标题： 基于扩散模型的心脏容积重构的潜在插值学习 显示英文标题

标题： Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction

摘要： 心脏磁共振（CMR）成像是诊断和管理心血管疾病的关键工具，但其效用常受限于二维短轴切片的稀疏采集，导致体积信息不完整。 从这些稀疏切片中准确的三维重建对于全面的心脏评估至关重要，但现有方法面临诸多挑战，包括依赖于预定义的插值方案（例如线性或球面），计算效率低下，以及依赖于额外的语义输入，如分割标签或运动数据。 为解决这些限制，我们提出了一种新颖的\textbf{钙}rdiac\textbf{L}atent\textbf{I}nterpolation\textbf{D}iffusion（CaLID）框架，该框架引入了三个关键创新。 首先，我们提出了一种基于扩散模型的数据驱动插值方案，可以捕捉稀疏切片之间的复杂非线性关系，并提高重建准确性。 其次，我们设计了一种计算高效的的方法，在潜在空间中运行，并将整个心脏的三维上采样时间加快了24倍，与之前的方法相比减少了计算开销。 第三，仅以稀疏的二维CMR图像作为输入，我们的方法在基线方法中实现了最先进（SOTA）的性能，消除了对辅助输入（如形态学指导）的需求，从而简化了工作流程。 我们进一步将方法扩展到二维+T数据，使能够有效建模时空动态并确保时间一致性。 广泛的体积评估和下游分割任务表明，CaLID实现了优越的重建质量和效率。 通过解决现有方法的基本限制，我们的框架推进了时空整体心脏重建的最先进水平，为心血管成像提供了一个强大且临床实用的解决方案。 显示英文摘要

摘要： Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D short-axis slices, resulting in incomplete volumetric information. Accurate 3D reconstruction from these sparse slices is essential for comprehensive cardiac assessment, but existing methods face challenges, including reliance on predefined interpolation schemes (e.g., linear or spherical), computational inefficiency, and dependence on additional semantic inputs such as segmentation labels or motion data. To address these limitations, we propose a novel \textbf{Ca}rdiac \textbf{L}atent \textbf{I}nterpolation \textbf{D}iffusion (CaLID) framework that introduces three key innovations. First, we present a data-driven interpolation scheme based on diffusion models, which can capture complex, non-linear relationships between sparse slices and improves reconstruction accuracy. Second, we design a computationally efficient method that operates in the latent space and speeds up 3D whole-heart upsampling time by a factor of 24, reducing computational overhead compared to previous methods. Third, with only sparse 2D CMR images as input, our method achieves SOTA performance against baseline methods, eliminating the need for auxiliary input such as morphological guidance, thus simplifying workflows. We further extend our method to 2D+T data, enabling the effective modeling of spatiotemporal dynamics and ensuring temporal coherence. Extensive volumetric evaluations and downstream segmentation tasks demonstrate that CaLID achieves superior reconstruction quality and efficiency. By addressing the fundamental limitations of existing approaches, our framework advances the state of the art for spatio and spatiotemporal whole-heart reconstruction, offering a robust and clinically practical solution for cardiovascular imaging.