Open AccessDevelopment of a multi-scene universal multiple wavelet-FFT algorithm (MW-FFTA) for denoising motion artifacts in OCT-angiography in vivo imaging
Author(s) -
Yunrui Zhang,
Junwei Li,
Chunlei Liu,
Kaili Zheng,
Bei Zhang,
Yuying Zhou,
Cuixia Dai,
Shanhui Fan,
Youliang Yao,
Rongqiang Zhuang,
Dongbei Guo,
Zicheng Huang,
Jingsong Mao,
Junqiang Liang,
Hongqin Yang,
Liansheng Wang,
Gang Liu,
Xiaoyuan Chen,
Qingliang Zhao
Publication year - 2022
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.465255
Subject(s) - computer science , heartbeat , fast fourier transform , computer vision , artificial intelligence , image quality , wavelet , wavelet transform , optical coherence tomography , robustness (evolution) , noise reduction , algorithm , optics , image (mathematics) , physics , biochemistry , chemistry , computer security , gene
Optical coherence tomography angiography (OCTA) images suffer from inevitable micromotion (breathing, heartbeat, and blinking) noise. These image artifacts can severely disturb the visibility of results and reduce accuracy of vessel morphological and functional metrics quantization. Herein, we propose a multiple wavelet-FFT algorithm (MW-FFTA) comprising multiple integrated processes combined with wavelet-FFT and minimum reconstruction that can be used to effectively attenuate motion artifacts and significantly improve the precision of quantitative information. We verified the fidelity of image information and reliability of MW-FFTA by the image quality evaluation. The efficiency and robustness of MW-FFTA was validated by the vessel parameters on multi-scene in vivo OCTA imaging. Compared with previous algorithms, our method provides better visual and quantitative results. Therefore, the MW-FFTA possesses the potential capacity to improve the diagnosis of clinical diseases with OCTA.