4.6 Article

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

Journal

JOURNAL OF BIOPHOTONICS
Volume 16, Issue 9, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/jbio.202300100

Keywords

deep learning; optical coherence tomography angiography; single image super-resolution

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Optical coherence tomography angiography (OCTA) has been successfully applied in clinical dermatology, but extracting high-quality OCTA images from skin tissues requires at least six repeated scans due to the skin's high optical scattering property. The motion artifacts from the patient and the free hand-held probe can lead to low-quality OCTA images. Our deep-learning-based scan pipeline enables fast and high-quality OCTA imaging with 0.3-s data acquisition.
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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