Photoacoustic dermoscopy (PAD) is an emerging non-invasive imaging technology aids in the diagnosis of dermatological conditions by obtaining optical absorption information of skin tissues. Despite advances in PAD, it remains unclear how to obtain quantitative accuracy of the reconstructed PAD images according to the optical and acoustic properties of multilayered skin, the wavelength and distribution of excitation light, and the detection performance of ultrasound transducers. In this work, a computing method of four-dimensional (4D) spectral-spatial imaging for PAD is developed to enable quantitative analysis and optimization of structural and functional imaging of skin. This method takes the optical and acoustic properties of heterogeneous skin tissues into account, which can be used to correct the optical field of excitation light, detectable ultrasonic field, and provide accurate single-spectrum analysis or multi-spectral imaging solutions of PAD for multilayered skin tissues. A series of experiments were performed, and simulation datasets obtained from the computational model were used to train neural networks to further improve the imaging quality of the PAD system. All the results demonstrated the method could contribute to the development and optimization of clinical PADs by datasets with multiple variable parameters, and provide clinical predictability of photoacoustic (PA) data for human skin.

中文翻译：

---

4D 光谱空间计算光声皮肤镜检查

光声皮肤镜（PAD）是一种新兴的非侵入性成像技术，通过获取皮肤组织的光吸收信息来帮助诊断皮肤病。尽管 PAD 取得了进展，但如何根据多层皮肤的光学和声学特性、激发光的波长和分布以及超声换能器的检测性能来获得重建 PAD 图像的定量精度仍不清楚。在这项工作中，开发了一种用于 PAD 的四维 (4D) 光谱空间成像计算方法，以实现皮肤结构和功能成像的定量分析和优化。该方法考虑了异质皮肤组织的光学和声学特性，可用于校正激发光光场、可检测超声场，为多层皮肤的PAD提供精确的单光谱分析或多光谱成像解决方案组织。进行了一系列实验，并使用从计算模型获得的模拟数据集来训练神经网络，以进一步提高PAD系统的成像质量。所有结果表明，该方法可以通过具有多个可变参数的数据集有助于临床 PAD 的开发和优化，并提供人体皮肤光声 (PA) 数据的临床可预测性。