Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images
  Incorporating Scanning Prior
Kai Pan Linyang Li Li Lin Pujin Cheng Junyan Lyu Lei Xi Xiaoyin Tang
Photoacoustic Microscopy (PAM) images integrating the advantages of optical
contrast and acoustic resolution have been widely used in brain studies.
However, there exists a trade-off between scanning speed and image resolution.
Compared with traditional raster scanning, rotational scanning provides good
opportunities for fast PAM imaging by optimizing the scanning mechanism.
Recently, there is a trend to incorporate deep learning into the scanning
process to further increase the scanning speed.Yet, most such attempts are
performed for raster scanning while those for rotational scanning are
relatively rare. In this study, we propose a novel and well-performing
super-resolution framework for rotational scanning-based PAM imaging. To
eliminate adjacent rows' displacements due to subject motion or high-frequency
scanning distortion,we introduce a registration module across odd and even rows
in the preprocessing and incorporate displacement degradation in the training.
Besides, gradient-based patch selection is proposed to increase the probability
of blood vessel patches being selected for training. A Transformer-based
network with a global receptive field is applied for better performance.
Experimental results on both synthetic and real datasets demonstrate the
effectiveness and generalizability of our proposed framework for rotationally
scanned PAM images'super-resolution, both quantitatively and qualitatively.
Code is available at https://github.com/11710615/PAMSR.git.