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Due to the limitations of sensors, the transmission medium and the intrinsicproperties of ultrasound, the quality of ultrasound imaging is always notideal, especially its low spatial resolution. To remedy this situation, deeplearning networks have been recently developed for ultrasound imagesuper-resolution (SR) because of the powerful approximation capability.However, most current supervised SR methods are not suitable for ultrasoundmedical images because the medical image samples are always rare, and usually,there are no low-resolution (LR) and high-resolution (HR) training pairs inreality. In this work, based on self-supervision and cycle generativeadversarial network (CycleGAN), we propose a new perception consistencyultrasound image super-resolution (SR) method, which only requires the LRultrasound data and can ensure the re-degenerated image of the generated SR oneto be consistent with the original LR image, and vice versa. We first generatethe HR fathers and the LR sons of the test ultrasound LR image through imageenhancement, and then make full use of the cycle loss of LR-SR-LR and HR-LR-SRand the adversarial characteristics of the discriminator to promote thegenerator to produce better perceptually consistent SR results. The evaluationof PSNR/IFC/SSIM, inference efficiency and visual effects under the benchmarkCCA-US and CCA-US datasets illustrate our proposed approach is effective andsuperior to other state-of-the-art methods.

Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN