Abstract
Optical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.
Highlights
Optical coherence tomography (OCT) is a non-invasive imaging modality that can provide three-dimensional (3D) information of optical scattering properties of biological samples
We have experienced the emergence of deeplearning-based image reconstruction and enhancement methods[21,22,23] to advance optical microscopy techniques, performing e.g., image super resolution[23,24,25,26,27,28], autofocusing[29,30,31], depth of field enhancement[32,33,34], holographic image reconstruction, and phase recovery[35,36,37,38], among many others[39,40,41,42]. Inspired by these applications of deep learning and neural networks in optical microscopy, here we demonstrate the use of deep learning to reconstruct swept-source OCT (SS-OCT) images using undersampled spectral data points
OCT image reconstruction framework, which we term DL-OCT, we trained and tested a deep neural network using SS-OCT images acquired on mouse embryo samples
Summary
Optical coherence tomography (OCT) is a non-invasive imaging modality that can provide three-dimensional (3D) information of optical scattering properties of biological samples. The first generation of OCT systems were based on time-domain (TD) imaging[1], using mechanical path-length scanning. The introduction of the Fourier Domain (FD) OCT techniques[2,3] with higher sensitivity[4,5] has contributed to a dramatic increase in imaging speed and quality[6]. Modern FDOCT systems can routinely achieve line rates of 50–400 kHz7–12 and there have been recent research efforts to further improve the speed of A-scans to tens of MHz13,14. Some of these advances employed hardware modifications to the optical set-up to improve OCT imaging speed and quality, and focused on, e.g., improving
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