scholarly journals Optical Interferometric Fringe Pattern-Incorporated Spectrum Calibration Technique for Enhanced Sensitivity of Spectral Domain Optical Coherence Tomography

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2067
Author(s):  
Sangyeob Han ◽  
Ruchire Eranga Wijesinghe ◽  
Deokmin Jeon ◽  
Youngmin Han ◽  
Jaeyul Lee ◽  
...  

Depth-visualizing sensitivity can be degraded due to imperfect optical alignment and non-equidistant distribution of optical signals in the pixel array, which requires a measurement of the re-sampling process. To enhance this depth-visualizing sensitivity, reference and sample arm-channeled spectra corresponding to different depths using mirrors were obtained to calibrate the spectrum sampling prior to Fourier transformation. During the process, eight interferogram patterns corresponding to point spread function (PSF) signals at eight optical path length differences were acquired. To calibrate the spectrum, generated intensity points of the original interferogram were re-indexed towards a maximum intensity range, and these interferogram re-indexing points were employed to generate a new lookup table. The entire software-based process consists of eight consecutive steps. Experimental results revealed that the proposed method can achieve images with a high depth-visualizing sensitivity. Furthermore, the results validate the proposed method as a rapidly performable spectral calibration technique, and the real-time images acquired using our technique confirm the simplicity and applicability of the method to existing optical coherence tomography (OCT) systems. The sensitivity roll-off prior to the spectral calibration was measured as 28 dB and it was halved after the calibration process.

2009 ◽  
Vol 53 (4) ◽  
pp. 315-326 ◽  
Author(s):  
Alexandre R. Tumlinson ◽  
Boris Hermann ◽  
Bernd Hofer ◽  
Boris Považay ◽  
Tom H. Margrain ◽  
...  

2011 ◽  
Vol 36 (23) ◽  
pp. 4575 ◽  
Author(s):  
Alireza Akhlagh Moayed ◽  
Sepideh Hariri ◽  
Vivian Choh ◽  
Kostadinka Bizheva

Optik ◽  
2010 ◽  
Vol 121 (11) ◽  
pp. 965-970 ◽  
Author(s):  
Chao Ding ◽  
Peng Bu ◽  
Xiangzhao Wang ◽  
Osami Sasaki

2018 ◽  
Vol 45 (2) ◽  
pp. 0207022
Author(s):  
陈艳 Chen Yan ◽  
李中梁 Li Zhongliang ◽  
南楠 Nan Nan ◽  
步扬 Bu Yang ◽  
卢宇 Lu Yu ◽  
...  

2018 ◽  
Vol 45 (6) ◽  
pp. 0607005
Author(s):  
王瑄 Wang Xuan ◽  
李中梁 Li Zhongliang ◽  
南楠 Nan Nan ◽  
步扬 Bu Yang ◽  
曾爱军 Zeng Aijun ◽  
...  

Author(s):  
Huaqi Zhang ◽  
Guanglei Wang ◽  
Yan Li ◽  
Feng Lin ◽  
Yechen Han ◽  
...  

Coronary optical coherence tomography (OCT) is a new high-resolution intravascular imaging technology that clearly depicts coronary artery stenosis and plaque information. Study of coronary OCT images is of significance in the diagnosis of coronary atherosclerotic heart disease (CAD). We introduce a new method based on the convolutional neural network (CNN) and an improved random walk (RW) algorithm for the recognition and segmentation of calcified, lipid and fibrotic plaque in coronary OCT images. First, we design CNN with three different depths (2, 4 or 6 convolutional layers) to perform the automatic recognition and select the optimal CNN model. Then, we device an improved RW algorithm. According to the gray-level distribution characteristics of coronary OCT images, the weights of intensity and texture term in the weight function of RW algorithm are adjusted by an adaptive weight. Finally, we apply mathematical morphology in combination with two RWs to accurately segment the plaque area. Compared with the ground truth of clinical segmentation results, the Jaccard similarity coefficient (JSC) of calcified and lipid plaque segmentation results is 0.864, the average symmetric contour distance (ASCD) is 0.375[Formula: see text]mm, the JSC and ASCD reliabilities are 88.33% and 92.50% respectively. The JSC of fibrotic plaque is 0.876, the ASCD is 0.349[Formula: see text]mm, the JSC and ASCD reliabilities are 90.83% and 95.83% respectively. In addition, the average segmentation time (AST) does not exceed 5 s. Reliable and significantly improved results have been achieved in this study. Compared with the CNN, traditional RW algorithm and other methods. The proposed method has the advantages of fast segmentation, high accuracy and reliability, and holds promise as an aid to doctors in the diagnosis of CAD.


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