Bogazici University Physics Department, Istanbul, Turkey.
Faculty of Engineering, Ozyegin University, Istanbul, Turkey.
Sci Rep. 2024 Jul 23;14(1):16996. doi: 10.1038/s41598-024-67957-z.
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. We propose a novel and highly adaptable algorithm named DiffPam that utilizes diffusion models to speed up the photoacoustic imaging process. 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. Our findings indicate that DiffPam performs similarly to a dedicated U-Net model without needing a large dataset. We demonstrate that scanning can be accelerated fivefold with limited information loss. We achieved a increase in peak signal-to-noise ratio and a increase in structural similarity index compared to the baseline bilinear interpolation method. The study also introduces the efficacy of shortened diffusion processes for reducing computing time without compromising accuracy. DiffPam stands out from existing methods as it does not require supervised training or detailed parameter optimization typically needed for other unsupervised methods. This study underscores the significance of DiffPam as a practical algorithm for reconstructing undersampled PAM images, particularly for researchers with limited artificial intelligence expertise and computational resources.
光声显微镜（PAM）集成了光学和声学成像，提高了检测组织中光吸收成分的穿透深度。然而，在大区域内实现高空间分辨率扫描仍然存在挑战。由于激光脉冲重复率的速度限制，计算方法在加速 PAM 成像方面的潜在作用得到了强调。我们提出了一种名为 DiffPam 的新颖且高度适应性的算法，该算法利用扩散模型来加速光声成像过程。我们利用专门针对自然图像训练的扩散模型，通过使用专注于小鼠脑微血管的 PAM 图像的数据集，将其性能与在域内训练的 U-Net 模型进行比较。我们的研究结果表明，DiffPam 可以在不需要大型数据集的情况下，与专用的 U-Net 模型性能相当。我们证明了在信息损失有限的情况下，可以将扫描速度提高五倍。与基线双线性插值方法相比，我们实现了峰值信噪比提高了 ，结构相似性指数提高了 。该研究还介绍了缩短扩散过程以减少计算时间而不影响准确性的效果。DiffPam 与现有方法的不同之处在于，它不需要监督式训练或通常需要其他无监督方法的详细参数优化。这项研究强调了 DiffPam 作为一种实用算法在重建欠采样 PAM 图像中的重要性，特别是对于人工智能专业知识和计算资源有限的研究人员来说。
