BACKGROUND Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping. METHODS A deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers. RESULTS The FCN achieved fast segmentation (< 0.3 s/image) with high DSC (0.85 ± 0.07). The automatically and manually calculated T1 values (1091 ± 59 ms and 1089 ± 59 ms, respectively) were highly correlated in per-patient (r = 0.82; slope = 1.01; p < 0.0001) and per-slice (r = 0.72; slope = 1.01; p < 0.0001) analyses. Bland-Altman analysis showed good agreement between the automated and manual measurements with 95% of measurements within the limits-of-agreement in both per-patient and per-slice analyses. The intraclass correllation of the T1 calculations by the automatic method vs reader 1 and reader 2 was respectively 0.86/0.56 and 0.74/0.49 in the per-patient/per-slice analyses, which were comparable to that between two expert readers (=0.72/0.58 in per-patient/per-slice analyses). CONCLUSION The proposed FCN-based image processing platform allows fast and automatic analysis of myocardial native T1 mapping images mitigating the burden and observer-related variability of manual analysis.

中文翻译：

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使用全卷积神经网络自动分析心血管磁共振心肌原生 T1 映射图像。


背景心血管磁共振（CMR）心肌天然T1标测允许评估间质弥漫性纤维化。在该技术中，通过在运动校正 T1 图中绘制感兴趣区域来手动测量全局和区域 T1。手动分析导致本已冗长的 CMR 分析工作流程，并影响测量的再现性。在本研究中，我们提出了一种将心肌分割、对齐和 T1 计算相结合的自动化方法，用于心肌 T1 映射。方法 使用深度全卷积神经网络 (FCN) 对 T1 加权图像中的心肌进行分割。然后在极坐标网格上对分段心肌进行重新采样，极坐标网格的原点位于分段心肌的质心。使用曲线拟合从重采样的 T1 加权图像重建心肌 T1 图。使用 210 名患者的手动分割图像（5 个切片，每个患者 11 次反转时间）对 FCN 进行了训练和测试。专家读者使用半自动工具分析了 455 名患者的附加图像数据集（每位患者 5 个切片和 11 次反转时间），用于验证自动计算的全局和区域 T1 值。 Bland-Altman 分析、Pearson 相关系数 r 和 Dice 相似系数 (DSC) 用于评估每个患者和每个切片的基于 FCN 的分析的性能。使用基于 FCN 的自动方法和两个读数器计算的 T1 值的组内相关系数 (ICC) 评估观察者间的变异性。结果 FCN 实现了快速分割（< 0.3 s/图像）和高 DSC (0.85 ± 0.07)。 自动和手动计算的 T1 值（分别为 1091 ± 59 ms 和 1089 ± 59 ms）在每位患者（r = 0.82；斜率 = 1.01；p < 0.0001）和每切片（r = 0.72；斜率）方面高度相关= 1.01；p < 0.0001) 分析。 Bland-Altman 分析显示，自动测量和手动测量之间具有良好的一致性，在每个患者和每个切片分析中，95% 的测量结果都在一致性限度内。在每个患者/每个切片分析中，自动方法与阅读器 1 和阅读器 2 的 T1 计算的组内相关性分别为 0.86/0.56 和 0.74/0.49，这与两个专家阅读器之间的相关性相当 (=0.72/每个患者/每个切片分析中为 0.58）。结论所提出的基于 FCN 的图像处理平台可以快速自动分析心肌原生 T1 映射图像，从而减轻手动分析的负担和观察者相关的变异性。