Lateral optical distortion is present in most optical imaging systems. In coherence scanning interferometry, distortion may cause field-dependent systematic errors in the measurement of surface topography. These errors become critical when high-precision surfaces, e.g. precision optics, are measured. Current calibration and correction methods for distortion require some form of calibration artefact that has a smooth local surface and a grid of high-precision manufactured features. Moreover, to ensure high accuracy and precision of the absolute and relative locations of the features of these artefacts, requires their positions to be determined using a traceable measuring instrument, e.g. a metrological atomic force microscope. Thus, the manufacturing and calibration processes for calibration artefacts are often expensive and complex. In this paper, we demonstrate for the first time the calibration and correction of optical distortion in a coherence scanning interferometer system by using an arbitrary surface that contains some deviations from flat and has some features (possibly just contamination), such that feature detection is possible. By using image processing and a self-calibration technique, a precision of a few nanometres is achieved for the distortion correction. An inexpensive metal surface, e.g. the surface of a coin, or a scratched and defected mirror, which can be easily found in a laboratory or workshop, may be used. The cost of the distortion correction with nanometre level precision is reduced to almost zero if the absolute scale is not required. Although an absolute scale is still needed to make the calibration traceable, the problem of obtaining the traceability is simplified as only a traceable measure of the distance between two arbitrary points is needed. Thus, the total cost of transferring the traceability may also be reduced significantly using the proposed method.
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http://dx.doi.org/10.1364/OE.25.018703 | DOI Listing |
Transl Vis Sci Technol
January 2025
Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA.
Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.
Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).
Invest Ophthalmol Vis Sci
January 2025
Department of Ophthalmology, New York Eye and Ear Infirmary of Mount Sinai, New York, New York, United States.
Purpose: To assess the preferential sites of retinal capillary occlusion at the parafovea in patients with sickle cell disease (SCD) using optical coherence tomography angiography (OCT-A).
Methods: OCT-A scans from 107 patients with SCD and 51 race-matched unaffected controls were obtained using a commercial spectral domain-OCT system. At least eight sequential 3 × 3 mm scans centered at the fovea were acquired and averaged for image analysis.
CNS Neurosci Ther
January 2025
Department of Neurology, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: Multiple sclerosis (MS) is an autoimmune disorder affecting the central nervous system, with varying clinical manifestations such as optic neuritis, sensory disturbances, and brainstem syndromes. Disease progression is monitored through methods like MRI scans, disability scales, and optical coherence tomography (OCT), which can detect retinal thinning, even in the absence of optic neuritis. MS progression involves neurodegeneration, particularly trans-synaptic degeneration, which extends beyond the initial injury site.
View Article and Find Full Text PDFCurr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
Br J Ophthalmol
January 2025
Fundacio Clinic per la Recerca Biomedica, Barcelona, Spain
Aim: To evaluate the impact of fluid volume fluctuations quantified with artificial intelligence in optical coherence tomography scans during the maintenance phase and visual outcomes at 12 and 24 months in a real-world, multicentre, national cohort of treatment-naïve neovascular age-related macular degeneration (nAMD) eyes.
Methods: Demographics, visual acuity (VA) and number of injections were collected using the Fight Retinal Blindness tool. Intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), total fluid (TF) and central subfield thickness (CST) were quantified using the RetinAI Discovery tool.
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