Demonstration of in vivo real-time 3D motion-compensated OCT volumetric imaging using a CNN-based algorithm

Ruizhi Zuo; Kristina Irsch; Jin U. Kang

doi:10.1117/12.2649352

6 March 2023 Demonstration of in vivo real-time 3D motion-compensated OCT volumetric imaging using a CNN-based algorithm

Ruizhi Zuo, Kristina Irsch, Jin U. Kang

Author Affiliations +

Proceedings Volume PC12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI; PC123680I (2023) https://doi.org/10.1117/12.2649352
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

In vivo 3D OCT imaging of live animal generally suffers from motion artifacts due to involuntary tissue movement. Here, we propose a real-time 3D OCT imaging approach using a convolution neural network (CNN)/regression-based algorithm to correct tissue motion in vivo. The system first scans four reference images along the slow axis within millisecond-scale acquisition time before acquiring a C-mode image. The algorithm recognizes the tissue surface by CNN, then uses the segmentation result along with reference images to compensate lateral and axial motion. We evaluated the system performance using a fish eye model.

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Citation Download Citation

Ruizhi Zuo, Kristina Irsch, and Jin U. Kang "Demonstration of in vivo real-time 3D motion-compensated OCT volumetric imaging using a CNN-based algorithm", Proc. SPIE PC12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI, PC123680I (6 March 2023); https://doi.org/10.1117/12.2649352

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KEYWORDS

3D image processing

In vivo imaging

Optical coherence tomography

Natural surfaces

Tissues

Detection and tracking algorithms

Convolution

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