GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer
The image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) could be achieved by iterative methods, which offer a more accurate estimation than the traditional inverse discrete Fourier transform (IDFT) reconstruction. However, the existing iterative methods are mostly A-line-ba...
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sg-ntu-dr.10356-1714702023-10-27T15:31:47Z GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer Wang, Mengyuan Ling, Yuye Dong, Zhenxing Yao, Xinwen Gan, Yu Zhou, Chuanqing Su, Yikai School of Chemistry, Chemical Engineering and Biotechnology Engineering::Electrical and electronic engineering Optical-coherence Tomography Ultrahigh-resolution The image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) could be achieved by iterative methods, which offer a more accurate estimation than the traditional inverse discrete Fourier transform (IDFT) reconstruction. However, the existing iterative methods are mostly A-line-based and are developed on CPU, which causes slow reconstruction. Besides, A-line-based reconstruction makes the iterative methods incompatible with most existing image-level image processing techniques. In this paper, we proposed an iterative method that enables B-scan-based OCT image reconstruction, which has three major advantages: (1) Large-scale parallelism of the OCT dataset is achieved by using GPU acceleration. (2) A novel image-level cross-domain regularizer was developed, such that the image processing could be performed simultaneously during the image reconstruction; an enhanced image could be directly generated from the OCT interferogram. (3) The scalability of the proposed method was demonstrated for 3D OCT image reconstruction. Compared with the state-of-the-art (SOTA) iterative approaches, the proposed method achieves higher image quality with reduced computational time by orders of magnitude. To further show the image enhancement ability, a comparison was conducted between the proposed method and the conventional workflow, in which an IDFT reconstructed OCT image is later processed by a total variation-regularized denoising algorithm. The proposed method can achieve a better performance evaluated by metrics such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), while the speed is improved by more than 30 times. Real-time image reconstruction at more than 20 B-scans per second was realized with a frame size of 4096 (axial) × 1000 (lateral), which showcases the great potential of the proposed method in real-world applications. Published version This work was funded by National Natural Science Foundation of China (61905141, 61875123); Open Research Fund Program of the State Key Laboratory of Low-Dimensional Quantum Physics (KF202107). 2023-10-26T01:00:59Z 2023-10-26T01:00:59Z 2023 Journal Article Wang, M., Ling, Y., Dong, Z., Yao, X., Gan, Y., Zhou, C. & Su, Y. (2023). GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer. Optics Express, 31(2), 1813-1831. https://dx.doi.org/10.1364/OE.478970 1094-4087 https://hdl.handle.net/10356/171470 10.1364/OE.478970 36785208 2-s2.0-85146086148 2 31 1813 1831 en Optics Express © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement. application/pdf |
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Engineering::Electrical and electronic engineering Optical-coherence Tomography Ultrahigh-resolution Wang, Mengyuan Ling, Yuye Dong, Zhenxing Yao, Xinwen Gan, Yu Zhou, Chuanqing Su, Yikai GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer |
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The image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) could be achieved by iterative methods, which offer a more accurate estimation than the traditional inverse discrete Fourier transform (IDFT) reconstruction. However, the existing iterative methods are mostly A-line-based and are developed on CPU, which causes slow reconstruction. Besides, A-line-based reconstruction makes the iterative methods incompatible with most existing image-level image processing techniques. In this paper, we proposed an iterative method that enables B-scan-based OCT image reconstruction, which has three major advantages: (1) Large-scale parallelism of the OCT dataset is achieved by using GPU acceleration. (2) A novel image-level cross-domain regularizer was developed, such that the image processing could be performed simultaneously during the image reconstruction; an enhanced image could be directly generated from the OCT interferogram. (3) The scalability of the proposed method was demonstrated for 3D OCT image reconstruction. Compared with the state-of-the-art (SOTA) iterative approaches, the proposed method achieves higher image quality with reduced computational time by orders of magnitude. To further show the image enhancement ability, a comparison was conducted between the proposed method and the conventional workflow, in which an IDFT reconstructed OCT image is later processed by a total variation-regularized denoising algorithm. The proposed method can achieve a better performance evaluated by metrics such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), while the speed is improved by more than 30 times. Real-time image reconstruction at more than 20 B-scans per second was realized with a frame size of 4096 (axial) × 1000 (lateral), which showcases the great potential of the proposed method in real-world applications. |
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School of Chemistry, Chemical Engineering and Biotechnology |
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School of Chemistry, Chemical Engineering and Biotechnology Wang, Mengyuan Ling, Yuye Dong, Zhenxing Yao, Xinwen Gan, Yu Zhou, Chuanqing Su, Yikai |
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Article |
author |
Wang, Mengyuan Ling, Yuye Dong, Zhenxing Yao, Xinwen Gan, Yu Zhou, Chuanqing Su, Yikai |
author_sort |
Wang, Mengyuan |
title |
GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer |
title_short |
GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer |
title_full |
GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer |
title_fullStr |
GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer |
title_full_unstemmed |
GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer |
title_sort |
gpu-accelerated iterative method for fd-oct image reconstruction with an image-level cross-domain regularizer |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/171470 |
_version_ |
1781793792169345024 |