Beetz Marcel, Banerjee Abhirup, Ossenberg-Engels Julius, Grau Vicente
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK.
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.
Med Image Anal. 2023 Dec;90:102975. doi: 10.1016/j.media.2023.102975. Epub 2023 Sep 23.
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.
电影磁共振成像（MRI）是目前评估心脏解剖结构和功能的金标准。然而，它通常只获取心脏潜在三维（3D）解剖结构的一组二维（2D）切片，从而限制了对健康和病理性心脏形态学与生理学的理解和分析。在本文中，我们提出了一种新颖的全自动表面重建流程，能够从原始电影MRI采集中重建多类3D心脏解剖网格。其关键组件是一个多类点云补全网络（PCCN），能够在统一模型中纠正3D重建任务的稀疏性和错位问题。我们首先在一个大型双心室解剖结构合成数据集上评估PCCN，观察到在多层切片错位情况下，重建解剖结构与金标准解剖结构之间的倒角距离低于或类似于基础图像分辨率。此外，我们发现与基准3D U-Net相比，在豪斯多夫距离和平均表面距离方面，重建误差分别降低了32%和24%。然后，我们将PCCN作为自动重建流程的一部分，应用于英国生物银行研究中的1000名受试者的跨域转移设置中，并证明其能够重建准确且拓扑合理的双心室心脏网格，临床指标与先前文献相当。最后，我们研究了我们提出的方法的稳健性，并观察到其成功处理多种常见异常情况的能力。
