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.

翻译：光声显微成像（PAM）兼具光学对比度与声学分辨率的优势，已被广泛应用于脑科学研究。然而，扫描速度与图像分辨率之间存在权衡。相较于传统光栅扫描，旋转扫描通过优化扫描机制为快速PAM成像提供了良好契机。近年来，将深度学习融入扫描过程以进一步提升扫描速度成为趋势，但相关尝试大多针对光栅扫描，针对旋转扫描的研究相对较少。本研究提出了一种新颖且性能优异的旋转扫描PAM超分辨率框架。为消除因运动伪影或高频扫描畸变导致的相邻行位移，我们在预处理阶段引入了奇偶行配准模块，并在训练过程中融入位移退化建模。此外，提出基于梯度的图像块选择策略，以提高血管区域图像块被选入训练集的概率。采用具有全局感受野的Transformer网络以提升性能。在合成数据集和真实数据集上的定性与定量实验均表明，本框架对旋转扫描PAM图像的超分辨率重建具有有效性与泛化性。代码开源地址：https://github.com/11710615/PAMSR.git。