PMID- 32010512
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200928
IS  - 2156-7085 (Print)
IS  - 2156-7085 (Electronic)
IS  - 2156-7085 (Linking)
VI  - 11
IP  - 1
DP  - 2020 Jan 1
TI  - Gabor optical coherence tomographic angiography (GOCTA) (Part II): theoretical 
      basis of sensitivity improvement and optimization for processing speed.
PG  - 227-239
LID - 10.1364/BOE.380287 [doi]
AB  - We previously proposed a Gabor optical coherence tomography angiography (GOCTA) 
      algorithm for spectral domain optical coherence tomography (SDOCT) to extract 
      microvascular signals from spectral fringes directly, with speed improvement of 4 
      to 20 times over existing methods. In this manuscript, we explored the 
      theoretical basis of GOCTA with comparison of experimental data using solid and 
      liquid displacement sample targets, demonstrating that the majority of the GOCTA 
      sensitivity advantage over speckle variance based techniques was in the small 
      displacement range (< 10  approximately  20 microm) of the moving target (such as red blood cells). 
      We further normalized GOCTA signal by root-mean-square (RMS) of original fringes, 
      achieving a more uniform image quality, especially at edges of blood vessels 
      where slow flow could occur. Furthermore, by transecting the spectral fringes and 
      using skipped convolution, the data processing speed could be further improved. 
      We quantified the trade-off in signal-to-noise-ratio (SNR) and 
      contrast-to-noise-ratio (CNR) under various sub-spectral bands and found an 
      optimized condition using 1/4 spectral band for minimal angiography image quality 
      degradation, yet achieving a further 26.7 and 34 times speed improvement on GPU 
      and CPU, respectively. Our optimized GOCTA algorithm has a speed advantage of 
      over 140 times compared to existing speckle variance OCT (SVOCT) method.
CI  - (c) 2019 Optical Society of America under the terms of the OSA Open Access 
      Publishing Agreement.
FAU - Chen, Chaoliang
AU  - Chen C
AD  - Biophotonics and Bioengineering Lab, Department of Electrical, Computer, and 
      Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada.
FAU - Shi, Weisong
AU  - Shi W
AD  - Biophotonics and Bioengineering Lab, Department of Electrical, Computer, and 
      Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada.
AD  - Department of Optical Engineering, Nanjing University of Science and Technology, 
      Nanjing, Jiangsu, China.
FAU - Ramjist, Joel
AU  - Ramjist J
AD  - Biophotonics and Bioengineering Lab, Department of Electrical, Computer, and 
      Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada.
FAU - Yang, Victor X D
AU  - Yang VXD
AD  - Biophotonics and Bioengineering Lab, Department of Electrical, Computer, and 
      Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada.
AD  - Division of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, Ontario, 
      Canada.
AD  - Division of Neurosurgery, Faculty of Medicine, University of Toronto, Toronto, 
      Ontario, Canada.
LA  - eng
PT  - Journal Article
DEP - 20191211
PL  - United States
TA  - Biomed Opt Express
JT  - Biomedical optics express
JID - 101540630
PMC - PMC6968745
COIS- The authors declare that there are no conflicts of interest related to this work.
EDAT- 2020/02/06 06:00
MHDA- 2020/02/06 06:01
PMCR- 2020/01/01
CRDT- 2020/02/04 06:00
PHST- 2019/10/17 00:00 [received]
PHST- 2019/12/05 00:00 [revised]
PHST- 2019/12/05 00:00 [accepted]
PHST- 2020/02/04 06:00 [entrez]
PHST- 2020/02/06 06:00 [pubmed]
PHST- 2020/02/06 06:01 [medline]
PHST- 2020/01/01 00:00 [pmc-release]
AID - 380287 [pii]
AID - 10.1364/BOE.380287 [doi]
PST - epublish
SO  - Biomed Opt Express. 2019 Dec 11;11(1):227-239. doi: 10.1364/BOE.380287. 
      eCollection 2020 Jan 1.