Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.
Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.
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DOI：
10.1186/s12968-021-00712-9
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发表时间：
2021-03-11
期刊：
影响因子：
--
通讯作者：
Epstein FH
中科院分区：
文献类型：
--
作者：
Ghadimi S;Auger DA;Feng X;Sun C;Meyer CH;Bilchick KC;Cao JJ;Scott AD;Oshinski JN;Ennis DB;Epstein FH
心血管磁共振 (CMR) 受激回波电影位移编码 (DENSE) 通过将心肌位移编码到信号相位中来测量心脏运动，从而促进整体和节段心肌应变的高精度和可重复性，并为临床表现带来益处。虽然密集图像应变分析的传统方法比心肌标记的方法更快，但它们仍然需要用户手动协助。本研究开发并评估了用于全自动 DENSE 应变分析的深度学习方法。开发和训练卷积神经网络 (CNN) 的目的是 (a) 识别左心室 (LV) 心外膜和心内膜边界，(b) 识别前右心室 (RV)-LV 插入点，以及 (c) 执行相位展开。随后采用传统的自动步骤来计算应变。该网络使用来自 45 名健康受试者和 19 名心脏病患者的 12,415 幅短轴 DENSE 图像进行训练，并使用来自 25 名健康受试者和 19 名患者的 10,510 幅图像进行测试。对每个单独的 CNN 进行了评估，并将端到端全自动深度学习流程与使用线性相关性和周向应变的 Bland Altman 分析的传统用户辅助 DENSE 分析进行了比较。与专家手动进行左心室心肌分割相比，左心室心肌分割U-Nets的DICE相似系数为0.87±0.04，豪斯多夫距离为2.7±1.0像素，平均表面距离为0.41±0.29像素。与手动注释的数据相比，前 RV-LV 插入点在 1.38±0.9 像素内被检测到。对于具有典型信噪比 (SNR) 或低 SNR (p<0.05) 的图像，与传统路径跟踪方法相比，相位展开 U-Net 与地面实况数据相比具有相似或更低的均方误差。 Bland-Altman 分析显示，与传统的用户辅助方法相比，基于深度学习的全自动全局和分段收缩末期周向应变的偏差为 0.00±0.03，一致性限制为 −0.04 至 0.05 或更好。深度学习可实现 DENSE CMR 的全自动全局和分段周向应变分析，与传统的用户辅助方法具有良好的一致性。基于深度学习的自动应变分析可能有助于 DENSE 在临床上更广泛地使用，以量化心脏病患者的整体和节段应变。
Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis. Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain. LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p < 0.05), respectively. Bland–Altman analyses showed biases of 0.00 ± 0.03 and limits of agreement of − 0.04 to 0.05 or better for deep learning-based fully-automatic global and segmental end-systolic circumferential strain vs. conventional user-assisted methods. Deep learning enables fully-automatic global and segmental circumferential strain analysis of DENSE CMR providing excellent agreement with conventional user-assisted methods. Deep learning-based automatic strain analysis may facilitate greater clinical use of DENSE for the quantification of global and segmental strain in patients with cardiac disease.
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