Blansit Kevin, Retson Tara, Masutani Evan, Bahrami Naeim, Hsiao Albert
Department of Biomedical Informatics (K.B., A.H.), Department of Radiology (T.R., N.B., A.H.), Department of Bioengineering (E.M.), and School of Medicine (E.M.), University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093.
Radiol Artif Intell. 2019 Nov 27;1(6):e180069. doi: 10.1148/ryai.2019180069.
To develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.
Annotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.
On LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.
DL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019
开发并评估一种基于深度学习（DL）对关键解剖标志进行定位的系统，用于心脏磁共振成像（MRI）成像平面的规划。
回顾性收集2012年2月至2017年6月期间来自心脏MR图像的892个长轴（LAX）和493个短轴（SAX）电影稳态自由进动序列上标注的标志。基于U-Net的热图回归用于心脏标志的定位，这些标志用于计算心脏MRI平面。通过比较DL预测结果与真实标注之间的定位距离和平面角度差异来评估性能。将DL得出的平面角度与技术人员在原始采集时规定的角度进行比较。数据分为80%用于训练，20%用于测试，并通过五重交叉验证来确认结果。
在LAX图像上，DL定位心尖的平均误差为12.56 mm±19.11（标准差），二尖瓣（MV）的定位误差为7.68 mm±6.91。在SAX图像上，DL定位主动脉瓣的误差为5.78 mm±5.68，MV为5.90 mm±5.24，肺动脉瓣为6.55 mm±6.39，三尖瓣为6.39 mm±5.89。基于这些定位，DL预测的成像平面相对于真实标注的平均角度偏差和平均误差如下：SAX，-1.27°±6.81和4.93°±4.86；四腔心，0.38°±6.45和5.16°±3.80；三腔心，0.13°±12.70和9.02°±8.83；两腔心，0.25°±9.08和6.53°±6.28。
基于DL的解剖定位是规划心脏MRI平面的一种可行策略。这种方法可以生成与由真实标志定义的成像平面平面相当平面相当的成像平面。©RSNA，2019
