Light sheet microscopy allows rapid imaging of three-dimensional fluorescent samples, using illumination and detection axes that are orthogonal. For imaging large samples, this often forces the objective to be tilted relative to the sample’s surface; for samples that are not precisely matched to the immersion medium index, this tilt introduces aberrations. Here we calculate the nature of these aberrations for a simple tissue model, and show that a low-dimensional parametrization of these aberrations facilitates online correction via a deformable mirror without introduction of beads or other fiducial markers. We use this approach to demonstrate improved image quality in living tissue.
© 2013 Optical Society of America
When imaging biological tissues by optical microscopy, image quality is degraded by both scattering and classical aberrations. The latter arise from changes in refractive index, either locally within the sample or at the interface between the sample and the immersion fluid. When examining living samples, the immersion fluid is typically saline, with a refractive index of approximately 1.34; most tissues have an average (bulk) refractive index in the range 1.36–1.40 . Consequently, the saline/tissue interface is a significant source of aberration. Using traditional forms of microscopy, this interface is approximately perpendicular to the imaging axis; under these conditions, spherical aberration plays the dominant role [2, 3].
Recently, light sheet illumination microscopy has emerged as an attractive technique for rapid three-dimensional imaging . In light sheet microscopy, the “axial” direction for imaging is orthogonal to the direction of propagation of the illumination. To image large samples, such as the mammalian nervous system [5, 6], it is therefore necessary to tilt the illumination and detection axes relative to the sample surface (Fig. 1). This tilt introduces new aberrations at the saline/tissue interface that are not significant when imaging tissue face-on. While both the illumination and detection paths are affected by these aberrations, their consequence is more serious for the detection path because of its higher numerical aperture. The detection aberrations have been corrected using wavefront sensors with beads as fiducial markers [7, 8] and/or via an image-based optimization of Zernike modes 4–15 . However, both approaches have limitations: the introduction of beads into samples can be problematic in practice, and optimization of image quality in a 12-dimensional space requires extensive search and collection of many calibration images. In particular, gradient-free optimization of even a simple quadratic function of N variables typically takes O(N2) iterations ; during correction with 12 parameters, many trial images have to be collected and, in the presence of noise, it may be difficult to assess progress in searching such a high-dimensional space. Under normal imaging conditions, these considerations may serve as an obstacle to achieving large improvements in image quality [7, Fig. 6].
Here we use a perturbative approach to calculate the low-order aberrations that arise from imaging at a tilt through a refractive index mismatch. We show that the aberration structure can be described using just two or three parameters. This low-dimensional subspace can be readily searched to correct aberrations via a deformable mirror. We demonstrate improved image quality when imaging a neural tissue, the mouse vomeronasal organ.
2. A homogeneous planar tissue model
Consider a sample which has an index of refraction ns different from the immersion index ni so that ns = ni + ε. Here we will focus in particular on the case where the surface is planar (Fig. 1), appropriate for flat or large samples where the local curvature is small compared to the field of view. In this case, the points at the tissue surface satisfy x · n̂ = c, where n̂ is the unit normal and c is a constant.
Starting from an object point x0, a ray propagating in a direction ê intersects the surface after traveling a distance s(ê), where for a flat surface we have
In the appendix, we show that to lowest order in ε the aberrations can be calculated in terms of the excess path length experienced by “unperturbed” rays. For the ray described in Eq. (1), the excess path length is simply εs(ê).
3. Defocus in meridional planes
To illustrate our approach, we begin with a relatively simple calculation of the lowest-order aberration, defocus, for a thin pencil of rays centered around the optic axis ẑ. The defocus can be calculated by modeling each ray’s excess path length as if the pencil originates from a shifted source point x0 + Δx. We therefore seek a Δx satisfying
First consider the meridional plane spanned by ẑ and n̂ (Fig. 1). In this plane, we can write ê = cosθẑ + sinθŷ, where ŷ is the coordinate perpendicular to ẑ in this plane and is parallel to the direction of propagation of the illumination. If the angle between the tissue normal and the optic axis is α, then ẑ · n̂ = cosα and ŷ · n̂ = sinα. Thus
Substituting this parametrization into Eq. (3), we obtain10] for small ε. It is possible to correct this defocus with a small tilt of the light sheet .
One can also treat the other meridional plane, for which we let ê = cosθẑ + sinθx̂, where x̂ · n̂ = 0. ThenEq. (3) one obtains Eq. (7) and Eq. (10), we see that the shift Δz is different in the two meridional planes, so the defocus is anisotropic. The mean defocus along ẑ is
This result also indicates a significant opportunity for improvement using adaptive optics. The maximum extent of the defocus anisotropy is 2εdz tan2α. For ε = 0.03, α = 45°, and dz = 200μm, the difference between the two Δz results is 12μm, significantly larger than the typical z-thickness of the light sheet.
4. General aberrations from a homogeneous tissue model
To compute aberrations over an extended field of view, we convert the displacement x0 of the source point and the direction ê of each ray into coordinates in the back pupil plane . Let the objective’s focal length be f0, and for a three-dimensional vector w consider just the two-dimensional projection w⊥ in the plane perpendicular to the optic axis. In such coordinates, for a lens satisfying the sine condition  we may write x0⊥ and e⊥ in terms of new variables u and v, where x0⊥ = − f0u and e⊥ = av − u, where a = |R/f0| is the radius R of the back aperture scaled by f0. The back-aperture position coordinate v assumes values over the entire unit circle.
In these coordinates, the wavefront aberration of Eq. (1) is writtenFig. 1 where the index mismatch is known, this expression has no free parameters and hence provides a mechanism to correct aberrations without any need for wavefront sensing or optimization. In practice, often one will not know ε for the specific tissue under investigation, and likewise the sample may be slightly tilted so that even α may not be known exactly. As a consequence, it is worth considering the two dimensional family of solutions
Eq. (14) can be used directly to specify the shape of a deformable mirror to correct the aberrations. (Because A is depth-dependent, this correction can only be done for a “stripe” of constant depth within the sample.) For example, for the center of the field of view (u = 0), the mirror should be tuned to assume a shapeFig. 2, for the case (meaning that the sheet is already in focus).
In many cases, the deformable mirror may have been calibrated using a Zernike basis. As a consequence, it is useful to calculate the projections of Φ(v, 0)/A onto such a basis, so that the mirror shape parametrized by Φ can be represented in terms of Zernike coefficients. For an object in the center of the field of view, up to fourth order these are:Fig. 3.
The number of parameters in this representation of the aberration is so low that no particular specialized procedures are required to perform aberration correction. Indeed, setting these parameters can easily be performed in the same way that microscopes are usually focused: visual tuning by the user. Naturally, this does not preclude more algorithmically-based approaches, but a key advantage of the analytic representation of Eq. (15) is that it greatly reduces the demands on all other components of the adaptive optics system.
5. Experimental results
An Objective-Coupled Planar Illumination (OCPI) microscope with adaptive-optics correction was built and calibrated as described . The mirror shape was parametrized in the Zernike basis described in section 4, using A′, t, and β as tunable coefficients. We found that in practice the user could tune these coefficients to a reasonable optimum with fewer than 20 images collected during tuning. These settings sufficed for imaging the same region of tissue over tens of minutes.
Figure 4 shows the improvement in image quality from this procedure, using two samples. In Fig. 4(a), a 200 nm bead is visualized in polydimethylsiloxane (PDMS, Dow Corning, DC 184-A and DC 184-B with a weight ratio of 10:1, n = 1.40). Figure 4(b) shows the corrected image on the same intensity scale; the peak intensity is increased by slightly over two-fold, and the bead image is considerably more compact. Figure 4(c) shows an image of GCaMP2-expressing neurons in the vomeronasal organ of a mouse ; the corrected version is seen in Fig. 4(d). It is apparent that many details of the image are considerably improved. AO correction resulted in overall 18% increase in the r.m.s. pixel intensity, and a 20% increase in the peak intensity.
Here we demonstrate a simple procedure for improving image quality in light sheet microscopy. For extended samples with a flat interface, the lowest-order aberrations may be calculated directly using a simple tissue model. This model greatly reduces the complexity of adaptive optics, by providing a functional form of the aberrations containing only two or three free parameters. These parameters are readily tuned at the beginning of image collection, and the corrected images show considerably improved quality. We believe that this represents a promising and pragmatic approach for many applications of light sheet microscopy.
A. Optical path length: a perturbative approach
Optical propagation through a sample can be expressed in terms of the optical path length (also known as the point characteristic ),
Consider an aberration that arises as a small perturbation ε(x) of the index of refraction, i.e., n(x) ⇒ n(x) +ε(x) so thatEq. (32) and Sε is of the same form, with n(x(s)) replaced by ε(x(s)). We want to expand ζtot to lowest order in ε.
Consider the curve C★ that satisfies infCSn [C]. We’ll expand S around C★ as C = C★ +δC. We haveEq. (35). In contrast, a is zeroth-order in ε because it includes the S″n [C★] term in Eq. (35). (If S″n [C★] = 0, which can happen for certain δC if x0 and x1 are conjugate points in an imaging system, then a too is first order in ε, but we will be careful not to use these expressions in such a case.) δC thus satisfies δC = −b/2a, and thus Eq. (37) only in second order in ε. Therefore, to first order the optical path length under a small perturbation is just
This work was funded by NIH NINDS/NIAAA ( R01 NS068409, TEH) and a McKnight Technological Innovation Award in Neuroscience ( TEH).
References and links
3. J. Porter, H. M. Queener, J. E. Lin, K. Thorn, and A. Awwal, Adaptive Optics for Vision Science (Wiley, 2006) [CrossRef] .
5. T. F. Holekamp, D. Turaga, and T. E. Holy, “Fast three-dimensional fluorescence imaging of activity in neural populations by objective-coupled planar illumination microscopy,” Neuron 57, 661–672 (2008) [CrossRef] [PubMed] .
8. R. Jorand, G. Le Corre, J. Andilla, A. Maandhui, C. Frongia, V. Lobjois, B. Ducommun, and C. Lorenzo, “Deep and clear optical imaging of thick inhomogeneous samples,” PloS One 7, e35795 (2012) [CrossRef] [PubMed] .
9. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C++: The Art of Scientific Computing, 3rd ed. (Cambridge University PressCambridge, 2007).
11. M. Born and E. Wolf, Principles of Optics (Pergamon Press, 1980), 6th ed.
12. J. L. F. de Meijere and C. H. F. Velzel, “Linear ray-propagation models in geometrical optics,” J. Opt. Soc. Am. A 4, 2162–2165 (1987) [CrossRef] .