4D spectral-spatial computational photoacoustic dermoscopy

Yang Gao; Ting Feng; Haixia Qiu; Ying Gu; Qian Chen; Chao Zuo; Haigang Ma

doi:10.1016/j.pacs.2023.100572

4D spectral-spatial computational photoacoustic dermoscopy

Photoacoustics. 2023 Nov 10:34:100572. doi: 10.1016/j.pacs.2023.100572. eCollection 2023 Dec.

Authors

Yang Gao^{1

2

3}, Ting Feng⁴, Haixia Qiu⁵, Ying Gu⁵, Qian Chen^{1

3}, Chao Zuo^{1

2

3}, Haigang Ma^{1

2

3}

Affiliations

¹ Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Smart Computational Imaging Laboratory (SCILab), Nanjing 210094, China.
² Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing 210094, China.
³ Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing 210094, China.
⁴ Fudan University, Academy for Engineering and Technology, Shanghai 200433, China.
⁵ First Medical Center of PLA General Hospital, Beijing 100853, China.

Abstract

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.

Keywords: 4D spectral-spatial; Learning from simulation; Photoacoustic dermoscopy; Quantitative calculation; Skin imaging.