Snapshot Coherence Tomographic Imaging

Mu Qiao, Yangyang Sun, Jiawei Ma, Ziyi Meng, Xuan Liu, Xin Yuan

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Article number9463714
Pages (from-to)624-637
Number of pages14
JournalIEEE Transactions on Computational Imaging
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Science Applications
  • Computational Mathematics


  • 3D imaging
  • Compressive sensing
  • coded aperture
  • computational imaging
  • deep learning
  • interferometer


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