发表机构
King’s College London; Guys and St Thomas’ NHS Foundation Trust（伦敦国王学院; 盖伊和圣托马斯国民保健信托基金会）
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SPARC流水线，结合物理信息SVR与DL模型辅助，实现胎儿心脏MRI自动重建，缩短时间、提升质量，适配临床部署且公开可用。
AI 中文摘要
胎儿心脏MRI（fCMR）提供了与超声心动图互补的有价值诊断信息，尤其针对复杂先天性心脏病（CHD）。动态电影成像捕捉心脏运动，对心脏功能评估至关重要；然而，从2D+时间采集的切片重建3D+时间电影体积仍具挑战性，原因在于不可预测的胎儿运动，以及缺乏适用于临床部署的自动化且稳健的处理工具。我们提出SPARC流水线（Slice-to-volume Pipeline for Automated Reconstruction of gated 3D+time fetal Cardiac MRI），其结合了物理信息的切片到体积重建（SVR），该重建基于多普勒超声（DUS）门控切片堆栈，并由深度学习（DL）模型辅助进行胸部分割和解剖学重定向。所提出的SVR算法相较于现有逐帧方法，重建时间减少了10倍（4.8±1.0分钟对比49.0±14.1分钟，p<0.0001），同时提升了重建质量。采用集成聚合的胸部分割性能超过了评分者间一致性（Dice系数84.7±3.9%对比81.4±7.7%，p<0.05），而解剖学重定向的成功率达到90.1%。对大型保留的临床队列（n=121）进行的端到端评估显示，82.6%的病例实现了全自动处理，平均运行时间为7.1±1.3分钟，可适配临床部署。完整的SPARC流水线作为Docker容器公开提供，当前在我们机构作为临床研究工具部署。
英文摘要
Fetal cardiac MRI (fCMR) provides valuable diagnostic information complementary to echocardiography, particularly for complex congenital heart disease (CHD). Dynamic cine imaging captures cardiac motion essential for assessment of cardiac function; however, the reconstruction of 3D+time cine volumes from 2D+time acquired slices remains challenging due to unpredictable fetal motion and the absence of automated and robust processing tools suitable for clinical deployment. We present the SPARC pipeline (Slice-to-volume Pipeline for Automated Reconstruction of gated 3D+time fetal Cardiac MRI) which combines physics-informed slice-to-volume reconstruction (SVR) of Doppler ultrasound (DUS) gated stacks of slices, assisted by deep learning (DL) models for thoracic segmentation and anatomical reorientation. The proposed SVR algorithm achieves a tenfold reduction in reconstruction time relative to existing frame-wise approaches ($4.8 \pm 1.0$ vs $49.0 \pm 14.1$ min, $p < 0.0001$) while improving the reconstruction quality. Thoracic segmentation performance using ensemble aggregation exceeded inter-rater agreement (Dice $84.7 \pm 3.9\%$ vs $81.4 \pm 7.7\%$, $p<0.05$), while anatomical reorientation achieved a success rate of $90.1\%$. End-to-end evaluation on a large held-out clinical cohort ($n = 121$) demonstrated fully automatic processing in $82.6\%$ of cases with a mean runtime of $7.1 \pm 1.3$ min, compatible with clinical deployment. The complete SPARC pipeline is publicly available as a Docker container https://hub.docker.com/r/aboutill/sparc and is currently deployed at our institution as a clinical research tool.
