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

TL;DR

This paper introduces a spectral-spatial downscaling method and a multi-scale deep learning framework to enable faster, high-quality OCT imaging of coronary arteries, improving resolution and speed without hardware changes.

Contribution

It proposes a novel spectral-spatial downscaling approach combined with MSSMN, a multi-scale deep learning network for reconstructing highly downscaled OCT images.

Findings

Spectral-spatial downscaling outperforms single-domain downscaling.

MSSMN achieves superior reconstruction quality over existing methods.

The combined approach enables faster coronary OCT imaging with high resolution.

Abstract

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.