Snapshot Coherence Tomographic Imaging

We demonstrate a high-throughput computation-efficient snapshot coherence tomographic imaging method by combining interferometric coding and compressive sampling. We first encode the depth distribution of a three-dimensional (3D) object into the spectrum of a light field, using the principle of optical coherence tomography (OCT), i.e., through a Michaelson interferometer, which generates an intermediate $(x, y, \lambda)$ data-cube that encodes the raw $(x, y, z)$ data of the object. We then sample the spectral data using a well-established compressive spectral imaging technique, called the coded aperture snapshot spectral imaging (CASSI), which yields a compressed 2D $(x, y)$ measurement that captures the whole 3D tomographic information of the object. Finally, a developed iterative algorithm and end-to-end deep learning network are used for tomographic reconstruction from the single 2D measurement. Such integration of OCT and CASSI leads to a physically simple and computationally efficient system, allowing us to implement a large data size of more than $2000\times 2000$ pixels in the transverse dimensions and up to 200 pixels (depth slices) in the axial dimension. Owning to the interferometry-based depth sensing mechanism, we achieve a high axial resolution of up to $13\,\mu m$ within an axial field of view of $\text{1.6}\,mm$. Video-rate visualization of dynamic 3D objects at micrometer scale are shown through several examples.