Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography

Ren, Jiaxiang; Park, Kicheon; Pan, Yingtian; Ling, Haibin

doi:10.1109/cvpr52688.2022.01996

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides finding solutions in hardware to reduce motion artifacts, a number of methods were proposed, such as using artificial intelligence [27], Fourier spatial frequency information [28], a regression-based algorithm [29], filters and morphological operations [30], an adaptive classifier [31], tensor voting [32], and automatic registration [33][34][35]. However, these are post-processing methods.…”

Section: Introductionmentioning

confidence: 99%

Windowed Eigen-Decomposition Algorithm for Motion Artifact Reduction in Optical Coherence Tomography-Based Angiography

et al. 2022

View full text Add to dashboard Cite

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.

show abstract