使用串联2D实时成像和深度学习的高分辨率各向同性3D Cine成像与自动分割
High Resolution Isotropic 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning
AI总结：
本研究提出一种串联2D自由呼吸实时cine图像并利用四个深度学习模型进行对比度/运动校正、超分辨率及分割的方法，快速生成高分辨率各向同性3D cine数据，在缩短采集重建时间的同时保持了与常规成像一致的定量精度。
摘要：
背景：传统的心血管磁共振（CMR）在儿科和先天性心脏病中，使用2D屏息平衡稳态自由进动（bSSFP）cine成像来评估功能，并使用心电门控、呼吸导航的静态3D bSSFP全心成像进行解剖评估。我们的目标是串联一组2D自由呼吸实时cine图像，并使用深度学习（DL）从这些图像创建一个各向同性的、完全分割的3D cine数据集。方法：在开源数据上训练了四个DL模型，分别执行：a) 层间对比度校正；b) 层间呼吸运动校正；c) 超分辨率（切片方向）；d) 右心房和左心房及心室（RA、LA、RV和LV）、胸主动脉（Ao）及肺动脉（PA）的分割。在10名接受常规心血管检查的患者中，我们的方法在前瞻性采集的矢状位实时cine图像堆栈上进行了验证。将3D cine的定量指标（心室容积和血管直径）及图像质量与常规屏息cine和全心成像进行了比较。结果：所有实时数据均成功转换为3D cine，所有病例的总后处理时间<1分钟。所有LV或RV指标均无显著偏差，且具有合理的一致性界限和相关性。所有血管直径也有合理的一致性，尽管RPA直径存在较小但显著的高估。结论：我们展示了使用一系列DL模型从串联的2D实时cine图像创建3D cine数据的潜力。我们的方法具有较短的采集和重建时间，完全分割的数据可在2分钟内获取。与常规成像的良好一致性表明，我们的方法有助于在临床实践中显著加快CMR。
英文摘要：
Background: Conventional cardiovascular magnetic resonance (CMR) in paediatric and congenital heart disease uses 2D, breath-hold, balanced steady state free precession (bSSFP) cine imaging for assessment of function and cardiac-gated, respiratory-navigated, static 3D bSSFP whole-heart imaging for anatomical assessment. Our aim is to concatenate a stack 2D free-breathing real-time cines and use Deep Learning (DL) to create an isotropic a fully segmented 3D cine dataset from these images. Methods: Four DL models were trained on open-source data that performed: a) Interslice contrast correction; b) Interslice respiratory motion correction; c) Super-resolution (slice direction); and d) Segmentation of right and left atria and ventricles (RA, LA, RV, and LV), thoracic aorta (Ao) and pulmonary arteries (PA). In 10 patients undergoing routine cardiovascular examination, our method was validated on prospectively acquired sagittal stacks of real-time cine images. Quantitative metrics (ventricular volumes and vessel diameters) and image quality of the 3D cines were compared to conventional breath hold cine and whole heart imaging. Results: All real-time data were successfully transformed into 3D cines with a total post-processing time of <1 min in all cases. There were no significant biases in any LV or RV metrics with reasonable limits of agreement and correlation. There is also reasonable agreement for all vessel diameters, although there was a small but significant overestimation of RPA diameter. Conclusion: We have demonstrated the potential of creating a 3D-cine data from concatenated 2D real-time cine images using a series of DL models. Our method has short acquisition and reconstruction times with fully segmented data being available within 2 minutes. The good agreement with conventional imaging suggests that our method could help to significantly speed up CMR in clinical practice.
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