Deep learning has been extensively used in medical imaging applications, assuming that the test and training datasets belong to the same probability distribution. However, a common challenge arises when working with medical images generated by different systems or even the same system with different parameter settings. Such images contain diverse textures and reverberation noise that violate the aforementioned assumption. Consequently, models trained on data from one device or setting often struggle to perform effectively with data from other devices or settings. In addition, retraining models for each specific device or setting is labor-intensive and costly. To address these issues in ultrasound images, we propose a novel Generative Adversarial Network (GAN)-based model. We formulated the domain adaptation tasks as an image-to-image translation task, in which we modified the texture patterns and removed reverberation noise in the test data images from the source domain to align with those in the target domain images while keeping the image content unchanged. We applied the proposed method to two datasets containing carotid ultrasound images from three different domains. The experimental results demonstrate that the model successfully translated the texture pattern of images and removed reverberation noise from the ultrasound images. Furthermore, we evaluated the CycleGAN approaches for a comparative study with the proposed model. The experimental findings conclusively demonstrated that the proposed model achieved domain adaptation (histogram correlation (0.960 (0.019), & 0.920 (0.043) and bhattacharya distance (0.040 (0.020), & 0.085 (0.048)), compared to no adaptation (0.916 (0.062) & 0.890 (0.077), 0.090 (0.070) & 0.121 (0.095)) for both datasets.

翻译：深度学习已广泛应用于医学成像领域，其前提是测试数据集与训练数据集服从相同的概率分布。然而，当处理由不同系统甚至同一系统不同参数设置生成的医学图像时，常会遇到一个普遍性挑战。此类图像包含多样化的纹理特征和混响噪声，违背了上述同分布假设。因此，在特定设备或设置下训练的模型，往往难以在其他设备或设置生成的数据上有效工作。此外，为每个特定设备或设置重新训练模型需要耗费大量人力与成本。针对超声图像中的这些问题，我们提出了一种基于生成对抗网络的新型模型。我们将域适应任务构建为图像到图像的转换任务，通过修改源域测试数据图像的纹理模式并去除混响噪声，使其与目标域图像的纹理特征保持一致，同时保持图像内容不变。我们将所提方法应用于包含三个不同域颈动脉超声图像的两个数据集。实验结果表明，该模型成功转换了图像的纹理模式并消除了超声图像中的混响噪声。此外，我们评估了CycleGAN方法以与所提模型进行对比研究。实验结果表明：在两个数据集上，所提模型实现了域适应（直方图相关性分别为0.960(0.019)和0.920(0.043)，巴氏距离分别为0.040(0.020)和0.085(0.048)），而未进行域适应的结果分别为（0.916(0.062)和0.890(0.077)，0.090(0.070)和0.121(0.095)）。