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Updated: Jul 12, 2026
08:17
Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo
Published on: September 22, 2017
19.3K
Quantitative Characterization of Retinal Features in Translated OCTA.
Rashadul Hasan Badhon1, Atalie Carina Thompson2, Jennifer I Lim3
1Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC, United States.
Medrxiv : the Preprint Server for Health Sciences
|March 11, 2024
Summary
Generative machine learning can translate Optical Coherence Tomography (OCT) images to Optical Coherence Tomography Angiography (OCTA) images. This technique shows potential for retinal disease diagnosis, improving accessibility and reducing costs associated with specialized OCTA hardware.
Area of Science:
- Ophthalmology
- Medical Imaging
- Machine Learning
Background:
- Optical Coherence Tomography Angiography (OCTA) is crucial for diagnosing retinal diseases but requires specialized hardware, limiting its widespread clinical adoption.
- Generative machine learning (ML) offers a potential solution to create OCTA-like images from standard Optical Coherence Tomography (OCT) data.
Conclusions:
- Generative ML provides a promising method to generate OCTA-quality images from standard OCT scans.
- TR-OCTA analysis of vascular features can aid in retinal disease detection, overcoming limitations of current OCTA adoption.
- This approach enhances diagnostic capabilities and reduces reliance on expensive OCTA equipment.
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In IR spectroscopy of carboxylic acids, the C=O bond shows a characteristic band between 1710 and 1760 cm⁻¹, and the O–H bond exhibits a broad band between 2500 and 3300 cm⁻¹.
However, the stretching absorptions for the C=O bond vary depending on the structure of carboxylic acids. The C=O bond of the free carboxylic acids shows a higher stretching frequency, 1760 cm−1, while H-bonded carboxylic acids (dimers) exhibit stretching absorptions at a lower frequency, 1710 cm−1. The C=O bond of the...
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