用稀疏心肌影像重建三维心脏模型，精度显著提升。
Bi-PT: Bidirectional Cross-Attention Point Transformers for Four-Chamber Heart Reconstruction from Sparse Cardiac MRI Data
- 双向点注意力融合解剖图谱与稀疏点云，增强对应关系建模。
- 通过神经常微分方程实现局部仿射保形变形，保证形状合理性。
- 适合临床心脏影像重建，尤其适用于扫描数据有限的场景。
我们提出Bi-PT，一种从临床稀疏采样心脏磁共振（CMR）数据中重建三维四腔心脏网格的流程。该工作解决从常规临床CMR中2D长轴和短轴视图提取的稀疏点云（SPC）生成3D心脏形状时误差大的问题。Bi-PT通过在图谱与SPC之间学习双向点交叉注意力，提取鲁棒点特征，并结合逐点语义标签以改善对应估计。将形变场建模为由逐点仿射变换和位移参数化的神经常微分方程（NODE），实现从图谱到目标心脏形状的形变。学习该NODE可保证形变场为局部仿射微分同胚。同时，在Chamfer距离中引入语义标签损失以促进标签一致的对应关系，并添加平滑正则项以稳定并优化形变场学习。大量实验表明，相比基线方法，Bi-PT在准确性和鲁棒性上均有显著提升。
原文摘要 · Abstract (English)
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.
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