Journal of biomedical optics · 2026
Improved method for optical coherence tomography angiography: from reconstruction to clinical indicator quantification.
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Cai Fuxin, Feng Xianglong, Zheng Zhihong, Zhang Yuhong, Xu Duo, Ma Chuanwei, Fan Yu, Chen Xinjian
Аннотация
SIGNIFICANCE: Optical coherence tomography angiography (OCTA) represents a significant advance in noninvasive ophthalmic vascular imaging, yet existing reconstruction algorithms face challenges such as spectral leakage and motion artifacts, which can compromise image quality and the accuracy of subsequent clinical quantification. Improving OCTA reconstruction and developing robust, automated methods for clinical indicator extraction are crucial for enhancing diagnostic reliability and facilitating precise disease monitoring.
AIM: We aim to propose and validate an improved OCTA reconstruction method based on a smoothed Walsh window function to reduce spectral leakage while preserving axial resolution, coupled with an enhanced blood flow B-scan signal using retinal layer segmentation. Furthermore, we seek to develop and evaluate a fully automated pipeline for calculating key clinical indicators-including foveal avascular zone (FAZ) parameters and vessel density-based on local fractal dimension analysis.
APPROACH: A spectral-domain OCT system was used to acquire volumetric retinal data from healthy volunteers. The reconstruction method utilized a smoothed Walsh window for full-spectrum splitting to mitigate spectral leakage, combined with a deep learning-based retinal layer segmentation algorithm and Otsu's thresholding to enhance blood flow B-scan signals. For clinical quantification, local fractal dimension analysis was employed to segment vascular networks and FAZ regions automatically, from which perimeter, area, circularity index, and sectoral vessel density were computed.
RESULTS: The proposed reconstruction method demonstrated superior performance compared with traditional OMAG and SSADA algorithms, showing significant improvements in vessel connectivity, contrast (increase up to ∼ 0.74 ), and signal-to-noise ratio (SNR increase up to ∼ 1.09 dB ). The FAZ segmentation based on local fractal dimension achieved a high similarity index of 0.9735 ± 0.0066 with manual ground truth, with low false positive ( 1.76 % ± 0.75 % ) and false negative ( 3.48 % ± 0.78 % ) rates. Intraclass correlation coefficients for FAZ area, perimeter, and circularity index all exceeded 0.90. Vessel density measurements across retinal sectors were consistent with established normative data.
CONCLUSIONS: The integration of a smoothed Walsh window function and retinal layer segmentation significantly enhances OCTA image quality and blood flow signal clarity. The local fractal dimension-based automated analysis pipeline provides accurate, reproducible quantification of FAZ morphology and vessel density, demonstrating strong agreement with manual annotations. This method offers a reliable framework for improving both OCTA reconstruction and the automated derivation of clinical indicators, supporting advanced ophthalmic diagnosis and longitudinal disease assessment.
AIM: We aim to propose and validate an improved OCTA reconstruction method based on a smoothed Walsh window function to reduce spectral leakage while preserving axial resolution, coupled with an enhanced blood flow B-scan signal using retinal layer segmentation. Furthermore, we seek to develop and evaluate a fully automated pipeline for calculating key clinical indicators-including foveal avascular zone (FAZ) parameters and vessel density-based on local fractal dimension analysis.
APPROACH: A spectral-domain OCT system was used to acquire volumetric retinal data from healthy volunteers. The reconstruction method utilized a smoothed Walsh window for full-spectrum splitting to mitigate spectral leakage, combined with a deep learning-based retinal layer segmentation algorithm and Otsu's thresholding to enhance blood flow B-scan signals. For clinical quantification, local fractal dimension analysis was employed to segment vascular networks and FAZ regions automatically, from which perimeter, area, circularity index, and sectoral vessel density were computed.
RESULTS: The proposed reconstruction method demonstrated superior performance compared with traditional OMAG and SSADA algorithms, showing significant improvements in vessel connectivity, contrast (increase up to ∼ 0.74 ), and signal-to-noise ratio (SNR increase up to ∼ 1.09 dB ). The FAZ segmentation based on local fractal dimension achieved a high similarity index of 0.9735 ± 0.0066 with manual ground truth, with low false positive ( 1.76 % ± 0.75 % ) and false negative ( 3.48 % ± 0.78 % ) rates. Intraclass correlation coefficients for FAZ area, perimeter, and circularity index all exceeded 0.90. Vessel density measurements across retinal sectors were consistent with established normative data.
CONCLUSIONS: The integration of a smoothed Walsh window function and retinal layer segmentation significantly enhances OCTA image quality and blood flow signal clarity. The local fractal dimension-based automated analysis pipeline provides accurate, reproducible quantification of FAZ morphology and vessel density, demonstrating strong agreement with manual annotations. This method offers a reliable framework for improving both OCTA reconstruction and the automated derivation of clinical indicators, supporting advanced ophthalmic diagnosis and longitudinal disease assessment.