Uniquely capable of simultaneous imaging of the hemoglobin concentration, blood oxygenation, and flow speed at the microvascular level in vivo, multi-parametric photoacoustic microscopy (PAM) has shown considerable impact in biomedicine. However, the multi-parametric PAM acquisition requires dense sampling and thus a high laser pulse repetition rate (up to MHz), which sets a strict limit on the applicable pulse energy due to safety considerations. A similar limitation is shared by high-speed PAM, which also uses lasers with high pulse repetition rates. To achieve high quantitative accuracy besides good structural visualization at low levels of laser fluence in PAM, we have developed a new, sparse coding-based two-step denoising technique. In the setting of intravital brain imaging, we demonstrated that this unsupervised learning approach enabled the reduction of the laser fluence in PAM by 5 times without compromise of the image quality (structural similarity index measure or SSIM: >0.92) and the quantitative accuracy (errors: <4.9%). Offering a significant relaxation in the requirement of PAM on laser fluence while maintaining the quality of structural imaging and accuracy of quantitative measurements, this sparse coding-based approach is expected to facilitate the application and clinical translation of multi-parametric PAM and high-speed PAM, which have a tight photon budget due to either safety considerations or laser source limitations.

中文翻译：

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支持稀疏编码的低通量多参数光声显微镜


多参数光声显微镜 (PAM) 具有独特的能力，能够同时对体内微血管水平的血红蛋白浓度、血液氧合和流速进行成像，在生物医学领域显示出相当大的影响。然而，多参数PAM采集需要密集采样，因此需要较高的激光脉冲重复率（高达MHz），出于安全考虑，这对适用的脉冲能量设置了严格的限制。高速 PAM 也有类似的限制，它也使用高脉冲重复率的激光器。为了在 PAM 中低激光注量下实现良好的结构可视化之外，还需要实现高定量精度，我们开发了一种新的基于稀疏编码的两步去噪技术。在活体脑成像中，我们证明这种无监督学习方法可以将 PAM 中的激光注量减少 5 倍，而不会影响图像质量（结构相似性指数测量或 SSIM：>0.92）和定量准确性（错误：<4.9%）。这种基于稀疏编码的方法显着放宽了 PAM 对激光注量的要求，同时保持结构成像的质量和定量测量的准确性，有望促进多参数 PAM 和高速 PAM 的应用和临床转化，由于安全考虑或激光源限制，其光子预算紧张。