  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.
