发表机构
University of Virginia（弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出运动空间生成布朗桥扩散模型，以标准CMR图像为条件学习运动映射，在多中心CMR数据集上验证其可提升应变分析准确性，为开发临床可用的低成本心脏评估AI工具提供新范式。
AI 中文摘要
心脏磁共振（CMR）图像的心肌应变分析是评估心脏功能的重要工具。然而，现有技术要么需要人工调整后处理，区域精度欠佳；要么需要专门且成本高昂的成像采集。本文提出利用生成模型从常规采集的CMR序列合成高质量的运动衍生应变值。具体而言，我们开发了一种新颖的运动空间布朗桥扩散模型，用于学习通过广泛采用的配准方法估计的标准CMR运动与高级应变成像技术提供的高精度运动之间的概率映射。为了在生成过程中提升解剖结构的保真度，我们的模型以对应的CMR图像为条件。我们在包含配对标准电影CMR和高级应变成像采集受试者的大规模多中心CMR数据集上验证了该方法。实验结果表明，与现有基于学习的方法相比，我们的框架显著提升了从标准CMR进行运动预测和应变分析的准确性。我们的研究为在繁忙的临床工作流程中开发具有增强应变准确性、成本效益高且可临床部署的心脏功能评估AI工具提供了新范式。我们的代码可在该http URL获取。
英文摘要
Myocardial strain analysis of cardiac magnetic resonance (CMR) images provides an important tool for evaluating cardiac function. However, current techniques require either human-adjusted post-processing with suboptimal regional accuracy, or specialized acquisitions with limited availability. In this paper, we propose to leverage the power of generative models to synthesize high-quality motion-derived strain values from routinely acquired CMR sequences. Specifically, we develop a novel Brownian bridge diffusion model in motion space to learn the probabilistic mapping between standard CMR motion estimated from widely adopted registration methods and highly accurate motion provided by advanced strain imaging techniques. To promote the fidelity of anatomical structure in the generation process, our model is conditioned on the corresponding CMR images. We validate our method on large-scale multi-center CMR datasets including subjects of paired standard cine CMR and advanced strain imaging acquisitions. Experimental results demonstrate that our framework significantly improves the accuracy of motion prediction and strain analysis from standard CMRs compared to existing learning-based approaches. Our research represents a new paradigm for potentially developing cost-effective, clinically deployable AI tools for cardiac function assessment with enhanced strain accuracy in busy clinical workflows. Our code is publicly available at https://github.com/Rishov-MIA/Brownian-Bridge-strain-analysis.
Comments12 pages
