔心网格。我们将变形场公式化为由逐点仿射变换和平移参数化的神经常微分方程（NODE），以使图谱向目标心脏形状变形。通过学习这样的NODE，我们可以保证变形场是局部仿射微分同胚变形。我们还将语义标签损失集成到倒角距离中，以鼓励标签一致的对应关系，并添加平滑正则化来稳定和改进变形场的学习。大量实验表明，与基线相比，Bi-PT具有准确且稳健的性能。
英文摘要
We propose Bi-PT, a pipeline for reconstructing 3D four-chamber human heart meshes from clinical sparsely sampled cardiac magnetic resonance imaging (CMR) data. This work addresses the error-prone generation of 3D cardiac shape from a sparse point cloud (SPC) extracted from 2D long-axis and short-axis views used in routine clinical CMR protocols. Bi-PT enables accurate inference of the four-chamber heart mesh from the SPC by learning robust point features via bidirectional point cross-attention between an atlas and the SPC, together with per-point semantic labels that improve correspondence estimation. We formulate the deformation field as a Neural Ordinary Differential Equation (NODE) parameterized by a per-point affine transformation and translation to deform the atlas toward the target heart shape. By learning such a NODE, we can guarantee the deformation field to be a locally affine diffeomorphic deformation. We also integrate a semantic label loss into the Chamfer distance to encourage label-consistent correspondences and add a smoothness regularization to stabilize and improve the learning of the deformation field. Extensive experiments demonstrate that Bi-PT achieves accurate and robust performance compared to baselines.
