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

• URL: http://arxiv.org/abs/2410.19786v1
• Date: Tue, 15 Oct 2024 00:44:57 GMT
• Title: Resolution Enhancement of Under-sampled Photoacoustic Microscopy Images using Implicit Neural Representations
• Authors: Youshen Xiao, Sheng Liao, Xuanyang Tian, Fan Zhang, Xinlong Dong, Yunhui Jiang, Xiyu Chen, Ruixi Sun, Yuyao Zhang, Fei Gao,
• Abstract summary: Acoustic-Resolution Photoacoustic Microscopy (AR-PAM) is promising for subcutaneous vascular imaging. Traditional deconvolution methods use the Point Spreadvolution (PSF) to improve resolution. We propose an approach based on Implicit Neural Representations (INR) Our method learns a continuous mapping from spatial coordinates to initial acoustic pressure, overcoming the limitations of discrete imaging.
• Score: 4.672315172406826
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• Abstract: 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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