用深度自编码器从稀疏血流数据重建心脏内三维血流，助力左心室辅助装置患者评估。
LVADNet3D: A Deep Autoencoder for Reconstructing 3D Intraventricular Flow from Sparse Hemodynamic Data
- 设计3D卷积自编码器，融合混合下采样与深层结构提升空间流场捕捉能力。
- 在多种输入条件下，重建误差更低，峰值信噪比（PSNR）显著优于基线模型。
- 适用于临床血流评估，尤其适合缺乏高质量影像数据的LVAD患者群体。
准确评估左心室辅助装置（LVAD）支持患者的心室内血流对判断血流动力学状态至关重要。然而，临床影像要么与LVAD不兼容，要么产生稀疏且低质量的速度数据。虽然计算流体动力学（CFD）模拟可提供高保真数据，但计算成本过高，难以用于常规临床实践。为此，我们提出LVADNet3D，一种3D卷积自编码器，能够从稀疏速度向量输入中重建全分辨率心室内速度场。相比标准的UNet3D模型，LVADNet3D引入混合下采样和更深的编码-解码架构，并增加通道容量，以更好捕捉空间流场模式。为训练与评估模型，我们利用CFD模拟生成了支持LVAD的心脏内高分辨率血流合成数据集。同时研究了基于解剖与生理先验条件对模型的影响。在多种输入配置下，LVADNet3D均优于基线UNet3D模型，表现出更低的重建误差和更高的峰值信噪比（PSNR）。
原文摘要 · Abstract (English)
Accurate assessment of intraventricular blood flow is essential for evaluating hemodynamic conditions in patients supported by Left Ventricular Assist Devices (LVADs). However, clinical imaging is either incompatible with LVADs or yields sparse, low-quality velocity data. While Computational Fluid Dynamics (CFD) simulations provide high-fidelity data, they are computationally intensive and impractical for routine clinical use. To address this, we propose LVADNet3D, a 3D convolutional autoencoder that reconstructs full-resolution intraventricular velocity fields from sparse velocity vector inputs. In contrast to a standard UNet3D model, LVADNet3D incorporates hybrid downsampling and a deeper encoder-decoder architecture with increased channel capacity to better capture spatial flow patterns. To train and evaluate the models, we generate a high-resolution synthetic dataset of intraventricular blood flow in LVAD-supported hearts using CFD simulations. We also investigate the effect of conditioning the models on anatomical and physiological priors. Across various input configurations, LVADNet3D outperforms the baseline UNet3D model, yielding lower reconstruction error and higher PSNR results.
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