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.

提出了一种基于深度学习的超分辨率超声图像和视频增强方法，通过训练模型和大量医学图像数据进行定制化的超分辨率处理，实现了对不同解剖区域的低分辨率超声图像和视频的有效提高，并且兼顾了训练网络的运算速度和硬件资源消耗。