scholarly journals Optical coherence tomography (OCT) leading to more insight into cochlear mechanics

2015 ◽  
Author(s):  
Egbert de Boer ◽  
Fangyi Chen ◽  
Dingjun Zha ◽  
Alfred L. Nuttall
2017 ◽  
Vol 10 (06) ◽  
pp. 1742007 ◽  
Author(s):  
L. Bartolini ◽  
F. Feroldi ◽  
J. J. A. Weda ◽  
M. Slaman ◽  
J. F. de Boer ◽  
...  

We present a multimodal ferrule-top sensor designed to perform the integrated epidetection of Optical Coherence Tomography (OCT) depth-profiles and micron-scale indentation by all-optical detection. By scanning a sample under the probe, we can obtain structural cross-section images and identify a region-of-interest in a nonhomogeneous sample. Then, with the same probe and setup, we can immediately target that area with a series of spherical-indentation measurements, in which the applied load is known with a [Formula: see text]N precision, the indentation depth with sub-[Formula: see text]m precision and a maximum contact radius of 100[Formula: see text][Formula: see text]m. Thanks to the visualization of the internal structure of the sample, we can gain a better insight into the observed mechanical behavior. The ability to impart a small, confined load, and perform OCT [Formula: see text]-scans at the same time, could lead to an alternative, high transverse resolution, Optical Coherence Elastography (OCE) sensor.


NeuroImage ◽  
2020 ◽  
Vol 214 ◽  
pp. 116704
Author(s):  
Robert Jones ◽  
Giorgia Grisot ◽  
Jean Augustinack ◽  
Caroline Magnain ◽  
David A. Boas ◽  
...  

2021 ◽  
Vol 11 (20) ◽  
pp. 9734
Author(s):  
Kristen M. Meiburger ◽  
Massimo Salvi ◽  
Giulia Rotunno ◽  
Wolfgang Drexler ◽  
Mengyang Liu

Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning.


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