Performance improvement of optical coherence tomography angiography for neuroimaging incorporating cortical segmentation

Zhang Jisheng; Fan Fan; Lianqing Zhu; Zongqing Ma; Jiang Zhu

doi:10.1117/12.2609396

4 March 2022 Performance improvement of optical coherence tomography angiography for neuroimaging incorporating cortical segmentation

Zhang Jisheng, Fan Fan, Lianqing Zhu, Zongqing Ma, Jiang Zhu

Author Affiliations +

Proceedings Volume 11945, Clinical and Translational Neurophotonics 2022; 1194504 (2022) https://doi.org/10.1117/12.2609396
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Optical coherence tomography angiography (OCTA) has been widely used for neuroimaging with non-invasive and high-resolution advantages. However, the signals from the skull and the noise from the deep imaging areas reduce the microvascular clarity in the OCTA projections. Here we proposed a U-Net deep learning method to segment the superficial cortical area from the skull and other tissues for improving the quality of the OCTA projections. The peak signal-to-noise ratio (pSNR) and the average contrast-to-noise ratio (aCNR) were analyzed to evaluate the OCTA projection images. The results showed that the pSNR and aCNR values increased significantly and, thus, the image quality of the microvascular projections was improved after the cortical segmentation.

Conference Presentation

Citation Download Citation

Zhang Jisheng, Fan Fan, Lianqing Zhu, Zongqing Ma, and Jiang Zhu "Performance improvement of optical coherence tomography angiography for neuroimaging incorporating cortical segmentation", Proc. SPIE 11945, Clinical and Translational Neurophotonics 2022, 1194504 (4 March 2022); https://doi.org/10.1117/12.2609396

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