We propose a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. We up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of $1.7\%$ on obstetric 2X raw images, $6.1\%$ on cardiac 2X raw images, and $4.4\%$ on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of $9.0\%$ on obstetric 4X raw images, $5.2\%$ on cardiac 4X raw images, and $6.2\%$ on abdominal 4X raw images. The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, our super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network's prediction on local devices.

翻译：我们提出了一种新颖的深度学习框架，用于在空间分辨率和线重建方面实现超声图像和视频的超分辨率处理。首先，通过基于视觉的插值方法对获取的低分辨率图像进行上采样；然后，训练一个基于学习的模型来提升上采样质量。我们针对不同解剖区域（如心脏、产科）的图像以及不同上采样倍数（即2倍、4倍）进行了定性和定量测试。与当前最优方法（SOTA）相比，我们的方法在产科2倍原始图像上PSNR中值提高了1.7%，在心脏2倍原始图像上提高了6.1%，在腹部4倍原始图像上提高了4.4%；同时，在产科4倍原始图像上，低预测误差的像素数量改善了9.0%，在心脏4倍原始图像上改善了5.2%，在腹部4倍原始图像上改善了6.2%。随后，该方法通过优化探头采集线在采集频率方面的采样，应用于二维视频的空间超分辨率。我们的方法通过设计网络架构和损失函数，考虑解剖区域和上采样因子，并利用大规模超声数据集，专门训练网络以预测高分辨率目标。在大规模数据集上使用深度学习克服了基于视觉算法的局限性，后者通常具有通用性且不编码数据特征。此外，数据集中可以加入由医学专家筛选的图像，以进一步使各个网络专业化。通过学习和高性能计算，我们的超分辨率方法通过训练多个网络实现了不同解剖区域的专门化处理。同时，计算需求被转移到集中式硬件资源上，并在本地设备上实时执行网络预测。