CT scanners that are commonly-used in hospitals nowadays produce low-resolution images, up to 512 pixels in size. One pixel in the image corresponds to a one millimeter piece of tissue. In order to accurately segment tumors and make treatment plans, doctors need CT scans of higher resolution. The same problem appears in MRI. In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional neural networks (CNNs) composed of 10 convolutional layers and an intermediate upscaling layer that is placed after the first 6 convolutional layers. Our first CNN, which increases the resolution on two axes (width and height), is followed by a second CNN, which increases the resolution on the third axis (depth). Different from other methods, we compute the loss with respect to the ground-truth high-resolution output right after the upscaling layer, in addition to computing the loss after the last convolutional layer. The intermediate loss forces our network to produce a better output, closer to the ground-truth. A widely-used approach to obtain sharp results is to add Gaussian blur using a fixed standard deviation. In order to avoid overfitting to a fixed standard deviation, we apply Gaussian smoothing with various standard deviations, unlike other approaches. We evaluate our method in the context of 2D and 3D super-resolution of CT and MRI scans from two databases, comparing it to relevant related works from the literature and baselines based on various interpolation schemes, using 2x and 4x scaling factors. The empirical results show that our approach attains superior results to all other methods. Moreover, our human annotation study reveals that both doctors and regular annotators chose our method in favor of Lanczos interpolation in 97.55% cases for 2x upscaling factor and in 96.69% cases for 4x upscaling factor.

翻译：目前医院常用的CT扫描仪生成低分辨率图像，像素尺寸最高为512像素。图像中的一个像素对应一毫米的组织。为了准确分割肿瘤并制定治疗方案，医生需要更高分辨率的CT扫描。核磁共振成像也存在同样的问题。在本文中，我们提出了一种针对三维CT或MRI扫描的单图像超分辨率方法。我们的方法基于深度卷积神经网络，该网络由10个卷积层和一个位于前6个卷积层之后的中间上采样层组成。第一个卷积神经网络在两个轴（宽度和高度）上提高分辨率，随后第二个卷积神经网络在第三个轴（深度）上提高分辨率。与其他方法不同，我们在上采样层之后立即计算相对于真实高分辨率输出的损失，同时在最后一个卷积层之后也计算损失。中间损失迫使网络生成更接近真实情况的更高质量输出。获得清晰图像的常用方法是使用固定标准差添加高斯模糊。为了避免过拟合到固定标准差，我们应用了具有不同标准差的高斯平滑，这与其他方法不同。我们在两个数据库的CT和MRI扫描的二维和三维超分辨率背景下评估了我们的方法，将其与文献中的相关工作以及基于各种插值方案的基线方法进行了比较，使用了2倍和4倍缩放因子。实证结果表明，我们的方法在所有方法中取得了最优结果。此外，我们的人工标注研究表明，在2倍放大因子下，医生和普通标注者在97.55%的案例中选择我们的方法而非Lanczos插值；在4倍放大因子下，这一比例为96.69%。