3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space, which ignore the fact that motion estimation is mainly relevant and useful within the object of interest, e.g., the heart. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, the method is able to leverage the anatomical shape information from 2D multi-view CMR images for 3D motion estimation. The differentiability of the rasterizer enables us to train the method end-to-end. One advantage of the proposed method is that by tracking the motion of each vertex, it is able to keep the vertex correspondence of 3D meshes between time frames, which is important for quantitative assessment of the cardiac function on the mesh. We evaluate the proposed method on CMR images acquired from the UK Biobank study. Experimental results show that the proposed method quantitatively and qualitatively outperforms both conventional and learning-based cardiac motion tracking methods.

翻译：从心心磁共振成像(CMR)图像中进行3D运动估计对于评估心脏功能和诊断心血管疾病非常重要。以往方法大多侧重于在全图像空间中估算像素/Voxel-with运动场,忽视了这样的事实,即运动估计在感兴趣的对象(例如心脏)中主要相关和有用。在这项工作中,我们将心脏建模为3D几何网格,并提出了一个基于深层次学习的新方法,该方法能够从2D短轴和长轴CMR图像中估计心脏3D运动。通过开发一个不同的网格到模拟光学,该方法能够利用2D多视CMR图像中的解剖形状信息来进行3D运动估计。拉斯特瑞能使我们能够将方法的终端训练。拟议方法的一个优点是,通过跟踪每个脊椎的动作,它能够将3DMES的垂直对时间框进行三维成像的反向通信。这是对2D多角度-模拟模拟模拟光学模型进行定量评估的重要,这是在英国的常规研究中,我们所获取的磁力学方法。我们所学的硬化的硬体研究方法显示。