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

Liao, Jinpeng; Yang, Shufan; Zhang, Tianyu; Li, Chunhui; Huang, Zhihong

Abstract

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 μm/pixel spatial interval rate and introduce angiography‐reconstruction‐transformer (ART) for 4× 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.

• Journal Article Type: Article
• Acceptance Date: May 24, 2023
• Online Publication Date: Jun 16, 2023
• Publication Date: 2023-09
• Deposit Date: Jun 19, 2023
• Publicly Available Date: Jun 19, 2023
• Journal: Journal of Biophotonics
• Print ISSN: 1864-063X
• Electronic ISSN: 1864-0648
• Publisher: Wiley-VCH Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 9
• Article Number: e202300100
• DOI: https://doi.org/10.1002/jbio.202300100
• Keywords: single image super‐resolution, optical coherence tomography angiography, deep learning