This paper presents a novel approach, termed {\em Temporal Latent Residual Network (TLRN)}, to predict a sequence of deformation fields in time-series image registration. The challenge of registering time-series images often lies in the occurrence of large motions, especially when images differ significantly from a reference (e.g., the start of a cardiac cycle compared to the peak stretching phase). To achieve accurate and robust registration results, we leverage the nature of motion continuity and exploit the temporal smoothness in consecutive image frames. Our proposed TLRN highlights a temporal residual network with residual blocks carefully designed in latent deformation spaces, which are parameterized by time-sequential initial velocity fields. We treat a sequence of residual blocks over time as a dynamic training system, where each block is designed to learn the residual function between desired deformation features and current input accumulated from previous time frames. We validate the effectivenss of TLRN on both synthetic data and real-world cine cardiac magnetic resonance (CMR) image videos. Our experimental results shows that TLRN is able to achieve substantially improved registration accuracy compared to the state-of-the-art. Our code is publicly available at https://github.com/nellie689/TLRN.

翻译：本文提出了一种新颖的方法，称为{\em 时序潜在残差网络（TLRN）}，用于预测时序图像配准中的一系列变形场。时序图像配准的挑战通常在于存在大范围运动，尤其是当图像与参考图像（例如，心脏周期起始阶段与峰值拉伸阶段相比）存在显著差异时。为实现准确且鲁棒的配准结果，我们利用运动连续性的本质，并挖掘连续图像帧间的时序平滑性。我们提出的TLRN强调了一个在潜在变形空间中精心设计残差块的时序残差网络，这些空间由时序初始速度场参数化。我们将随时间推移的一系列残差块视为一个动态训练系统，其中每个块旨在学习期望变形特征与从先前时间帧累积的当前输入之间的残差函数。我们在合成数据以及真实世界的心脏电影磁共振（CMR）图像视频上验证了TLRN的有效性。实验结果表明，与现有最先进方法相比，TLRN能够显著提高配准精度。我们的代码公开在 https://github.com/nellie689/TLRN。