Optical coherence refraction tomography

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Abstract

Optical coherence tomography (OCT) is a cross-sectional, micrometre-scale imaging modality with widespread clinical application. Typical OCT systems sacrifice lateral resolution to achieve long depths of focus for bulk tissue imaging, and therefore tend to have better axial than lateral resolution. Such anisotropic resolution can obscure fine ultrastructural features. Furthermore, conventional OCT suffers from refraction-induced image distortions. Here, we introduce optical coherence refraction tomography (OCRT), which extends the superior axial resolution to the lateral dimension, synthesizing undistorted cross-sectional image reconstructions from multiple conventional images acquired with angular diversity. In correcting refraction-induced distortions to register the OCT images, OCRT also achieves spatially resolved refractive index imaging. We demonstrate greater than threefold improvement in lateral resolution as well as speckle reduction in imaging the tissue ultrastructure, consistent with histology. With further optimization in optical designs to incorporate angular diversity into clinical instruments, OCRT could be widely applied as an enhancement over conventional OCT.

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Fig. 1: OCRT, like CT, is a Fourier synthesis technique.
Fig. 2: Overview of the iterative OCRT reconstruction algorithm.
Fig. 3: Experimental validation of the isotropic resolution and RI estimates of OCRT.
Fig. 4: OCRT of mouse vas deferens and femoral artery.
Fig. 5: OCRT of mouse bladder and trachea.
Fig. 6: OCRT of human cornea and crane fly leg reveals additional RI information.

Data availability

The multi-angle datasets for the biological samples in Figs. 46 are available at https://doi.org/10.6084/m9.figshare.8297138.

Code availability

The Python code used for generating the OCRT results in Figs. 46 is available at https://github.com/kevinczhou/optical-coherence-refraction-tomography.

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Acknowledgements

We thank G. Waterman for designing the rotation stage, M. Stern for helpful advice regarding TensorFlow, A. Kuo for providing the human cornea samples and M. Fitzmaurice for helpful discussions regarding the biological data. K.C.Z. was supported by the National Science Foundation (DGF-1106401), J.A.I. was supported in part by the National Institutes of Health (U01EY028079) and S.F. was supported in part by the National Institutes of Health (P30EY005722) and the Google Faculty Research Award.

Author information

K.C.Z. and J.A.I. conceived and developed the idea. R.Q. and S.F. helped to further develop the idea. K.C.Z. developed the algorithms, wrote the code, and collected and analysed the data. R.Q. assisted with data collection and performed the Zemax simulations. All authors contributed to data interpretation. S.D. prepared all biological samples for data collection and histology.

Correspondence to Joseph A. Izatt.

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Competing interests

K.C.Z., R.Q., S.F. and J.A.I. have submitted a patent application for this work, assigned to Duke University. J.A.I. is also an inventor on multiple patents, including those licensed to Leica Microsystems and Carl Zeiss Meditec.

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