IEEE Trans Biomed Eng. 2020 Jan;67(1):298-311. doi: 10.1109/TBME.2019.2912986. Epub 2019 Apr 24.
As a widely used imaging modality in the medical field, ultrasound has been applied in community medicine, rural medicine, and even telemedicine in recent years. Therefore, the development of portable ultrasound devices has become a popular research topic. However, the limited size of portable ultrasound devices usually degrades the imaging quality, which reduces the diagnostic reliability. To overcome hardware limitations and improve the image quality of portable ultrasound devices, we propose a novel generative adversarial network (GAN) model to achieve mapping between low-quality ultrasound images and corresponding high-quality images. In contrast to the traditional GAN method, our two-stage GAN that cascades a U-Net network prior to the generator as a front end is built to reconstruct the tissue structure, details, and speckle of the reconstructed image. In the training process, an ultrasound plane-wave imaging (PWI) data-based transfer learning method is introduced to facilitate convergence and to eliminate the influence of deformation caused by respiratory activities during data pair acquisition. A gradual tuning strategy is adopted to obtain better results by the PWI transfer learning process. In addition, a comprehensive loss function is presented to combine texture, structure, and perceptual features. Experiments are conducted using simulated, phantom, and clinical data. Our proposed method is compared to four other algorithms, including traditional gray-level-based methods and learning-based methods. The results confirm that the proposed approach obtains the optimum solution for improving quality and offering useful diagnostic information for portable ultrasound images. This technology is of great significance for providing universal medical care.
作为医学领域广泛使用的成像模式，近年来，超声已应用于社区医学、农村医学，甚至远程医疗。因此，便携式超声设备的发展已成为热门研究课题。然而，便携式超声设备的有限尺寸通常会降低成像质量，从而降低诊断的可靠性。为了克服硬件限制并提高便携式超声设备的图像质量，我们提出了一种新颖的生成对抗网络（GAN）模型，以实现低质量超声图像与相应高质量图像之间的映射。与传统的 GAN 方法不同，我们构建了一个两级 GAN，在生成器之前级联一个 U-Net 网络作为前端，以重建重建图像的组织结构、细节和斑点。在训练过程中，引入了基于超声平面波成像（PWI）数据的迁移学习方法，以促进收敛并消除在数据对采集过程中呼吸活动引起的变形的影响。采用逐步调整策略，通过 PWI 迁移学习过程获得更好的结果。此外，提出了一种综合损失函数，以结合纹理、结构和感知特征。使用模拟、体模和临床数据进行了实验。将所提出的方法与包括传统灰度级方法和基于学习的方法在内的其他四种算法进行了比较。结果证实，所提出的方法在提高便携式超声图像的质量和提供有用的诊断信息方面获得了最佳解决方案。这项技术对于提供普遍医疗保健具有重要意义。
