Ultrasound imaging is crucial for evaluating organ morphology and function, yet depth adjustment can degrade image quality and field-of-view, presenting a depth-dependent dilemma. Traditional interpolation-based zoom-in techniques often sacrifice detail and introduce artifacts. Motivated by the potential of arbitrary-scale super-resolution to naturally address these inherent challenges, we present the Residual Dense Swin Transformer Network (RDSTN), designed to capture the non-local characteristics and long-range dependencies intrinsic to ultrasound images. It comprises a linear embedding module for feature enhancement, an encoder with shifted-window attention for modeling non-locality, and an MLP decoder for continuous detail reconstruction. This strategy streamlines balancing image quality and field-of-view, which offers superior textures over traditional methods. Experimentally, RDSTN outperforms existing approaches while requiring fewer parameters. In conclusion, RDSTN shows promising potential for ultrasound image enhancement by overcoming the limitations of conventional interpolation-based methods and achieving depth-independent imaging.

翻译：超声成像对于评估器官形态和功能至关重要，然而深度调整会降低图像质量和视野，形成深度依赖困境。传统基于插值的放大技术常以牺牲细节为代价且引入伪影。受任意尺度超分辨率技术可自然解决这些固有挑战的启发，我们提出残差密集Swin变换器网络（RDSTN），旨在捕获超声图像固有的非局部特征与长程依赖关系。该网络包含用于特征增强的线性嵌入模块、采用移位窗口注意力机制建模非局部性的编码器，以及用于连续细节重建的MLP解码器。该策略在平衡图像质量与视野方面具有优势，相比传统方法能提供更优的纹理表现。实验表明，RDSTN在参数更少的情况下性能超过现有方法。总之，RDSTN通过克服传统插值方法的局限性并实现深度无关成像，在超声图像增强领域展现出广阔潜力。