arXiv 2309.143062023-09-25eess.IVcs.CV

DeepMesh：使用深度学习的基于网格的心脏运动追踪

DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning

Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；MRC London Institute of Medical Sciences（英国医学研究理事会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河畔临床教学中心）

Qingjie Meng, Wenjia Bai, Declan P O'Regan, and Daniel Rueckert

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AI总结：

本文提出 DeepMesh 学习框架，利用多视图二维 CMR 图像重建个体心脏三维网格并估计逐顶点运动场，在 UK Biobank 上优于现有图像与网格类方法。

摘要：

从电影心脏磁共振（CMR）图像进行三维运动估计，对于评估心功能和诊断心血管疾病十分重要。当前最先进的方法集中于在图像空间中估计稠密的像素级/体素级运动场，这忽略了一个事实：运动估计只有在感兴趣的解剖对象（例如心脏）内部才是相关且有用的。在本工作中，我们将心脏建模为由心外膜和心内膜表面组成的三维网格。我们提出了一种新颖的学习框架 DeepMesh，该框架将模板心脏网格传播到受试者空间，并根据个体受试者的 CMR 图像估计心脏网格的三维运动。在 DeepMesh 中，首先根据模板网格重建个体受试者舒张末期帧的心脏网格。随后，根据二维短轴和长轴 CMR 图像估计相对于舒张末期帧的、基于网格的三维运动场。通过开发可微的网格到图像光栅化器，DeepMesh 能够利用来自多个解剖视图的二维形状信息来进行三维网格重建和网格运动估计。所提出的方法估计逐顶点位移，从而保持各时间帧之间的顶点对应关系，这对于在不同受试者和人群之间定量评估心功能非常重要。我们在从 UK Biobank 获取的 CMR 图像上评估了 DeepMesh。本工作聚焦于左心室的三维运动估计。实验结果表明，所提出的方法在定量和定性方面均优于其他基于图像和基于网格的心脏运动追踪方法。

英文摘要：

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an individual subject is first reconstructed from the template mesh. Mesh-based 3D motion fields with respect to the end-diastolic frame are then estimated from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information from multiple anatomical views for 3D mesh reconstruction and mesh motion estimation. The proposed method estimates vertex-wise displacement and thus maintains vertex correspondences between time frames, which is important for the quantitative assessment of cardiac function across different subjects and populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank. We focus on 3D motion estimation of the left ventricle in this work. Experimental results show that the proposed method quantitatively and qualitatively outperforms other image-based and mesh-based cardiac motion tracking methods.

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