Windowed eigen-decomposition algorithm for motion artifact reduction in optical coherence tomography-based angiography

Tianyu Zhang, Kanheng Zhou, Holly R. Rocliffe, Antonella Pellicoro, Jenna L. Cash, Wendy Wang, Zhiqiong Wang, Chunhui Li (Lead / Corresponding author), Zhihong Huang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
33 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number378
Number of pages15
JournalApplied Sciences
Volume13
Issue number1
Early online date28 Dec 2022
DOIs
Publication statusPublished - 1 Jan 2023

Keywords

  • optical coherence tomography (OCT)
  • optical coherence tomography-based angiography (OCTA)
  • eigen-decomposition (ED)
  • motion artifact

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