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Volumetric optical coherence microscopy with a high space-bandwidth-time product enabled by hybrid adaptive optics

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Abstract

Optical coherence microscopy (OCM) is a promising modality for high resolution imaging, but has limited ability to capture large-scale volumetric information about dynamic biological processes with cellular resolution. To enhance the throughput of OCM, we implemented a hybrid adaptive optics (hyAO) approach that combines computational adaptive optics with an intentionally aberrated imaging beam generated via hardware adaptive optics. Using hyAO, we demonstrate the depth-equalized illumination and collection ability of an astigmatic beam compared to a Gaussian beam for cellular-resolution imaging. With this advantage, we achieved volumetric OCM with a higher space-bandwidth-time product compared to Gaussian-beam acquisition that employed focus-scanning across depth. HyAO was also used to perform volumetric time-lapse OCM imaging of cellular dynamics over a 1mm × 1mm × 1mm field-of-view with 2 μm isotropic spatial resolution and 3-minute temporal resolution. As hyAO is compatible with both spectral-domain and swept-source beam-scanning OCM systems, significant further improvements in absolute volumetric throughput are possible by use of ultrahigh-speed swept sources.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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Supplementary Material (2)

NameDescription
Visualization 1       Volumetric visualization of fibroblast cell dynamics. The central part has 1mm × 1mm × 1mm field of view, and all the extracted parts have 0.25mm × 0.25mm × 0.25mm field of view.
Visualization 2       En face maximum intensity projection (400 µm slices) of fibroblast cell dynamics. Scale bar represents 50 µm for all sections.

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Figures (6)

Fig. 1
Fig. 1 System diagram for hybrid AO OCM. FC: 50-50 Fiber Coupler, PC: Polarization Controller, CL: Collimating Lens, M: Mirror, DM: Deformable Mirror, RP: Right-angle Prism, GMx and GMy: galvanometer mirror along x and y directions, OBJ: Objective Lens, S: Sample. All other unlabeled lenses are telescope pairs used for pupil conjugation.
Fig. 2
Fig. 2 Simulation results showing the depth-dependent incident beam intensity and reconstructed OCT signals for a Gaussian versus astigmatic beam. (a) Depth-dependent illumination profile obtained with the following beam types. Beam1: Gaussian beam illumination. Beam 2: Astigmatic beam with EPI. Beam 3: Astigmatic beam with EPII. (b) Reconstruction of depth-dependent collection profile with CAO. Signal A: Depth-dependent OCM signal from Beam 1. Signal B: CAO reconstruction of Signal A. Signal C: Depth-dependent OCM signal from Beam 3. Signal D: CAO reconstruction of Signal C. The dynamic range occupied by Gaussian and astigmatic beam is labelled on the right side.
Fig. 3
Fig. 3 Performance characterization of Gaussian versus astigmatic illumination beams in a resolution phantom, using ESI. (a-e) Comparison of cross-sectional energy distribution across depth, from maximum intensity projection of a 250μm slice. The bottom row is the depth-normalized version of the top row (method explained in Sect. 4.2), with (a) OCM with Gaussian beam, (b) CAO-OCM with Gaussian beam, (c) OCM with astigmatic beam, (d) CAO-OCM with astigmatic beam, and (e) focus scanning OCM fused from 18 volumes. Scale bars indicate 100 μm for all images. (f) Quantitative measurement of peak reconstructed OCT signal intensity. (g) FWHM resolution for hyAO and focus scanning OCM. Color-filled regions indicate ± one standard deviation. In (f) and (g), the depth axis matches the portion of the cross sectional images that are below the sample surface.
Fig. 4
Fig. 4 Comparison of cross sectional energy distribution across depth in grape imaging, from maximum intensity projection of a 10 μm slice, in EPI. (a) OCM with Gaussian beam, (b) CAO-OCM with Gaussian beam, (c) OCM with astigmatic beam, (d) CAO-OCM with astigmatic beam, (e) Focus scanning OCM fused from 18 volumes. The line plots indicate the normalized intensity profile in the image at four depths, labelled by arrows with corresponding color, with black dashed arrow representing the focal plane position for the standard Gaussian acquisition. All images are depth-normalized with the same method as in the phantom results in Fig. 3. Scale bars indicate 100 μm for all images. A gamma correction with γ = 0.7 was applied to all images.
Fig. 5
Fig. 5 Volumetric visualization of fibroblast cell dynamics. (a) the entire volume with 1 mm × 1mm × 1mm FOV, extracted from Supplementary Visualization 1. (b) cell exhibits upward motion in a 30 min duration, (c) cell exhibits rapid sideways motion in a 6 min duration. Both (b) and (c) cover a 0.25mm × 0.25mm × 0.25mm volume, with the initial time point indicated by the cyan channel and final time point by the red channel.
Fig. 6
Fig. 6 En face maximum intensity projection (400 μm slices) of fibroblast cell dynamics. (a) the entire 1mm × 1mm FOV, extracted from Supplementary Visualization 2. (b) cell extending filopodia across a 1 hour duration, (c) cell undergoing migration over a 263 minute period. Scale bar represents 50 μm for all groups.

Tables (1)

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Table 1 Summary of different illumination approaches used for comparing Gaussian and astigmatic beams in the simulation and experiments.

Equations (7)

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I ill (z;k)== 2 P inc π  w 2 (z) ,
g(x,y,z;k)= G( k x , k y ,z=0;k ) e iϕ( k x , k y ) e i k z z e i( k x x+ k y y) d k x d k y .
h(x,y,z;k)= P inc g ill ( x,y,z;k ) g col ( x,y,z;k )= P inc g 2 (x,y,z;k)
H( k x , k y ,z;k )= h(x,y,z;k) e i( k x x+ k y y) dxdy
h ac (x,y,z;k)= H( k x , k y ,z;k ) e i ϕ ac ( k x , k y ,z ) e i( k x x+ k y y) d k x d k y
I rec ( z;k ) max x,y { | h( x,y,z;k ) | 2 },
I rec,ac ( z;k ) max x,y { | h ac ( x,y,z;k ) | 2 }.
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