BACKGROUND Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. METHODS A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. RESULTS Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25). CONCLUSION Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.

中文翻译：

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机器学习派生的相速度编码心血管磁共振分段，用于全自动主动脉流量量化。


背景技术相衬（PC）心血管磁共振（CMR）广泛用于流量量化，但分析通常需要耗时的手动分割，这可能需要人工校正。机器学习的进步显着改善了自动化处理，但尚未应用于 PC-CMR。这项研究测试了一种新颖的机器学习模型，用于全自动分析 PC-CMR 主动脉血流。方法 设计机器学习模型来基于神经网络方法跟踪主动脉瓣边界。该模型在包含 150 名接受临床 PC-CMR 的患者的衍生队列中进行训练，然后与前瞻性验证队列中的手动和市售自动分割进行比较。在从不同站点/CMR 供应商获取的外部队列中进行了进一步的验证测试。结果 在 190 名冠状动脉疾病患者中，前瞻性地在商用扫描仪上接受 CMR（84% 1.5T，16% 3T），机器学习分割一致成功，无需人工干预：分割时间 < 0.01 分钟/例（整个数据集为 1.2 分钟） ）；手动分割需要 3.96 ± 0.36 分钟/案例（整个数据集需要 12.5 小时）。机器学习和手动分割导出的流程之间的相关性接近统一（r = 0.99，p < 0.001）。与商业自动化相比，机器学习通过手动分割产生的绝对差异更小（1.85 ± 1.80 vs. 3.33 ± 3.18 mL，p < 0.01）：几乎所有（98%）的案例在机器学习和手动方法之间的差异≤5 mL。在没有晚期二尖瓣反流的患者中，机器学习相关性良好（r = 0.63，p < 0.001），并且与电影 CMR 心搏量存在微小差异（Δ 1.3 ± 17.7 mL，p = 0.36）。 在晚期二尖瓣反流患者中，机器学习产生的每搏输出量比容积电影 CMR 低（Δ 12.6 ± 20.9 mL，p = 0.005），进一步支持了该方法的有效性。在使用不同 CMR 供应商获得的外部验证队列 (n = 80) 中，该算法与手动分割产生了相当小的差异 (Δ 1.39 ± 1.77 mL，p = 0.4) 和高相关性 (r = 0.99，p < 0.001)，包括 20 名二尖瓣或狭窄主动脉瓣病变患者的类似结果（Δ 1.71 ± 2.25 mL，p = 0.25）。结论 全自动机器学习 PC-CMR 分割在主动脉流量量化方面表现强劲 - 产生快速分割，与手动分割差异较小，并在伴随二尖瓣反流的情况下识别差异前向/左心室每搏输出量。研究结果支持使用机器学习来分析大规模 CMR 数据集。