Objective: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method & Materials: Previous CNN-based MRI super-resolution methods cause loss of input image information due to the pooling layer. An Autoencoder-inspired Convolutional Network-based Super-resolution (ACNS) method was developed with the deconvolution layer that extrapolates the missing spatial information by the convolutional neural network-based nonlinear mapping between LR and HR features of MRI. Simulation experiments were conducted with virtual phantom images and thoracic MRIs from four volunteers. The Peak Signal-to-Noise Ratio (PSNR), Structure SIMilarity index (SSIM), Information Fidelity Criterion (IFC), and computational time were compared among: ACNS; Super-Resolution Convolutional Neural Network (SRCNN); Fast Super-Resolution Convolutional Neural Network (FSRCNN); Deeply-Recursive Convolutional Network (DRCN). Results: ACNS achieved comparable PSNR, SSIM, and IFC results to SRCNN, FSRCNN, and DRCN. However, the average computation speed of ACNS was 6, 4, and 35 times faster than SRCNN, FSRCNN, and DRCN, respectively under the computer setup used with the actual average computation time of 0.15 s per $100\times100$ pixels. Conclusion: The result of this study implies the potential application of ACNS to real-time resolution enhancement of 4D MRI in MRI guided radiation therapy.

中文翻译：

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MRI中基于自编码器的基于卷积网络的超分辨率方法

目的：介绍一种 MRI 平面内分辨率增强方法，该方法可以从低分辨率 (LR) MRI 中估计高分辨率 (HR) MRI。方法和材料：以前基于 CNN 的 MRI 超分辨率方法由于池化层而导致输入图像信息丢失。一种受自动编码器启发的基于卷积网络的超分辨率 (ACNS) 方法被开发出来，其反卷积层通过基于卷积神经网络的 MRI LR 和 HR 特征之间的非线性映射来推断缺失的空间信息。使用来自四名志愿者的虚拟体模图像和胸部 MRI 进行了模拟实验。峰值信噪比 (PSNR)、结构相似性指数 (SSIM)、信息保真度标准 (IFC) 和计算时间在以下之间进行了比较：ACNS；超分辨率卷积神经网络（SRCNN）；快速超分辨率卷积神经网络（FSRCNN）；深度递归卷积网络 (DRCN)。结果：ACNS 的 PSNR、SSIM 和 IFC 结果与 SRCNN、FSRCNN 和 DRCN 相当。然而，在实际平均计算时间为 0.15 秒/秒的计算机设置下，ACNS 的平均计算速度分别比 SRCNN、FSRCNN 和 DRCN 快 6、4 和 35 倍 $100\times100$像素。结论：本研究结果表明 ACNS 在 MRI 引导放射治疗中的 4D MRI 实时分辨率增强的潜在应用。