  Due to the limitations of sensors, the transmission medium and the intrinsic
properties of ultrasound, the quality of ultrasound imaging is always not
ideal, especially its low spatial resolution. To remedy this situation, deep
learning networks have been recently developed for ultrasound image
super-resolution (SR) because of the powerful approximation capability.
However, most current supervised SR methods are not suitable for ultrasound
medical images because the medical image samples are always rare, and usually,
there are no low-resolution (LR) and high-resolution (HR) training pairs in
reality. In this work, based on self-supervision and cycle generative
adversarial network (CycleGAN), we propose a new perception consistency
ultrasound image super-resolution (SR) method, which only requires the LR
ultrasound data and can ensure the re-degenerated image of the generated SR one
to be consistent with the original LR image, and vice versa. We first generate
the HR fathers and the LR sons of the test ultrasound LR image through image
enhancement, and then make full use of the cycle loss of LR-SR-LR and HR-LR-SR
and the adversarial characteristics of the discriminator to promote the
generator to produce better perceptually consistent SR results. The evaluation
of PSNR/IFC/SSIM, inference efficiency and visual effects under the benchmark
CCA-US and CCA-US datasets illustrate our proposed approach is effective and
superior to other state-of-the-art methods.
