TY - JOUR
T1 - Windowed eigen-decomposition algorithm for motion artifact reduction in optical coherence tomography-based angiography
AU - Zhang, Tianyu
AU - Zhou, Kanheng
AU - Rocliffe, Holly R.
AU - Pellicoro, Antonella
AU - Cash, Jenna L.
AU - Wang, Wendy
AU - Wang, Zhiqiong
AU - Li, Chunhui
AU - Huang, Zhihong
N1 - Funding Information:
The establishment of the mouse model was funded by a Wellcome Trust and Royal Society Sir Henry Dale Fellowship to Dr. Cash (202581/Z/16/Z), and a Chancellors Fellowship PhD studentship, which was funded through Dr. Cash’s fellowship, to Dr. Holly Rocliffe.
Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Optical coherence tomography-based angiography (OCTA) has attracted attention in clinical applications as a non-invasive and high-resolution imaging modality. Motion artifacts are the most seen artifact in OCTA. Eigen-decomposition (ED) algorithms are popular choices for OCTA reconstruction, but have limitations in the reduction of motion artifacts. The OCTA data do not meet one of the requirements of ED, which is that the data should be normally distributed. To overcome this drawback, we propose an easy-to-deploy development of ED, windowed-ED (wED). wED applies a moving window to the input data, which can contrast the blood-flow signals with significantly reduced motion artifacts. To evaluate our wED algorithm, pre-acquired dorsal wound healing data in a murine model were used. The ideal window size was optimized by fitting the data distribution with the normal distribution. Lastly, the cross-sectional and en face results were compared among several OCTA reconstruction algorithms, Speckle Variance, A-scan ED (aED), B-scan ED, and wED. wED could reduce the background noise intensity by 18% and improve PSNR by 4.6%, compared to the second best-performed algorithm, aED. This study can serve as a guide for utilizing wED to reconstruct OCTA images with an optimized window size.
AB - Optical coherence tomography-based angiography (OCTA) has attracted attention in clinical applications as a non-invasive and high-resolution imaging modality. Motion artifacts are the most seen artifact in OCTA. Eigen-decomposition (ED) algorithms are popular choices for OCTA reconstruction, but have limitations in the reduction of motion artifacts. The OCTA data do not meet one of the requirements of ED, which is that the data should be normally distributed. To overcome this drawback, we propose an easy-to-deploy development of ED, windowed-ED (wED). wED applies a moving window to the input data, which can contrast the blood-flow signals with significantly reduced motion artifacts. To evaluate our wED algorithm, pre-acquired dorsal wound healing data in a murine model were used. The ideal window size was optimized by fitting the data distribution with the normal distribution. Lastly, the cross-sectional and en face results were compared among several OCTA reconstruction algorithms, Speckle Variance, A-scan ED (aED), B-scan ED, and wED. wED could reduce the background noise intensity by 18% and improve PSNR by 4.6%, compared to the second best-performed algorithm, aED. This study can serve as a guide for utilizing wED to reconstruct OCTA images with an optimized window size.
KW - optical coherence tomography (OCT)
KW - optical coherence tomography-based angiography (OCTA)
KW - eigen-decomposition (ED)
KW - motion artifact
UR - http://www.scopus.com/inward/record.url?scp=85146019838&partnerID=8YFLogxK
U2 - 10.3390/app13010378
DO - 10.3390/app13010378
M3 - Article
SN - 2076-3417
VL - 13
JO - Applied Sciences
JF - Applied Sciences
IS - 1
M1 - 378
ER -