Rapid whole-heart CMR with single volume super-resolution
Rapid whole-heart CMR with single volume super-resolution
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DOI：
10.1186/s12968-020-00651-x
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发表时间：
2020-08-03
影响因子：
6.4
通讯作者：
Muthurangu, Vivek
中科院分区：
文献类型：
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作者：
Steeden, Jennifer A.;Quail, Michael;Muthurangu, Vivek
背景资料：三维、全心脏、平衡稳态自由进动（WH-bSSFP）序列提供了心内和血管解剖结构的描绘。然而，他们有很长的收购时间。在这里，我们提出了使用深度学习单体积超分辨率重建的显着加速，从快速采集的低分辨率WH-bSSFP图像中恢复高分辨率特征。方法：使用合成数据训练3D残差U-Net，通过模拟50%切片分辨率和50%相位分辨率，从500个高分辨率WH-bSSFP图像库中创建。用25个合成测试数据集对训练好的网络进行了验证。此外，在40例患者中采集了前瞻性低分辨率数据和高分辨率数据。在前瞻性数据中，比较低分辨率、超分辨率和参考高分辨率WH-bSSFP数据之间的血管直径、定量和定性图像质量以及诊断评分。前瞻性采集的低分辨率数据比前瞻性高分辨率数据快3倍（173 s vs 488 s）。低分辨率数据的超分辨率重建每体积花费< 1 s。定性图像评分显示，与低分辨率图像相比，超分辨率图像具有更好的边缘清晰度，更少的残留伪影和更少的图像失真，与高分辨率数据的评分相似。定量图像评分显示，超分辨率图像的边缘清晰度明显优于低分辨率或高分辨率图像，信噪比明显优于高分辨率数据。与高分辨率数据相比，血管直径测量显示低分辨率测量的高估。在任何大血管的超分辨率测量中均未发现显著差异和偏倚。然而，在超分辨率数据的近端左冠状动脉直径测量中发现了一个小但显著的低估。诊断评分显示，虽然超分辨率没有提高诊断的准确性，它确实提高了诊断的信心相比，低分辨率imaging.Conclusion：本文证明了潜在的使用剩余的U-Net的超分辨率重建快速获得的低分辨率的全心脏bSSFP数据在临床环境。我们能够使用来自回顾性高分辨率全心脏数据的合成训练数据来训练网络。由此产生的网络可以非常快速地应用，使得这些技术在忙碌的临床工作流程中特别有吸引力。因此，我们相信这项技术可能有助于在临床实践中加快全心脏CMR。
Background: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed-ups using a deep-learning single volume super-resolution reconstruction, to recover high-resolution features from rapidly acquired low-resolution WH-bSSFP images.Methods:A 3D residual U-Net was trained using synthetic data, created from a library of 500 high-resolution WH-bSSFP images by simulating 50% slice resolution and 50% phase resolution. The trained network was validated with 25 synthetic test data sets. Additionally, prospective low-resolution data and high-resolution data were acquired in 40 patients. In the prospective data, vessel diameters, quantitative and qualitative image quality, and diagnostic scoring was compared between the low-resolution, super-resolution and reference high-resolution WH-bSSFP data.Results: The synthetic test data showed a significant increase in image quality of the low-resolution images after super-resolution reconstruction. Prospectively acquired low-resolution data was acquired similar to x 3 faster than the prospective high-resolution data (173 s vs 488 s). Super-resolution reconstruction of the low-resolution data took < 1 s per volume. Qualitative image scores showed super-resolved images had better edge sharpness, fewer residual artefacts and less image distortion than low-resolution images, with similar scores to high-resolution data. Quantitative image scores showed super-resolved images had significantly better edge sharpness than low-resolution or high-resolution images, with significantly better signal-to-noise ratio than high-resolution data. Vessel diameters measurements showed over-estimation in the low-resolution measurements, compared to the high-resolution data. No significant differences and no bias was found in the super-resolution measurements in any of the great vessels. However, a small but significant for the underestimation was found in the proximal left coronary artery diameter measurement from super-resolution data. Diagnostic scoring showed that although super-resolution did not improve accuracy of diagnosis, it did improve diagnostic confidence compared to low-resolution imaging.Conclusion: This paper demonstrates the potential of using a residual U-Net for super-resolution reconstruction of rapidly acquired low-resolution whole heart bSSFP data within a clinical setting. We were able to train the network using synthetic training data from retrospective high-resolution whole heart data. The resulting network can be applied very quickly, making these techniques particularly appealing within busy clinical workflow. Thus, we believe that this technique may help speed up whole heart CMR in clinical practice.