Wavenumber-space wavefront sensorless adaptive-optics for optical coherence tomography

Adaptive-optics optical coherence tomography (AO-OCT) allows the visualization of cellular-scale retinal structures; however, its adoption both at research and clinical levels has been restricted by hardware and software complexity. Based on the observation that aberrations other than defocus are depth-independent, we propose an approach for wavefront sensorless AO-OCT that utilizes the interferometric fringe modulation in wavenumber (<i>k</i>-) space to optimize the wavefront correction. This approach avoids the need for tomogram reconstruction at each optimization iteration and increases robustness against axial motion. The proposed routine combines <i>k</i>-space optimization with focal plane shifting (i.e., defocus optimization) and evaluates the objective function B-scan-wise, achieving 8 Zernike modes correction in ∼1.89 s. Experimental testing with a phantom model eye and computational complexity analysis show the proposed algorithm has a lower computational complexity and faster optimization time per mode while performing at least as well as depth-resolved optimization, using a LabVIEW implementation without the need for high-performance dedicated software or GPU acceleration. We demonstrate its performance in human retinal imaging <i>in vivo</i>.

Authors

Institutions

Publication Details

Journal
Biomedical Optics Express
Published
2025-12-08
DOI
https://doi.org/10.1364/boe.582534
Citations
1
Primary Topic
Optical Coherence Tomography Applications
Type
article
Field-Weighted Citation Impact
0.67
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Wavenumber-space wavefront sensorless adaptive-optics for optical coherence tomography

Brett Bouma, Néstor Uribe‐Patarroyo, Sebastián Ruiz Lopera, David Veysset
1 citations
Biomedical Optics Express
Optical Coherence Tomography Applications
0.67
article

Wavenumber-space wavefront sensorless adaptive-optics for optical coherence tomography

Brett Bouma, Néstor Uribe‐Patarroyo, Sebastián Ruiz Lopera, David Veysset
article en
1 citations

Abstract

Adaptive-optics optical coherence tomography (AO-OCT) allows the visualization of cellular-scale retinal structures; however, its adoption both at research and clinical levels has been restricted by hardware and software complexity. Based on the observation that aberrations other than defocus are depth-independent, we propose an approach for wavefront sensorless AO-OCT that utilizes the interferometric fringe modulation in wavenumber (<i>k</i>-) space to optimize the wavefront correction. This approach avoids the need for tomogram reconstruction at each optimization iteration and increases robustness against axial motion. The proposed routine combines <i>k</i>-space optimization with focal plane shifting (i.e., defocus optimization) and evaluates the objective function B-scan-wise, achieving 8 Zernike modes correction in ∼1.89 s. Experimental testing with a phantom model eye and computational complexity analysis show the proposed algorithm has a lower computational complexity and faster optimization time per mode while performing at least as well as depth-resolved optimization, using a LabVIEW implementation without the need for high-performance dedicated software or GPU acceleration. We demonstrate its performance in human retinal imaging <i>in vivo</i>.

Biomedical Optics ExpressVol. 17(1)
Harvard University (US), Massachusetts General Hospital (US), Massachusetts Institute of Technology (US)
Openalex Percentile: Top 24%
Optical Coherence Tomography Applications
0.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.