# Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

arXiv: https://arxiv.org/abs/2103.03877 · HTML: https://mlsanity.com/paper/2103.03877/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://mlsanity.com/paper/1705.04286/) (2017)
- [Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning](https://mlsanity.com/paper/1901.11252/) (2019)
- [Single-shot autofocusing of microscopy images using deep learning](https://mlsanity.com/paper/2003.09585/) (2020)
- [Deep Learning Microscopy](https://mlsanity.com/paper/1705.04709/) (2017)
- [Deep learning-based holographic polarization microscopy](https://mlsanity.com/paper/2007.00741/) (2020)
- [Recurrent neural network-based volumetric fluorescence microscopy](https://mlsanity.com/paper/2010.10781/) (2020)
- [Resolution enhancement in scanning electron microscopy using deep learning](https://mlsanity.com/paper/1901.11094/) (2019)
- [Deep learning-based color holographic microscopy](https://mlsanity.com/paper/1907.06727/) (2019)
- [Deep learning-based virtual refocusing of images using an engineered point-spread function](https://mlsanity.com/paper/2012.11892/) (2020)
- [Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery](https://mlsanity.com/paper/1803.08138/) (2018)
- [Deep learning approach to Fourier ptychographic microscopy](https://mlsanity.com/paper/1805.00334/) (2018)

## Built on by (most cited first, 6 of 6)

- [Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data](https://mlsanity.com/paper/2403.14324/) (2024)
- [Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning](https://mlsanity.com/paper/2204.11769/) (2022)
- [Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network](https://mlsanity.com/paper/2307.11130/) (2023)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning](https://mlsanity.com/paper/2301.00504/) (2023)
- [Reconstruction of Optical Coherence Tomography Images from Wavelength-space Using Deep-learning](https://mlsanity.com/paper/2509.18783/) (2025)

## Abstract (arXiv)

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 set-up 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 ~6.73 ms using a desktop computer, 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 3x undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2x spectral undersampling. 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.