arXiv 2410.197862024-10-15cs.CV

使用隐式神经表示增强欠采样光声显微图像的分辨率

Resolution Enhancement of Under-sampled Photoacoustic Microscopy Images using Implicit Neural Representations

School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）

Youshen Xiao, Sheng Liao, Xuanyang Tian, Fan Zhang, Xinlong Dong, Yunhui Jiang, Xiyu Chen, Ruixi Sun, Yuyao Zhang, Fei Gao

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AI总结：

针对AR-PAM中PSF难测与欠采样图像恢复难题，提出基于INR的连续表示与可学习PSF方法，显著提升模拟及活体显微图像分辨率。

摘要：

声学分辨率光声显微成像（AR-PAM）在皮下血管成像方面具有广阔前景，但其空间分辨率受到点扩散函数（PSF）的限制。Richardson-Lucy和基于模型的反卷积等传统反卷积方法利用PSF来提高分辨率。然而，精确测量PSF十分困难，导致人们依赖精度较低的盲反卷积技术。此外，AR-PAM还存在扫描时间长的问题，可通过下采样来缩短扫描时间，但这就需要从欠采样数据中有效恢复图像，而传统插值方法在这一任务上表现不佳，尤其是在高欠采样率下。为应对这些挑战，我们提出了一种基于隐式神经表示（INR）的方法。该方法学习从空间坐标到初始声压的连续映射，克服了离散成像的局限性并提高了AR-PAM的分辨率。通过将PSF视为INR框架内的可学习参数，我们的技术缓解了与PSF估计相关的不准确性。我们在模拟血管数据上评估了所提方法，结果表明其在峰值信噪比（PSNR）和结构相似性指数（SSIM）方面相较传统方法有显著提升。在叶脉和小鼠体内脑微血管图像中也观察到了定性增强。将该方法应用于自研AR-PAM系统时，铅笔芯实验表明我们的方法能够提供更锐利、更高分辨率的结果，显示出其推动光声显微成像发展的潜力。

英文摘要：

Acoustic-Resolution Photoacoustic Microscopy (AR-PAM) is promising for subcutaneous vascular imaging, but its spatial resolution is constrained by the Point Spread Function (PSF). Traditional deconvolution methods like Richardson-Lucy and model-based deconvolution use the PSF to improve resolution. However, accurately measuring the PSF is difficult, leading to reliance on less accurate blind deconvolution techniques. Additionally, AR-PAM suffers from long scanning times, which can be reduced via down-sampling, but this necessitates effective image recovery from under-sampled data, a task where traditional interpolation methods fall short, particularly at high under-sampling rates. To address these challenges, we propose an approach based on Implicit Neural Representations (INR). This method learns a continuous mapping from spatial coordinates to initial acoustic pressure, overcoming the limitations of discrete imaging and enhancing AR-PAM's resolution. By treating the PSF as a learnable parameter within the INR framework, our technique mitigates inaccuracies associated with PSF estimation. We evaluated our method on simulated vascular data, showing significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) over conventional methods. Qualitative enhancements were also observed in leaf vein and in vivo mouse brain microvasculature images. When applied to a custom AR-PAM system, experiments with pencil lead demonstrated that our method delivers sharper, higher-resolution results, indicating its potential to advance photoacoustic microscopy.

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