LV-CARE-Diff：用于超稀疏电影切片左心室形状重建和功能量化的条件化解剖结构感知扩散模型
LV-CARE-Diff: A Conditional Anatomy-Aware Diffusion Model for Left Ventricular Shape Reconstruction and Function Quantification from Ultra-Sparse Cine Slices
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中文总结 AI 辅助
本研究提出LV-CARE-Diff模型，采用由粗到精策略，从超稀疏CMR电影切片重建左心室形状，保留96%功能量化准确率，减少73%成像时间，缩短检查时长且不牺牲定量精度。
中文摘要 AI 辅助
左心室功能量化是一项重要检查，通常使用心血管磁共振（CMR）电影成像进行。然而，传统CMR电影方案需要采集多个短轴（SAX）切片以覆盖整个左心室（LV），并辅以两个长轴（LAX）切片，这一过程耗时较长，会给无法耐受多次屏气的患者带来极大负担，限制了其在大规模早期筛查中的适用性。本研究开发了一种采用由粗到精策略的条件化解剖结构感知扩散模型（LV-CARE-Diff），旨在从超稀疏电影切片（即3个短轴和2个长轴切片）重建完整的左心室形状，以缩短CMR电影检查时间。LV-CARE-Diff采用3DUNet生成粗略初始形状，随后通过残差扩散模型进行细化；构建了包含成像平面方向和位置元数据的条件引导输入，以实现空间感知；并采用联合监督形状、功能和解剖结构的多目标训练策略，指导高保真重建。将LV-CARE-Diff与独立3DUNet、独立扩散模型及带扩散细化的3D UNet进行对比，测试结果显示所有深度学习模型均可稳健重建完整左心室形状，其中所提出的LV-CARE-Diff实现了最高的重建性能。从稀疏电影切片重建左心室形状的深度学习模型保留了96%的功能量化准确率，同时减少了73%的成像时间。本研究建立的LV-CARE-Diff框架可实现超稀疏电影采集，在不牺牲定量功能准确率的前提下缩短CMR检查时长。
英文摘要
Left ventricular functional quantification is an essential examination and is routinely performed using cardiovascular magnetic resonance (CMR) cine imaging. However, conventional CMR cine protocols require the acquisition of multiple short-axis (SAX) slices to cover the entire left ventricle (LV) along with two long-axis (LAX) slices, which is time-consuming and places a considerable burden on patients who are unable to sustain repeated breath-holds, limiting its suitability for large-scale early screening. In this study, a Conditional Anatomy-Aware Diffusion Model (LV-CARE-Diff) was developed using a coarse-to-fine strategy to reconstruct the complete LV shape from ultra-sparse cine slices, namely three short-axis and two long-axis slices, with the aim of accelerating CMR cine examination. LV-CARE-Diff employs a 3DUNet to generate a coarse initial shape, which is subsequently refined through a residual diffusion model. A condition-guided input incorporating imaging plane orientation and positional metadata was constructed to enable spatial awareness, and a multi-objective training strategy jointly supervising shape, function, and anatomy was incorporated to guide high-fidelity reconstruction. LV-CARE-Diff was compared against a standalone 3DUNet, a standalone diffusion model, and a 3D UNet with diffusion-based refinement. Testing results indicated that complete LV shape could be robustly reconstructed by all deep learning models, with the highest reconstruction performance achieved by the proposed LV-CARE-Diff. Deep learning models reconstructing LV shape from sparse cine slices preserved 96% of functional quantification accuracy while reducing imaging time by 73%. The LV-CARE-Diff framework established in this study enables ultra-sparse cine acquisition to shorten CMR examination duration without sacrificing quantitative functional accuracy.
