用扩散模型将低质量心脏影像转为高清动态影像
Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
- 通过运动特征引导的扩散模型，实现无配对影像转换
- 在心脏数据集上优于现有方法，合成影像更真实清晰
- 适合心血管影像分析与临床辅助诊断研究者
本文提出一种新型无配对视频到视频翻译方法（MFD-V2V），旨在将低对比度、含伪影的位移编码刺激回波（DENSE）心脏磁共振影像，转化为高对比度、动态清晰的心脏电影（cine）CMR影像。首先，设计一个潜在时序多注意力（LTMA）配准网络，从心电影影像中有效学习更准确一致的心脏运动信息。随后构建多层级运动特征引导的扩散模型，配备专门的时空运动编码器（STME），提取细粒度运动条件以提升合成质量与保真度。在完整心脏数据集上的评估显示，MFD-V2V在定量指标和定性视觉评价上均优于当前最优方法。此外，合成的心电影影像显著提升了下游临床与分析任务表现，凸显本方法的广泛价值。代码已公开于 https://github.com/SwaksharDeb/MFD-V2V。
原文摘要 · Abstract (English)
This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos. A multi-level motion feature guided diffusion model, equipped with a specialized Spatio-Temporal Motion Encoder (STME) to extract fine-grained motion conditioning, is then developed to improve synthesis quality and fidelity. We evaluate our method, MFD-V2V, on a comprehensive cardiac dataset, demonstrating superior performance over the state-of-the-art in both quantitative metrics and qualitative assessments. Furthermore, we show the benefits of our synthesized cine CMRs improving downstream clinical and analytical tasks, underscoring the broader impact of our approach. Our code is publicly available at https://github.com/SwaksharDeb/MFD-V2V.
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