Background: Photoacoustic Microscopy (PAM) integrates optical and acoustic
imaging, offering enhanced penetration depth for detecting optical-absorbing
components in tissues. Nonetheless, challenges arise in scanning large areas
with high spatial resolution. With speed limitations imposed by laser pulse
repetition rates, the potential role of computational methods is highlighted in
accelerating PAM imaging. Purpose: We are proposing a novel and highly
adaptable DiffPam algorithm that utilizes diffusion models for speeding up the
photoacoustic imaging process. Method: We leveraged a diffusion model trained
exclusively on natural images, comparing its performance with an in-domain
trained U-Net model using a dataset focused on PAM images of mice brain
microvasculature. Results: Our findings indicate that DiffPam achieves
comparable performance to a dedicated U-Net model, without the need for a large
dataset or training a deep learning model. The study also introduces the
efficacy of shortened diffusion processes for reducing computing time without
compromising accuracy. Conclusion: This study underscores the significance of
DiffPam as a practical algorithm for reconstructing undersampled PAM images,
particularly for researchers with limited AI expertise and computational
resources.

通过使用扩散模型，利用 “DiffPam” 算法来加速光声成像过程。研究发现 DiffPam 在重构欠采样的光声显微镜图像方面具有可比性，并且无需大量数据集或深度学习模型的训练。这项研究强调了对具有有限人工智能专业知识和计算资源的研究人员来说，DiffPam 算法的重要性。

利用扩散模型加快光声成像速度

Speeding up Photoacoustic Imaging using Diffusion Models

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 this https URL

该研究介绍了一种新颖且性能优越的超分辨率框架，用于基于旋转扫描的光声显微镜成像，实验结果在合成和真实数据集上量化和定性地证明了该框架的有效性和通用性。

基于扫描先验的旋转扫描光声显微镜图像超分辨率

Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images  Incorporating Scanning Prior

Colorectal cancer is a deadly disease that has become increasingly prevalent
in recent years. Early detection is crucial for saving lives, but traditional
diagnostic methods such as colonoscopy and biopsy have limitations. Colonoscopy
cannot provide detailed information within the tissues affected by cancer,
while biopsy involves tissue removal, which can be painful and invasive. In
order to improve diagnostic efficiency and reduce patient suffering, we studied
machine-learningbased approach for colorectal tissue classification that uses
acoustic resolution photoacoustic microscopy (ARPAM). With this tool, we were
able to classify benign and malignant tissue using multiple machine learning
methods. Our results were analyzed both quantitatively and qualitatively to
evaluate the effectiveness of our approach.

探究利用声学分辨光声显微镜（ARPAM）机器学习方法对结直肠组织进行良恶性分类以提高诊断效率和降低患者痛苦的研究。