A hand-held optical coherence tomography angiography scanner based on angiography reconstruction transformer networks

被引:0
|
作者
Liao, Jinpeng [1 ]
Yang, Shufan [2 ,3 ]
Zhang, Tianyu [1 ]
Li, Chunhui [1 ]
Huang, Zhihong [1 ]
机构
[1] Univ Dundee, Sch Sci & Engn, Dundee, Scotland
[2] Edinburgh Napier Univ, Sch Comp Engn & Built Environm, Edinburgh, Scotland
[3] UCL, Res Dept Orthopaed & Musculoskeletal Sci, London, England
关键词
deep learning; optical coherence tomography angiography; single image super-resolution; SPECKLE NOISE-REDUCTION; OCT IMAGES; ALGORITHM; SKIN;
D O I
10.1002/jbio.202300100
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Optical coherence tomography angiography (OCTA) has successfully demonstrated its viability for clinical applications in dermatology. Due to the high optical scattering property of skin, extracting high-quality OCTA images from skin tissues requires at least six-repeated scans. While the motion artifacts from the patient and the free hand-held probe can lead to a low-quality OCTA image. Our deep-learning-based scan pipeline enables fast and high-quality OCTA imaging with 0.3-s data acquisition. We utilize a fast scanning protocol with a 60 mu m/pixel spatial interval rate and introduce angiographyreconstruction-transformer (ART) for 4x super-resolution of low transverse resolution OCTA images. The ART outperforms state-of-the-art networks in OCTA image super-resolution and provides a lighter network size. ART can restore microvessels while reducing the processing time by 85%, and maintaining improvements in structural similarity and peak-signal-to-noise ratio. This study represents that ART can achieve fast and flexible skin OCTA imaging while maintaining image quality.
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页数:17
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