Optical Coherence Tomography: Principles, Image Acquisition, and Assessment

2017 ◽  
pp. 1-13
Author(s):  
Annapoorna Kini ◽  
Jagat Narula ◽  
Yuliya Vengrenyuk ◽  
Samin Sharma
Keyword(s):  
Optical Coherence Tomography ◽  
Image Acquisition ◽  
Optical Coherence

scholarly journals Intracoronary optical coherence tomography, basic theory and image acquisition techniques

2011 ◽  
Vol 27 (2) ◽  
pp. 251-258 ◽  
Author(s):  
F. Prati ◽  
M. W. Jenkins ◽  
A. Di Giorgio ◽  
A. M. Rollins
Keyword(s):  
Optical Coherence Tomography ◽  
Image Acquisition ◽  
Basic Theory ◽  
Optical Coherence

scholarly journals Identification of Coronary Calcifications in Optical Coherence Tomography Imaging Using Deep Learning

2020 ◽  
Author(s):  
Yarden Avital ◽  
Akiva Madar ◽  
Shlomi Arnon ◽  
Edward Koifman
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Image Acquisition ◽  
Percutaneous Treatment ◽  
Optical Coherence ◽  
Optimal Method ◽  
Deep Learning Algorithm ◽  
Coronary Calcifications ◽  
Artery Disease

Abstract Coronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is optical coherence tomography (coronary OCT). To identify calcification on OCT and subsequently tailor the appropriate treatment, requires expertise in both image acquisition and interpretation. Image acquisition consists from system calibration, blood clearance by a contrast agent along with synchronization of the pullback process. Accurate interpretation demands careful review by the operator of a segment of 50-75mm of the coronary vessel at steps of 0.5-1mm accounting for 75-100 images in each OCT run, which is time consuming and necessitates some expertise in OCT analysis.In this paper we developed a new deep learning algorithm to assist the physician to identify and quantify coronary calcifications promptly, efficiently and accurately. Our algorithm achieves an accuracy of 0.9903 ± 0.009 over the test set at size of 1500 frames and even managed to find calcifications that weren’t recognized manually by the physician. For the best knowledge of the authors our algorithm achieves high accuracy which was never achieved in the past.

scholarly journals Safety and Usefulness of Non-Occlusion Image Acquisition Technique for Optical Coherence Tomography

2008 ◽  
Vol 72 (9) ◽  
pp. 1536-1537 ◽  
Author(s):  
Hideaki Kataiwa ◽  
Atsushi Tanaka ◽  
Hironori Kitabata ◽  
Toshio Imanishi ◽  
Takashi Akasaka
Keyword(s):  
Optical Coherence Tomography ◽  
Image Acquisition ◽  
Optical Coherence ◽  
Acquisition Technique

scholarly journals An assessment of the quality of optical coherence tomography image acquisition

2020 ◽  
Vol 36 (6) ◽  
pp. 1013-1020
Author(s):  
Elder Iarossi Zago ◽  
Abdul Jawwad Samdani ◽  
Gabriel Tensol Rodrigues Pereira ◽  
Armando Vergara-Martel ◽  
Mohamad Amer Alaiti ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Acquisition ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

scholarly journals Cross-correlation-based image acquisition technique for manually-scanned optical coherence tomography

2009 ◽  
Vol 17 (10) ◽  
pp. 8125 ◽  
Author(s):  
Adeel Ahmad ◽  
Steven G. Adie ◽  
Eric J. Chaney ◽  
Utkarsh Sharma ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Cross Correlation ◽  
Image Acquisition ◽  
Optical Coherence ◽  
Acquisition Technique

scholarly journals Enhanced Visualization of Retinal Microvasculature in Optical Coherence Tomography Angiography Imaging via Deep Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 1322 ◽  
Author(s):  
Shin Kadomoto ◽  
Akihito Uji ◽  
Yuki Muraoka ◽  
Tadamichi Akagi ◽  
Akitaka Tsujikawa
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optical Coherence Tomography Angiography ◽  
Image Acquisition ◽  
Acquisition Time ◽  
Single Shot ◽  
Optical Coherence ◽  
Original Image ◽  
Noise Ratio ◽  
Denoised Image

Background: To investigate the effects of deep learning denoising on quantitative vascular measurements and the quality of optical coherence tomography angiography (OCTA) images. Methods: U-Net-based deep learning denoising with an averaged OCTA data set as teacher data was used in this study. One hundred and thirteen patients with various retinal diseases were examined. An OCT HS-100 (Canon inc., Tokyo, Japan) performed a 3 × 3 mm2 superficial capillary plexus layer slab scan centered on the fovea 10 times. A single-shot image was defined as the original image and the 10-frame averaged image and denoised image generated from the original image using deep learning denoising for the analyses were obtained. The main parameters measured were the OCTA image acquisition time, contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), vessel density (VD), vessel length density (VLD), vessel diameter index (VDI), and fractal dimension (FD) of the original, averaged, and denoised images. Results: One hundred and twelve eyes of 108 patients were studied. Deep learning denoising removed the background noise and smoothed the rough vessel surface. The image acquisition times for the original, averaged, and denoised images were 16.6 ± 2.4, 285 ± 38, and 22.1 ± 2.4 s, respectively (P < 0.0001). The CNR and PSNR of the denoised image were significantly higher than those of the original image (P < 0.0001). There were significant differences in the VLD, VDI, and FD (P < 0.0001) after deep learning denoising. Conclusions: The deep learning denoising method achieved high speed and high quality OCTA imaging. This method may be a viable alternative to the multiple image averaging technique.

Evaluation of potential image acquisition pitfalls during optical coherence tomography and their influence on retinal image segmentation

2007 ◽  
Vol 12 (4) ◽  
pp. 041209 ◽  
Author(s):  
Gábor Márk Somfai ◽  
Harry M. Salinas ◽  
Carmen A. Puliafito ◽  
Delia Cabrera Fernández
Keyword(s):  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Retinal Image ◽  
Image Acquisition ◽  
Optical Coherence ◽  
Retinal Image Segmentation
Sign in / Sign up

Export Citation Format

Share Document