In Fourier-domain optical coherence tomography (FD-OCT), image reconstruction has been extensively studied. This paper addresses the trade-off between reconstruction time and image quality of the optimization methods by proposing an unsupervised deep learning-based approach. Different from the existing learning-based methods, the proposed unsupervised learning method incorporates a neural network as an inverse solver and eliminates the need for large training pairs. A proof-of-concept simulation was conducted, comparing our method with an iterative optimization technique using stochastic gradient descent (SGD). Results show that the proposed method achieves real-time reconstruction with a small decrease in image quality compared to SGD, while enabling real-time reconstruction at a speed of 0.008s per B-scan (125 frames per second). In contrast, the SGD method took 0.32s per B-scan, making it 40 times slower. This deep learning-based method has significant potential for real-time image reconstruction and display in future FD-OCT.
Iterative methods could provide high-quality image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) by solving an inverse problem. Compared with the regular IFFT-based reconstruction, a more accurate estimation could be iteratively solved by integrating prior knowledge, however, it is often more time-consuming. To deal with the time problem, we proposed a fast iterative method for FD-OCT image reconstruction empowered by GPU acceleration. An iterative scheme is adopted, including a forward model and an inverse solver. Large-scale parallelism of OCT image reconstruction is performed on B-scans. We deployed the framework on Nvidia GeForce RTX 3090 graphic card that enables parallel processing. With the widely used toolkit Pytorch, the inverse problem of OCT image reconstruction is solved by the stochastic gradient descent (SGD) algorithm. To validate the effectiveness of the proposed method, we compare the computational time and image quality with other iterative approaches including ADMM, AR, and RFIAA method. The proposed method could provide a significant speed enhancement of 1,500 times with comparable image quality to that of ADMM reconstruction. The result indicates a potential for high-quality real-time volumetric OCT image reconstruction via iterative algorithms.
Here, we analytically study the signal digitization procedure in FD-OCT and propose a novel mixed-signal framework to model its time-domain image formation. It turns out that FD-OCT is a shift-variant system, if the conventional IDFT-based technique is used to reconstruct the A-lines. Specifically, both amplitude and phase responses of the system are dependent on the axial location of the input sample. We believe this finding could provide us with new insights towards the image reconstruction of FD-OCT and guide researchers to develop better reconstruction algorithms in the future.
FD-OCT is a widely used technology which could provide high-resolution 3D reconstruction images. Conventional OCT uses IDFT reconstruction method, which could obtain an FFT-limited axial resolution. Recently, several optimization-based methods have reportedly achieved a resolution improvement, while undesired noise artifacts might appear in their result, which could degrade the image quality. In this work, we proposed an iterative error reduction method in order to remove the artifacts as well as improve the resolution. A numerical simulation is designed to validate our algorithm. Two reconstruction methods including IDFT reconstruction, and l1 minimization are selected for a comparative study. Specifically, we conduct the simulation at four different noise levels. The result shows that our proposed method could greatly suppress the artifact and obtain a great reconstruction even when the SNR is reduced.
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