Residual Dense Swin Transformer for Continuous Depth-Independent
  Ultrasound Imaging
Jintong Hu Hui Che Zishuo Li Wenming Yang
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.