Zakeri Arezoo, Hokmabadi Alireza, Bi Ning, Wijesinghe Isuru, Nix Michael G, Petersen Steffen E, Frangi Alejandro F, Taylor Zeike A, Gooya Ali
Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, UK.
Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, UK.
Med Image Anal. 2023 Jan;83:102678. doi: 10.1016/j.media.2022.102678. Epub 2022 Nov 2.
Deformable image registration (DIR) can be used to track cardiac motion. Conventional DIR algorithms aim to establish a dense and non-linear correspondence between independent pairs of images. They are, nevertheless, computationally intensive and do not consider temporal dependencies to regulate the estimated motion in a cardiac cycle. In this paper, leveraging deep learning methods, we formulate a novel hierarchical probabilistic model, termed DragNet, for fast and reliable spatio-temporal registration in cine cardiac magnetic resonance (CMR) images and for generating synthetic heart motion sequences. DragNet is a variational inference framework, which takes an image from the sequence in combination with the hidden states of a recurrent neural network (RNN) as inputs to an inference network per time step. As part of this framework, we condition the prior probability of the latent variables on the hidden states of the RNN utilised to capture temporal dependencies. We further condition the posterior of the motion field on a latent variable from hierarchy and features from the moving image. Subsequently, the RNN updates the hidden state variables based on the feature maps of the fixed image and the latent variables. Different from traditional methods, DragNet performs registration on unseen sequences in a forward pass, which significantly expedites the registration process. Besides, DragNet enables generating a large number of realistic synthetic image sequences given only one frame, where the corresponding deformations are also retrieved. The probabilistic framework allows for computing spatio-temporal uncertainties in the estimated motion fields. Our results show that DragNet performance is comparable with state-of-the-art methods in terms of registration accuracy, with the advantage of offering analytical pixel-wise motion uncertainty estimation across a cardiac cycle and being a motion generator. We will make our code publicly available.
可变形图像配准（DIR）可用于跟踪心脏运动。传统的DIR算法旨在在独立的图像对之间建立密集的非线性对应关系。然而，它们计算量很大，并且不考虑时间依赖性来调节心动周期中的估计运动。在本文中，我们利用深度学习方法，构建了一种新颖的分层概率模型，称为DragNet，用于快速可靠地对心脏磁共振成像（CMR）电影图像进行时空配准，并生成合成心脏运动序列。DragNet是一个变分推理框架，它将序列中的一幅图像与递归神经网络（RNN）的隐藏状态相结合，作为每个时间步推理网络的输入。作为该框架的一部分，我们根据用于捕获时间依赖性的RNN的隐藏状态来设定潜在变量的先验概率。我们进一步根据层次结构中的一个潜在变量和运动图像的特征来设定运动场的后验概率。随后，RNN根据固定图像的特征图和潜在变量更新隐藏状态变量。与传统方法不同，DragNet在前向传播中对未见过的序列进行配准，这显著加快了配准过程。此外，DragNet仅给定一帧就能生成大量逼真的合成图像序列，同时还能检索相应的变形。概率框架允许计算估计运动场中的时空不确定性。我们的结果表明，DragNet在配准精度方面与现有方法相当，其优势在于能够在整个心动周期内提供解析的逐像素运动不确定性估计，并且是一个运动生成器。我们将公开我们的代码。
