发表机构
Johns Hopkins University; Massachusetts General Hospital and Harvard Medical School（约翰斯·霍普金斯大学; 马萨诸塞总医院和哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PhaseFlow3D提出一种无需心电图的生成框架，从单个舒张末期三维容积合成完整4D心脏电影序列，通过分段线性相位和径向收缩分解实现，在ACDC和M&Ms基准上取得最优射血分数误差和分布质量。
AI 中文摘要
电影心血管磁共振（CMR）将心动周期捕获为四维（4D）序列，但标准采集需要心电图（ECG）门控和多次屏气。仅凭视觉真实性并不能确定准确的患者特异性射血分数（EF）或心室容积。我们提出PhaseFlow3D，一种生成框架，可从单个舒张末期（ED）三维（3D）容积合成完整的4D电影序列，无需ECG。为捕捉不对称的收缩期和舒张期动力学，它将心动周期表示为以ED和收缩末期（ES）时间点锚定的分段线性相位。在推理时，群体级规范模板提供该相位，无需患者特异性时间信息。相位条件化的整流流模型在潜空间生成心脏运动轨迹。径向收缩分解将每个潜状态转换为3D位移场，结合基于物理的径向分量（用于向心性心肌收缩）和图像条件化的残差（用于旋转和平面外运动）。每帧通过直接扭曲ED容积生成，绕过变分自编码器解码。在ACDC和M&Ms组合基准上，PhaseFlow3D实现了最低的EF平均绝对误差，唯一正的左心室容积曲线$R^2$，以及所比较方法中最佳的分布质量。消融实验确认了每个组件的贡献。下游评估证明了合成序列和位移场在分割、病理分类、标签传播和心肌应变分析中的实用性。
英文摘要
Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electrocardiogram (ECG) gating and repeated breath holds. Visual realism alone does not establish accurate patient-specific ejection fraction (EF) or ventricular volumes. We present PhaseFlow3D, a generative framework that synthesizes a complete 4D cine sequence from a single end-diastolic (ED) three-dimensional (3D) volume without ECG. To capture asymmetric systolic and diastolic dynamics, it represents the cardiac cycle as a piecewise linear phase anchored at ED and end-systolic (ES) time points. At inference, a population-level canonical template supplies this phase without patient-specific temporal information. A phase-conditioned rectified flow model generates a cardiac motion trajectory in latent space. Radial Contraction Decomposition converts each latent state into a 3D displacement field, combining a physics-informed radial component for centripetal myocardial contraction with an image-conditioned residual for rotation and out-of-plane motion. Each frame is generated by directly warping the ED volume, bypassing variational autoencoder decoding. On the combined ACDC and M&Ms benchmark, PhaseFlow3D achieves the lowest EF mean absolute error, the only positive left-ventricular volume-curve $R^2$, and the best distributional quality among compared methods. Ablations confirm each component's contribution. Downstream evaluations demonstrate the utility of the synthesized sequences and displacement fields for segmentation, pathology classification, label propagation, and myocardial strain analysis.
