Motion and deformation analysis of cardiac magnetic resonance (CMR) imaging
videos is crucial for assessing myocardial strain of patients with abnormal
heart functions. Recent advances in deep learning-based image registration
algorithms have shown promising results in predicting motion fields from
routinely acquired CMR sequences. However, their accuracy often diminishes in
regions with subtle appearance change, with errors propagating over time.
Advanced imaging techniques, such as displacement encoding with stimulated
echoes (DENSE) CMR, offer highly accurate and reproducible motion data but
require additional image acquisition, which poses challenges in busy clinical
flows. In this paper, we introduce a novel Latent Motion Diffusion model
(LaMoD) to predict highly accurate DENSE motions from standard CMR videos. More
specifically, our method first employs an encoder from a pre-trained
registration network that learns latent motion features (also considered as
deformation-based shape features) from image sequences. Supervised by the
ground-truth motion provided by DENSE, LaMoD then leverages a probabilistic
latent diffusion model to reconstruct accurate motion from these extracted
features. Experimental results demonstrate that our proposed method, LaMoD,
significantly improves the accuracy of motion analysis in standard CMR images;
hence improving myocardial strain analysis in clinical settings for cardiac
patients. Our code will be publicly available on upon acceptance.

采用基于深度学习的算法进行心脏磁共振成像视频的运动和变形分析对于评估心功能异常的患者的心肌应变至关重要。本文介绍了一种新颖的潜在运动扩散模型（LaMoD），通过从标准心脏磁共振视频中提取的潜在运动特征，利用概率性潜在扩散模型重构准确的运动，从而显著提高了标准心脏磁共振图像中运动分析的准确性，从而改进了临床环境中心脏患者的心肌应变分析。

LaMoD: 隐含运动扩散模型用于心肌应变生成

LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation

Spatio-temporal coherency is a major challenge in synthesizing high quality
videos, particularly in synthesizing human videos that contain rich global and
local deformations. To resolve this challenge, previous approaches have
resorted to different features in the generation process aimed at representing
appearance and motion. However, in the absence of strict mechanisms to
guarantee such disentanglement, a separation of motion from appearance has
remained challenging, resulting in spatial distortions and temporal jittering
that break the spatio-temporal coherency. Motivated by this, we here propose
LEO, a novel framework for human video synthesis, placing emphasis on
spatio-temporal coherency. Our key idea is to represent motion as a sequence of
flow maps in the generation process, which inherently isolate motion from
appearance. We implement this idea via a flow-based image animator and a Latent
Motion Diffusion Model (LMDM). The former bridges a space of motion codes with
the space of flow maps, and synthesizes video frames in a warp-and-inpaint
manner. LMDM learns to capture motion prior in the training data by
synthesizing sequences of motion codes. Extensive quantitative and qualitative
analysis suggests that LEO significantly improves coherent synthesis of human
videos over previous methods on the datasets TaichiHD, FaceForensics and
CelebV-HQ. In addition, the effective disentanglement of appearance and motion
in LEO allows for two additional tasks, namely infinite-length human video
synthesis, as well as content-preserving video editing.

提出了一种名为 LEO 的新颖方法，通过将运动表示为一系列流场图，在生成过程中固有地分离运动和外貌，从而强调时空一致性，并取得了比以前方法更好的人类视频合成效果。