Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI
Deep Learning Single-Frame and Multiframe Super-Resolution for Cardiac MRI
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DOI：
10.1148/radiol.2020192173
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发表时间：
2020-06-01
期刊：
影响因子：
19.7
通讯作者：
Hsiao, Albert
中科院分区：
文献类型：
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作者：
Masutani, Evan M.;Bahrami, Naeim;Hsiao, Albert
背景：心脏MRI受采集时间长的限制，而小矩阵图像的快速采集减少了空间细节。深度学习（DL）可以通过超分辨率实现更快的获取和更高的空间细节。目的：探讨DI应用的可行性。增强小矩阵MRI采集的空间细节，并评估其与传统图像升级方法的性能。材料和方法：回顾性收集2012年1月至2018年12月在一家机构进行的短轴心脏MRI检查，用于算法开发和测试。卷积神经网络（cnn）是dnn的一种形式，通过使用生成的低分辨率数据来训练在图像空间中执行超分辨率。分别有7090次、2090次和1090次考试分配给训练集、验证集和测试集。通过计算高分辨率地面真值与每种升级方法之间的结构相似指数（SSIM），将cnn与双三次插值和基于傅立叶的零填充进行比较。报告SSIM的均值和标准差，并采用Wilcoxon符号秩检验确定统计显著性。为了评估临床表现，测量左心室容积，并通过配对学生t检验确定统计学意义。结果：CNN训练和回顾性分析共纳入367例患者（平均年龄48岁+/- 18岁，214名男性）的400次MRI扫描。在上采样因子从2到64的范围内，所有cnn都优于零填充和双三次插值（P < .001）。cnn在超过90.2%的切片（9907片中的9828片）上表现优于零填充。此外，10例患者（平均年龄，51 +/- 22岁；7名男性）被透视招募进行超分辨率MRI检查。超分辨率低分辨率图像显示的左心室容积与全分辨率图像相当（P < 0.05），超分辨率全分辨率图像似乎进一步增强了解剖细节。结论：深度学习在恢复高频空间信息方面优于传统的升级方法。虽然训练只在短轴心脏MRI检查上进行，但所提出的策略似乎提高了其他成像平面的质量。(c) rsna, 2020
Background: Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution.Purpose: To explore the feasibility of using DI. to enhance spatial detail from small-matrix MRI acquisitions and evaluate its performance against that of conventional image upscaling methods.Materials and methods: Short-axis cite cardiac MRI examinations performed between January 2012 and December 2018 at one institution were retrospectively collected for algorithm development and testing. Convolutional neural networks (CNNs), a form of DNNs were trained to perform super resolution in image space by using as generated low-resolution data. There were 7090, 2090, and 1090 of examinations allocated to training, validation, and test sets, respectively. CNNs were compare' against bicubic interpolation and Fourier-based zero padding by calculating the structural similarity index (SSIM) between high-resolution ground truth and each upscaling method. Means and standard deviations of the SSIM were reported, and statistical significance was determined by using the Wilcoxon signed-rank test. For evaluation of clinical performance, left ventricular volumes were measured, and statistical significance was determine by using the paired Student t test.Results: For CNN training and retrospective analysis, 400 MRI scans front 367 patients (mean age, 48 years +/- 18; 214 men) were included. All CNNs outperformed zero padding and bicubic interpolation at upsatnpling factors from two to 64 (P < .001). CNNs outperformed zero padding on more than 90.2% of sliccs (9828 of 9907). In addition, 10 patients (mein age, 51 +/- years 22; seven men) were perspective recruied for super-resolution MRI. Super-resolved low-resolution images yielded left ventricular volumes comparable to those from full-resolution images (P > .05), and super-resolved full-resolution images appeared to further enhance anatomic detail.Conclusion: Deep learning outperformed conventional upscaling methods and recovered high-frequency spatial information. Although training was performed only on short-axis cardiac MRI examinations, the proposed strategy appeared to improve quality in other imaging planes. (C) RSNA, 2020