Feasibility of deep learning-based polarization-sensitive optical coherence tomography angiography for imaging cutaneous microvasculature

Moning Pan; Yuxing Wang; Peijun Gong; Qiang Wang; Barry Cense

doi:10.1364/BOE.488822

Feasibility of deep learning-based polarization-sensitive optical coherence tomography angiography for imaging cutaneous microvasculature

Biomed Opt Express. 2023 Jul 5;14(8):3856-3870. doi: 10.1364/BOE.488822. eCollection 2023 Aug 1.

Authors

Moning Pan¹, Yuxing Wang¹, Peijun Gong^{1

2

3}, Qiang Wang³, Barry Cense^{3

4}

Affiliations

¹ Key Laboratory for Biomedical Engineering of Ministry of Education, Embedded System Engineering Research Center of Ministry of Education and Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Zhejiang University, Hangzhou, 310027, China.
² BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia.
³ Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia.
⁴ Department of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea.

Abstract

Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization states of the backscattered light from tissue that can improve angiography based on conventional optical coherence tomography (OCT). We present a feasibility study on PS-OCT integrated with deep learning for PS-OCT angiography (PS-OCTA) imaging of human cutaneous microvasculature. Two neural networks were assessed for PS-OCTA, including the residual dense network (RDN), which previously showed superior performance for angiography with conventional OCT and the upgraded grouped RDN (GRDN). We also investigated different protocols to process the multiple signal channels provided by the Jones matrices from the PS-OCT system to achieve optimal PS-OCTA performance. The training and testing of the deep learning-based PS-OCTA were performed using PS-OCT scans collected from 18 skin locations comprising 16,600 B-scan pairs. The results demonstrated a moderately improved performance of GRDN over RDN, and of the use of the combined signal from the Jones matrix elements over the separate use of the elements, as well as a similar image quality to that provided by speckle decorrelation angiography. GRDN-based PS-OCTA also showed ∼2-3 times faster processing and improved mitigation of tissue motion as compared to speckle decorrelation angiography, and enabled fully automatic processing. Deep learning-based PS-OCTA can be used for imaging cutaneous microvasculature, which may enable easy adoption of PS-OCTA for preclinical and clinical applications.