Three-dimensional characterization of optical coherence tomography point spread functions with a nanoparticle-embedded phantom

2010 ◽  
Vol 35 (13) ◽  
pp. 2269 ◽  
Author(s):  
Anant Agrawal ◽  
T. Joshua Pfefer ◽  
Naureen Gilani ◽  
Rebekah Drezek
The Analyst ◽  
2020 ◽  
Vol 145 (4) ◽  
pp. 1445-1456 ◽  
Author(s):  
Fabian Placzek ◽  
Eliana Cordero Bautista ◽  
Simon Kretschmer ◽  
Lara M. Wurster ◽  
Florian Knorr ◽  
...  

Characterization of bladder biopsies, using a combined fiber optic probe-based optical coherence tomography and Raman spectroscopy imaging system that allows a large field-of-view imaging and detection and grading of cancerous bladder lesions.


Heliyon ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e06645
Author(s):  
Charlotte Theresa Trebing ◽  
Sinan Sen ◽  
Stefan Rues ◽  
Christopher Herpel ◽  
Maria Schöllhorn ◽  
...  

2021 ◽  
pp. 153537022110285
Author(s):  
Hao Zhou ◽  
Tommaso Bacci ◽  
K Bailey Freund ◽  
Ruikang K Wang

The choroid provides nutritional support for the retinal pigment epithelium and photoreceptors. Choroidal dysfunction plays a major role in several of the most important causes of vision loss including age-related macular degeneration, myopic degeneration, and pachychoroid diseases such as central serous chorioretinopathy and polypoidal choroidal vasculopathy. We describe an imaging technique using depth-resolved swept-source optical coherence tomography (SS-OCT) that provides full-thickness three-dimensional (3D) visualization of choroidal anatomy including topographical features of individual vessels. Enrolled subjects with different clinical manifestations within the pachychoroid disease spectrum underwent 15 mm × 9 mm volume scans centered on the fovea. A fully automated method segmented the choroidal vessels using their hyporeflective lumens. Binarized choroidal vessels were rendered in a 3D viewer as a vascular network within a choroidal slab. The network of choroidal vessels was color depth-encoded with a reference to the Bruch’s membrane segmentation. Topographical features of the choroidal vasculature were characterized and compared with choroidal imaging obtained with indocyanine green angiography (ICGA) from the same subject. The en face SS-OCT projections of the larger choroid vessels closely resembled to that obtained with ICGA, with the automated SS-OCT approach proving additional depth-encoded 3D information. In 16 eyes with pachychoroid disease, the SS-OCT approach added clinically relevant structural details, including choroidal thickness and vessel depth, which the ICGA studies could not provide. Our technique appears to advance the in vivo visualization of the full-thickness choroid, successfully reveals the topographical features of choroidal vasculature, and shows potential for further quantitative analysis when compared with other choroidal imaging techniques. This improved visualization of choroidal vasculature and its 3D structure should provide an insight into choroid-related disease mechanisms as well as their responses to treatment.


2021 ◽  
Vol 11 (7) ◽  
pp. 3119
Author(s):  
Cristina L. Saratxaga ◽  
Jorge Bote ◽  
Juan F. Ortega-Morán ◽  
Artzai Picón ◽  
Elena Terradillos ◽  
...  

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.


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