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. (Figure presented.).
Original language | English |
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Article number | e202300100 |
Number of pages | 17 |
Journal | Journal of Biophotonics |
Volume | 16 |
Issue number | 9 |
Early online date | 1 Jun 2023 |
DOIs | |
Publication status | Published - Sept 2023 |
Keywords
- deep learning
- optical coherence tomography angiography
- single image super-resolution
ASJC Scopus subject areas
- General Engineering
- General Physics and Astronomy
- General Chemistry
- General Biochemistry,Genetics and Molecular Biology
- General Materials Science