# Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning

arXiv: https://arxiv.org/abs/2204.11769 · HTML: https://mlsanity.com/paper/2204.11769/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Meta-SR: A Magnification-Arbitrary Network for Super-Resolution](https://mlsanity.com/paper/1903.00875/) (2019)
- [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (2018)
- [Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data](https://mlsanity.com/paper/2103.03877/) (2021)
- [Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography](https://mlsanity.com/paper/2003.06035/) (2020)
- [High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning](https://mlsanity.com/paper/1910.05498/) (2019)
- [Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning](https://mlsanity.com/paper/1910.01344/) (2019)

## Built on by (most cited first, 2 of 2)

- [Structural constrained virtual histology staining for human coronary imaging using deep learning](https://mlsanity.com/paper/2211.06737/) (2022)
- [Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network](https://mlsanity.com/paper/2307.11130/) (2023)

## Abstract (arXiv)

Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/ features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain while maintaining high quality in image reconstruction. The down-scaling schedule boosts data acquisition speed without any hardware modifications. Additionally, we propose a unified multi-scale reconstruction framework, namely Multiscale- Spectral-Spatial-Magnification Network (MSSMN), to resolve highly down-scaled (compressed) OCT images with flexible magnification factors. We incorporate the proposed methods into Spectral Domain OCT (SD-OCT) imaging of human coronary samples with clinical features such as stent and calcified lesions. Our experimental results demonstrate that spectral-spatial downscaled data can be better reconstructed than data that is downscaled solely in either spectral or spatial domain. Moreover, we observe better reconstruction performance using MSSMN than using existing reconstruction methods. Our acquisition method and multi-scale reconstruction framework, in combination, may allow faster SD-OCT inspection with high resolution during coronary intervention.