Computational adaptive optics of the human retina

It is well known that patient-specific ocular aberrations limit imaging resolution in the human retina. Previously, hardware adaptive optics (HAO) has been employed to measure and correct these aberrations to acquire high-resolution images of various retinal structures. While the resulting aberration-corrected images are of great clinical importance, clinical use of HAO has not been widespread due to the cost and complexity of these systems. We present a technique termed computational adaptive optics (CAO) for aberration correction in the living human retina without the use of hardware adaptive optics components. In CAO, complex interferometric data acquired using optical coherence tomography (OCT) is manipulated in post-processing to adjust the phase of the optical wavefront. In this way, the aberrated wavefront can be corrected. We summarize recent results in this technology for retinal imaging, including aberration-corrected imaging in multiple retinal layers and practical considerations such as phase stability and image optimization.

[1]  Adeel Ahmad,et al.  Computational adaptive optics for broadband optical interferometric tomography of biological tissue , 2012, Proceedings of the National Academy of Sciences.

[2]  Nathan D. Shemonski,et al.  Stability in computed optical interferometric tomography (Part II): in vivo stability assessment. , 2014, Optics express.

[3]  D R Williams,et al.  Supernormal vision and high-resolution retinal imaging through adaptive optics. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  Wolfgang Drexler,et al.  Subaperture correlation based digital adaptive optics for full field optical coherence tomography. , 2013, Optics express.

[5]  Steven M. Jones,et al.  High-speed volumetric imaging of cone photoreceptors with adaptive optics spectral-domain optical coherence tomography. , 2006, Optics express.

[6]  Daniel L Marks,et al.  Nonparaxial vector-field modeling of optical coherence tomography and interferometric synthetic aperture microscopy. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  Stephen A. Boppart,et al.  A computational approach to high-resolution imaging of the living human retina without hardware adaptive optics , 2015, Photonics West - Biomedical Optics.

[8]  Stephen A. Boppart,et al.  Computed optical interferometric tomography for high-speed volumetric cellular imaging , 2014 .

[9]  Nathan D. Shemonski,et al.  Stability in computed optical interferometric tomography (part I): stability requirements. , 2014, Optics express.

[10]  Nathan D. Shemonski,et al.  Computational high-resolution optical imaging of the living human retina , 2015, Nature Photonics.

[11]  Nathan D. Shemonski,et al.  Guide-star-based computational adaptive optics for broadband interferometric tomography. , 2012, Applied physics letters.

[12]  Nathan D. Shemonski,et al.  Three-dimensional motion correction using speckle and phase for in vivo computed optical interferometric tomography. , 2014, Biomedical optics express.

[13]  Ravi S. Jonnal,et al.  Imaging retinal nerve fiber bundles using optical coherence tomography with adaptive optics , 2011, Vision Research.