## Phase optimisation for structured illumination microscopy |

Optics Express, Vol. 21, Issue 2, pp. 2032-2049 (2013)

http://dx.doi.org/10.1364/OE.21.002032

Acrobat PDF (3245 KB)

### Abstract

Structured illumination microscopy can achieve super-resolution in fluorescence imaging. The sample is illuminated with periodic light patterns, and a series of images are acquired for different pattern positions, also called phases. From these a super-resolution image can be computed. However, for an artefact-free reconstruction it is important that the pattern phases be known with very high precision. If the necessary precision cannot be guaranteed experimentally, the phase information has to be retrieved *a posteriori* from the acquired data. We present a fast and robust algorithm that iteratively determines these phases with a precision of typically below *λ*/100. Our method, which is based on cross-correlations, allows optimisation of pattern phase even when the pattern itself is too fine for detection, in which case most other methods inevitably fail. We analyse the performance of this method using simulated data from a synthetic 2D sample as well as experimental single-slice data from a 3D sample and compare it with another previously published approach.

© 2013 OSA

## 1. Introduction

1. E. Abbe, “Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung,” Arch. Mikrosk. Anat. **9**, 413–468 (1873). [CrossRef]

2. T. A. Klar and S. W. Hell, “Subdiffraction resolution in far-field fluorescence microscopy,” Opt. Lett. **24**, 954–956 (1999). [CrossRef]

6. M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods **3**, 793–796 (2006). [CrossRef] [PubMed]

7. R. Heintzmann and C. Cremer, “Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating,” Proc. SPIE **3568**, 185–196 (1999). [CrossRef]

10. R. Heintzmann, T. M. Jovin, and C. Cremer, “Saturated patterned excitation microscopy - a concept for optical resolution improvement,” JOSA A **19**, 1599–1609” (2002). [CrossRef] [PubMed]

11. M. G. L. Gustafsson, “Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution,” PNAS **102**, 13081–13086 (2005). [CrossRef] [PubMed]

12. L. Hirvonen, O. Mandula, K. Wicker, and R. Heintzmann, “Structured illumination microscopy using photo-switchable fluorescent proteins,” Proc. SPIE **6861**, 68610L (2008). [CrossRef]

13. E. H. Rego, L. Shao, J. Macklin, L. Winoto, G. A. Johansson, N. Kamps-Hughes, M. W. Davidson, and M. G. L. Gustafsson, “Nonlinear structured-illumination microscopy with a photoswitchable protein reveals cellular structures at 50-nm resolution” PNAS **109**, E135–E143 (2012). [CrossRef]

*effective*illumination, which can lie outside the illumination support. This enables, in principle, unlimited resolution.

*a posteriori*from the acquired data. Shroff et al. have presented a method for obtaining the pattern position in each individual raw image by analysing the phase of the peaks corresponding to the pattern frequency in the Fourier representation of the image [14

14. S. A. Shroff, J. R. Fienup, and D. R. Williams, “Phase-shift estimation in sinusoidally illuminated images for lateral superresolution,” JOSA A **26**, 413–424 (2009). [CrossRef] [PubMed]

15. R. Fiolka, M. Beck, and A. Stemmer, “Structured illumination in total internal reflection fluorescence microscopy using a spatial light modulator,” Opt. Lett. **33**, 1629–1631 (2008). [CrossRef] [PubMed]

16. P. Kner, B. B. Chhun, E. R. Griffis, L. Winoto, and M. G. L. Gustafsson, “Super-resolution video microscopy of live cells by structured illumination,” Nat. Methods **6**, 339–342 (2009). [CrossRef] [PubMed]

17. E. Mudry, K. Belkebir, J. Girard, J. Savatier, E. Le Moal, C. Nicoletti, M. Allain, and A. Sentenac, “Structured illumination microscopy using unknown speckle patterns,” Nature Photon. **6**, 312–315 (2012). [CrossRef]

## 2. Image formation and reconstruction in SIM

*D*(

*r⃗*) can be described as a convolution (⊗) of the emitted fluorescence

*E*(

*r⃗*) with the microscope point spread function (PSF),

*h*(

*r⃗*):

*D*(

*r⃗*) = [

*E*⊗

*h*](

*r⃗*). Omitting constant scaling factors such as quantum efficiency, the emitted light distribution can be written as a product of the sample fluorophore density

*S*(

*r⃗*) and an

*effective*illumination

*I*(

*r⃗*):

*E*(

*r⃗*)=

*S*(

*r⃗*)

*I*(

*r⃗*). In the case of a linear sample response to the illumination light, this effective illumination is identical to the real illumination. If the sample response is nonlinear, the process of illumination and emission can be written as the sample responding linearly to a modified effective illumination, which accounts for the nonlinearity [10

10. R. Heintzmann, T. M. Jovin, and C. Cremer, “Saturated patterned excitation microscopy - a concept for optical resolution improvement,” JOSA A **19**, 1599–1609” (2002). [CrossRef] [PubMed]

18. M. G. L. Gustafsson, L. Shao, P. M. Carlton, C. J. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. **94**, 4957–4970 (2008). [CrossRef] [PubMed]

*M*of components, which can be separated into axial (

*z*) and lateral (

*x*,

*y*) parts:

*I*(

*r⃗*,

_{xy}*z*) = ∑

*(*

_{m}I_{m}*z*)

*J*(

_{m}*r⃗*). Each lateral component should be a harmonic wave, containing only a single spatial frequency

_{xy}*p⃗*, i.e.

_{m}*J*(

_{m}*r⃗*)= exp{

_{xy}*ι*(2

*πp⃗*·

_{m}*r⃗*+

_{xy}*ϕ*)}. Here

_{m}*ϕ*denotes the phase of the

_{m}*m*

^{th}component. As the illumination intensity has to be real-valued, each illumination component

*J*(

_{m}*r⃗*) must have a complex conjugate partner

*m*=−(

*M*−1)/2 to (

*M*−1)/2 and defining

*p⃗*

_{−}

*=−*

_{m}*p⃗*and

_{m}*ϕ*

_{−m}=−

*ϕ*. If the spatial frequencies

_{m}*p⃗*describe a harmonic pattern, they will be multiples of a single fundamental frequency

_{m}*p⃗*:

*p⃗*=

_{m}*mp⃗*. Also, if this pattern is symmetrical and remains rigid under translation between the images, the phases of the individual frequencies will be multiples of the phase of the fundamental frequency,

*ϕ*:

*ϕ*=

_{m}*mϕ*[18

18. M. G. L. Gustafsson, L. Shao, P. M. Carlton, C. J. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. **94**, 4957–4970 (2008). [CrossRef] [PubMed]

*h*(

_{m}*r⃗*)=

*h*(

*r⃗*)

*I*(

_{m}*z*). The image formation can then be described by a sample that is illuminated by lateral structured illumination components

*J*(

_{m}*r⃗*) only, while each of these is imaged with their own respective PSF

*h*(

_{m}*r⃗*). We can therefore write the acquired SIM data as or in Fourier space with Fourier components

*C*̃

*(*

_{m}*k⃗*). For structured illumination a series of typically

*N*≥

*M*images is acquired for different pattern phases

*ϕ*. We can write the

_{n}*n*

^{th}Fourier image as or in matrix notation where

**M**contains the various pattern phases

*mϕ*for different illumination orders (

_{n}*m*) and images (

*n*).

*C*̃

*(*

_{m}*r⃗*) need to be extracted from the acquired SIM data. This can be done by inverting Eq. (3): If the mixing matrix

**M**is not square, i.e. if there are more images than components to be separated, the Moore-Penrose pseudo inverse [19

19. R. Penrose, “A generalized inverse for matrices,” Proc. Cambridge Philos. Soc. **51**, 406–413 (1955). [CrossRef]

**M**

^{−1}:=(

**M**

^{†}

**M**)

^{−1}

**M**

^{†}can be used. Here the dagger symbol

^{†}denotes the conjugate transpose or Hermitian conjugate of a matrix. For square mixing matrices (

*M*=

*N*) equidistant phase steps (i.e.

*ϕ*= 2

_{n}*πn*/

*N*) will yield the best conditioning number and will therefore yield components with the highest possible signal-to-noise levels. Nevertheless, separation will work for other phase steps, as long as

**M**is non-singular.

*Q*of pattern orientations, yielding separated components

*C̃*(

_{q,m}*k⃗*), where the additional index

*q*denotes the orientation. These components can then be recombined using weighted averaging in Fourier space combined with a generalised Wiener filter [18

18. M. G. L. Gustafsson, L. Shao, P. M. Carlton, C. J. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. **94**, 4957–4970 (2008). [CrossRef] [PubMed]

20. L. P. Yaroslavsky and H. J. Caulfield, “Deconvolution of multiple images of the same object,” Appl. Opt. **33**, 2157–2162 (1994). [CrossRef] [PubMed]

*Ã*(

*k⃗*), yielding a sample estimation

*S*̃′(

*k⃗*) that contains higher frequency information than the wide-field image

*S*̃(

*k⃗*)

*h*̃(

*k⃗*). In the above equation

*w*is the constant Wiener parameter, which is adjusted empirically. The purpose of the apodisation function is to avoid hard edges in the effective reconstructed OTF, which may otherwise result from Wiener-filtering. This way ringing artefacts in the reconstructed PSF are avoided. The apodisation function used to reconstruct the images in this article is a distance transform (

*s*) applied to the footprint (

*f*) of the OTF (i.e. the distance from the edge of the support), normalised to one in the centre, taken to the power of 0.4:

*Ã*(

*k⃗*)=[

*s*(

*f*(

*h*̃(

*k⃗*)))]

^{0.4}. This function was empirically found to yield good results.

## 3. Current phase determination techniques

*ϕ*. If these phases are not known with sufficient precision, the unmixing process will not be able to perfectly separate the components; instead, they will contain residual information from other components. After shifting, these residual components will be at incorrect locations in Fourier space, leading to artefacts in the reconstructed image. Precise knowledge of the pattern phases is therefore paramount. As it is not always possible to exert enough control over these phases in the experimental setup, it is desirable to be a able to determine the pattern phases

_{n}*a posteriori*from the acquired data.

### 3.1. Determining the pattern phase from the phase of the Fourier peaks

14. S. A. Shroff, J. R. Fienup, and D. R. Williams, “Phase-shift estimation in sinusoidally illuminated images for lateral superresolution,” JOSA A **26**, 413–424 (2009). [CrossRef] [PubMed]

14. S. A. Shroff, J. R. Fienup, and D. R. Williams, “Phase-shift estimation in sinusoidally illuminated images for lateral superresolution,” JOSA A **26**, 413–424 (2009). [CrossRef] [PubMed]

*C*̃

_{−1}(

*k⃗*),

*C̃*

_{0}(

*k⃗*) and

*C*̃

_{+1}(

*k⃗*), superimposed with different phase:

*c*is the contrast of the illumination pattern. Shroff et al. retrieve the pattern phase of the

*n*

^{th}image from the phase of the Fourier image at the frequency of the pattern peak

*p⃗*: where the function arg gives the angle of a complex number. This approximation yields good results if three assumptions are fulfilled.

*c*, has to be sufficiently large. Secondly, the sample power spectrum must decrease sufficiently fast with growing frequency, so that |

*S*̃(0)|

^{2}≫

*S*̃(

*p*⃗)||

^{2}+|

*S*̃(2

*p⃗*)|

^{2}. This is a good assumption for most natural samples as long as the pattern frequency

*p*⃗ is sufficiently large. When these two conditions are fulfilled, Eq. (7) will be dominated by the last term, yielding For any real valued sample, the centre frequency

*S*̃(0) will be real valued, as will be the OTF,

*h*̃(

*k⃗*), if the PSF

*h*(

*r⃗*) is real and symmetrical. This allows the phase retrieval in the above manner. For asymmetrical PSFs the phases of the OTFs have to be accounted for.

*h*̃(

*p⃗*), has to be sufficiently large. If this is not the case, noise in the acquired image may alter the phase measured at

*p⃗*significantly. Naturally, for pattern frequencies

*p⃗*outside the support of the detection OTF, i.e.

*h*̃ (

*p⃗*)=0, the method cannot work.

## 4. Phase determination using cross-correlations of separated components

*h*̃(

*p*⃗) be sufficiently large will not always be fulfilled. For very high pattern frequencies, which are desirable for significant resolution enhancement, the OTF

*h*̃(

*p⃗*) quickly drops to low values, so that shot noise in the Fourier images may dominate the phase value at

*p⃗*. But worse, it is possible that the pattern peaks lie outside the OTF support altogether. This is the case when using SIM in TIRF configuration, where the numerical aperture (NA) of the illumination is larger than the NA of the detection. But even for SIM systems in a conventional (epi-)fluorescence configuration, the Stokes shift may lead to patterns that are no longer visible in the detection.

### 4.1. Correlating separated components

*C*̃

*,*

_{i}*C*̃

*can be measured using a weighted cross-correlation (WCC, denoted by ⍟*

_{j}*, defined in Appendix B) at frequency zero,*

_{w}*should*therefore yield a low value for

*i*≠

*j*. Auto-correlations of components (i.e.

*i*=

*j*) on the other hand will yield high values, as all separated components will obviously have strong information overlap with themselves.

*lp⃗*, however, there will be information overlap between some of the components. The correlation values

*should*be high for

*i*=

*j*+

*l*(as we can see from Eq. (2)), and low for

*i*≠

*j*+

*l*.

8. M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. **198**, 82–87 (2000). [CrossRef] [PubMed]

### 4.2. Iterative optimisation of pattern phases

*i*≠

*j*+

*l*. This fact can be used for the optimisation of the unmixing matrix. We achieve this by iteratively varying the pattern phases

*ϕ*in the mixing matrix (

_{n}**M**

*=*

_{n,m}*e*

^{ιmϕn}), inverting this matrix and applying it to separate the components (

*l*=1 to

*L*,

*Lp⃗*being the highest shift for which there is still any overlap between the OTFs of the shifted components. The mixing matrix is then optimised iteratively by using a gradient search algorithm (“minfunc” by Mark Schmidt [21

21. M. Schmidt, “minfunc.m,” http://www.di.ens.fr/~mschmidt/Software/minFunc.html (2012).

*i*=

*j*+

*l*), or a combination of this approach with the above function for the optimum usage of all the information available, we found that this did not improve the perceived reliability of our optimisation.

### 4.3. Speeding up the algorithm significantly

**M**, the separation of the components and the re-calculation of the correlation tensor

*g*(

**M**) for each iteration. This means that (

*M*

^{2}−

*M*+

*L*/2) (

*L*+1)correlations have to be calculated in each iteration. This corresponds to the total number of elements in the correlation tensor less the number of element expected to have a high correlation (i.e.

*j*+

*l*−

*i*=0, which are not used in the cost function). This number can be decreased if symmetries in the correlation tensor are exploited. Nevertheless, the iterative optimisation will be computationally very expensive and time-consuming, especially for large images and even more so in the case of nonlinear SIM, which requires a larger number of components.

*𝒞*after each iteration, we calculate a different tensor,

*𝒟*, containing the correlation values of the unshifted and

*lp⃗*-shifted Fourier

*images*rather than of the Fourier

*components*: From this Fourier image correlation tensor

*𝒟*, the component correlation tensor

*𝒞*can be calculated via As

*𝒟*is independent of the mixing matrix

**M**it has to be calculated only once before the start of the iterative optimisation. This means that

*N*

^{2}

*L*correlations (or fewer, when exploiting symmetries in the tensor) have to be calculated

*once*. Furthermore, for each iteration the unmixing matrix

**M**

^{−1}will now operate on

*L*correlation matrices of size

*N*×

*N*, rather than on

*M*Fourier images containing thousands or millions of pixels. Both aspects lead to a dramatic improvement in optimisation speed.

### 4.4. Accounting for auto-correlations of the noise

*different*(or shifted) Fourier images, and a non-random part stemming from auto-correlations of noise from

*one*image with itself. This non-random part has an expectation value that is real and positive, and should be removed from the correlation values

### 4.5. Reconstructing two-dimensional data

*k*onto the

_{z}*k*,

_{x}*k*-plane (which corresponds to the pure 2D in-focus OTF). When applied to 2D samples (e.g. very thin samples or TIRF) as the synthetic sample used in our simulated images, this again yields the correct weights. When reconstructing 2D data from 3D samples (as for our experimental data), these incorrect 2D weights will treat all information as overlapping, as long as it has overlap in the

_{y}*k*,

_{x}*k*-coordinates, ignoring

_{y}*k*. This is however still better than not using any weights at all, which would then put an undue emphasis on noise-only correlations.

_{z}## 5. Simulations

### Methods

*λ*= 488 nm; emission wavelength

_{ex}*λ*= 515 nm; numerical aperture

_{em}*NA*=1.4; refractive index of the embedding medium

*n*=1.52. These parameters correspond to a maximum detectable spatial frequency of (184 nm)

_{r}^{−1}, defined by the detection OTF. The illumination patterns were simulated for two-beam (i.e. sinusoidal) illumination. The pixel size in the simulated raw image corresponds to 65 nm in sample space. The synthetic sample used for the simulations is shown in Fig. 1(a), its Fourier transform in Fig. 1(b). No camera offset or other background was assumed in the simulation.

^{−1}, which corresponds to 87.6% of the maximum frequency supported by the OTF; and illumination with a very fine pattern with a spatial frequency of (185 nm)

^{−1}, corresponding to 99.4% of the OTF support.

*ϕ*

_{1,p}, 120° +

*ϕ*

_{2,p}, 240° +

*ϕ*

_{3}

*},*

_{,p}*p*=1..20. For each of these, we simulated noisy images for 51 different signal-to-noise levels, using Poisson noise and an expectation values of 10

^{l/10},

*l*=0..50, i.e. between 1 and 10

^{5}photons, in the brightest pixel.

**D̃**

*={*

_{q,p,l}*D*̃

_{1,q,p,l},

*D*̃

_{2,q,p,l},

*D*̃

_{3,q,p,l}} (i.e. three raw SIM images for one pattern orientation (index

*q*), with one set of randomised phases (index

*p*), for one particular photon level (index

*l*)) we optimised the pattern phases using our iterative, correlation-based algorithm and – for comparison – also using the method proposed by Shroff et al. [14

**26**, 413–424 (2009). [CrossRef] [PubMed]

*ϕ*

_{1,q,p,l}, Δ

*ϕ*

_{2,q,p,l}and Δ

*ϕ*

_{3,q,p,l}. We then calculated the standard deviation of these three individual remaining phase errors,

*denotes the mean of an expression over the index*

_{n}*n*. We call this

*ε*the phase error of the dataset

_{q,p,l}**D̃**

*. This approach disregards any global pattern phase offsets, as component separation only requires the knowledge of phase steps between images (i.e. a constant offset present in all the remaining phase errors is not an error at all, as it does not affect component separation; this global phase can be fitted in the overlap region of separated components [8*

_{q,p,l}8. M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. **198**, 82–87 (2000). [CrossRef] [PubMed]

*ε*is really our measure of how well the algorithm managed to find the correct phases for the dataset

_{q,p,l}**D̃**

*. For each of the 51 photon levels (*

_{q,p,l}*l*) we then calculated the mean of the datasets’ phase errors over all 20 different phase variations (

*p*) and three orientations (

*q*),

*E*= 〈

_{l}*ε*〉

_{q,p,l}*. We call this the*

_{q,p}*average phase error*for a particular photon level (

*l*). We furthermore calculated the standard deviation from this average phase error,

### Results

*E*of our iterative,

_{l}*correlation-based*algorithm, the shaded blue areas indicate its standard deviation Δ

*E*. Likewise the red lines and shaded areas show the corresponding values obtained for Shroff et al.’s

_{l}*phase-of-peak*method.

^{4.6}≈ 40000 photons) our algorithm failed and returned a phase error of 31°, leading to a degradation of average phase error and standard deviation. For images of lesser SNR (less than 100 photons expected in the brightest pixel), our iterative method yields more accurate results with higher confidence.

^{4}photons) a slight decrease in average phase error occurs.

### Influence of phase optimisation

^{5}photons in the brightest pixel. For a more pronounced effect we randomised the pattern phase with a standard deviation of 20° (10 nm), yielding phase errors {−7.4°, 7.1°, 0.3°} for orientation 1, {23.6°, −4.3°, −19.3°} for orientation 2 and {38.0°, −31.6°, −6.4°} for orientation 3. The phase errors found by our algorithm were {−10.0°, 9.6°, 0.4°} for orientation 1, {23.4°, −4.3°, −19.1°} for orientation 2 and {37.4°, −30.9°, −6.6°} for orientation 3.

*w*in Eq. (5) was chosen to be 2.4·10

^{−4}of the maximum value of ∑

_{q′,m′}{|

*h̃*

_{m′}(

*k⃗*+

*m*′

*p⃗*

_{q}_{′})|

^{2}}, i.e. of the denominator without

*w*in Eq. (5). Figure 1(a) shows the synthetic sample used for the simulation (with a close-up of the blue box to the left), Fig. 1(b) its Fourier transform (for the Fourier transformed images we linearly display the square root of their magnitude). Figures 3(a) and 3(b) show the Wiener-filter deconvolved wide-field image and its Fourier transform. Figures 3(c) and 3(d) show the result of SIM reconstruction without correcting the phases, using instead the nominal phases. Even when using the wrong phases the reconstructed image clearly exhibits strong resolution enhancement, and the extent of spatial frequencies in the Fourier image is much improved over the wide-field case. However, there is unwanted residual zero-order information in the shifted components, which is clearly visible as rays emanating from the edge of the support rather then from the centre. They have been highlighted in the bottom half of the Fourier image, but can also be seen (as symmetrical copies) in the upper half. They stem from a bad separation of components due to the use of incorrect phases and cause artefacts in the reconstructed image. This is most obvious in the close-up, where the reconstructed line patterns exhibit a jagged appearance. This is remedied in the reconstructed SIM image using iterative, correlation-based optimisation, which is shown in Fig. 4(c). Also, the residual rays in the Fourier transformed image (Fig. 4(d)) are no longer visible, indicating a successful separation of the components. Due to the high spatial frequency of the pattern Shroff et al.’s phase-of-peak method failed to find the pattern phases, leading to a failure of component separation (Fig. 4(b)) and image reconstruction (Fig. 4(a)).

## 6. Performance on experimental data

### Method

*λ*= 488 nm; emission wavelength

_{ex}*λ*> 500 nm; numerical aperture

_{em}*NA*=1.4, oil immersion. The SIM pattern was generated using two-beam interference from a purposely misaligned Twyman-Green-interferometer. The pattern frequency and orientation could be varied by tilting and rotating the beam splitter, resulting in frequencies |

*p⃗*|={(310.6 nm)

^{−1}, (311.4 nm)

^{−1}, (301.5 nm)

^{−1}} for the three orientations

*α*= {0.9°, 120.8°, 59.7°}. Phase stepping was achieved by moving the mirrors in the interferometer arms using piezo actuators. A more detailed description of the setup can be found in [22

22. G. Best, R. Amberger, D. Baddeley, T. Ach, S. Dithmar, R. Heintzmann, and C. Cremer, “Structured illumination microscopy of autofluorescent aggregations in human tissue,” Micron **42**, 330–335 (2011). [CrossRef]

*D*,

_{q}*D*

_{q}_{+1}and

*D*

_{q}_{+3}, 1 ≤

*q*≤ 17, for individual reconstruction.

*ϕ*

_{2}≈ 75° (when defining the relative phase of the first image as

*ϕ*

_{1}:= 0°) and that of the third image about

*ϕ*

_{3}≈ 3

*ϕ*

_{2}≈ 225°, we started our iterative optimisation with initial phases of

*ϕ*

_{2}= 40° and

*ϕ*

_{3}= 240° in order to show the capability of the algorithm to retrieve completely unknown phases.

### Results

*ϕ*

_{2}and

*ϕ*

_{3}. Their respective means

*μ*

_{2}and

*μ*

_{3}and ratio of the means

*μ*

_{3}/

*μ*

_{2}were:

*μ*

_{2}= 76.2°,

*μ*

_{3}= 229.2°,

*μ*

_{3}/

*μ*

_{2}= 3.01 for orientation 1;

*μ*

_{2}= 74.9°,

*μ*

_{3}= 225.2°,

*μ*

_{3}/

*μ*

_{2}= 3.01 for orientation 2; and

*μ*

_{2}= 73.2°,

*μ*

_{3}= 218.8°,

*μ*

_{3}/

*μ*

_{2}= 2.99 for orientation 3.

*μ*

_{2}and

*μ*

_{3}from the phases determined by the algorithm. As we wanted to disregard any constant phase offset in these remaining errors, we calculated each dataset’s phase error as the standard deviation of its remaining phase errors. Their mean, i.e. the average phase error, and its standard deviation were: 1.2°±0.9° for orientation 1; 2.9°±1.6° for orientation 2; and 1.7°±1.1° for orientation 3. Taking into account the varying pattern frequencies of the different orientations, this corresponds to an average phase error of 1.0 nm ±0.8 nm for orientation 1; 2.5 nm ±1.4 nm for orientation 2; and 1.4 nm±0.9 nm for orientation 3.

*μ*

_{2}= 76.9°,

*μ*

_{3}= 228.3°,

*μ*

_{3}/

*μ*

_{2}= 2.97 for orientation 1;

*μ*

_{2}= 75.2°,

*μ*

_{3}= 225.8°,

*μ*

_{3}/

*μ*

_{2}= 3.00 for orientation 2; and

*μ*

_{2}=72.8°,

*μ*

_{3}=218.5°,

*μ*

_{3}/

*μ*

_{2}=3.00 for orientation 3. The average phase error, and its standard deviation were: 7.4°±1.5° for orientation 1; 1.5°±0.9° for orientation 2; and 1.2°±0.6° for orientation 3. Taking into account the varying pattern frequencies of the different orientations, this corresponds to an average phase error of 6.4 nm±1.3 nm for orientation 1; 1.3 nm±0.8 nm for orientation 2; and 1.0 nm±0.5 nm for orientation 3.

### Influence of phase optimisation

^{th}component along with a close-up of the sample region inside the blue box. Figure 5(b) shows the corresponding Fourier image. SIM reconstruction without phase optimisation (i.e. assuming the phases {0°, 40°, 240°}, which were used as starting phases for the iterative reconstruction) yields an image with improved resolution, sectioning and contrast (Fig. 5(c)). However, the image appears to have been superimposed with a grainy, honeycomb-like structure. This is due to bad separation of components, leading to residual information being shifted to wrong locations in Fourier space (Fig. 5(d)). These are visible as bright peaks at the position of one and two times the pattern frequency, marked by blue circles in Fig. 5(d). When compared to the values

*μ*

_{2}and

*μ*

_{3}, correlation-based phase optimisation led to phase errors of 0.7° for orientation 1, 3.2° for orientation 2 and 1.5° for orientation 3. This improves the component separation, leading to a Fourier image devoid of these unwanted peaks (Fig. 6(d)). The resulting high resolution SIM reconstruction (Fig. 6(c)) does not exhibit the grainy structure present in the uncorrected image.

## 7. Discussion

8. M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. **198**, 82–87 (2000). [CrossRef] [PubMed]

11. M. G. L. Gustafsson, “Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution,” PNAS **102**, 13081–13086 (2005). [CrossRef] [PubMed]

**94**, 4957–4970 (2008). [CrossRef] [PubMed]

## A. Reconstructing two-dimensional data of three-dimensional samples

*k*onto the

_{z}*k*,

_{x}*k*-plane, the straightforward approach to SIM reconstruction would be to apply Eq. (5) using a 2D OTF, which is the sum projection of the true 3D OTF:

_{y}*h*̃′(

*k⃗*)= ∫

_{xy}*h*̃(

*k⃗*)

*dk*. However, this approach is not ideal: as can be seen in Fig. 7(b), the resulting 2D OTF is strongest around the zero frequency, i.e. where the corresponding 3D OTF suffers from the missing cone, as indicated by arrows in Fig. 7(a). In these regions Eq. (5) would therefore lead to an undue emphasis of components that do not carry any missing cone information, while suppressing components that could fill this gap. As a result, the reconstructed images will exhibit almost no optical sectioning, although the acquired data does contain the necessary sectioning information.

_{z}*g*̃(

*k⃗*) = 1 −

_{xy}*a*exp{−|

*k⃗*|

_{xy}^{2}/(2

*d*

^{2})}, which is shown as a black dotted line in Fig. 7(c). Recombining the components using these modified weights and then applying the generalised Wiener filter, Eq. (5) becomes

*g*̃ does not act quadratically in the denominator of the final Wiener-filtered image. The strength

*a*of the attenuation and its width

*d*are chosen empirically for each experiment. For the reconstruction of SIM images of RPE cells in this article, they were

*a*= 0.999 and

*w*was empirically optimised and set to 0.3 of the maximum value of ∑

_{q′,m′}{|

*h*̃′

_{m′}(

*k⃗*+

_{xy}*m*′

*p⃗*

_{q′})|

^{2}

*g*̃(

*k⃗*+

_{xy}*mp⃗*)}, i.e. of the denominator without

_{q}*w*in Eq. (14).

## B. Weighted cross-correlations

*k⃗*, the correlation of information strongly depends on the magnitude of the product of the components’ respective OTFs, |

*h̃*(

_{i}*k⃗*)

*h̃*(

_{j}*k⃗*−

*lp⃗*)|. E.g. the contribution will be zero, where one of the OTFs is zero and no information is transmitted. Any contribution from this frequency

*k⃗*would therefore only be from noise. As this would degrade the overall correlation value, it is beneficial to emphasise contributions from frequencies with high information content and disregard that from frequencies contributing only with noise.

*w*(

*r⃗*). The weights are chosen such that the ratio of information correlation over noise correlation is maximised. If we assume white noise of standard deviation

*σ*in the different components, Gaussian error propagation lets us calculate this quality factor as:

*lp⃗*only (i.e. for fixed overlaps), this distortion does not affect us, and for optimisation purposes the WCC will yield more reliable results than the conventional unweighted one.

## Acknowledgments

*Carl Zeiss Microscopy GmbH*.

## References and links

1. | E. Abbe, “Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung,” Arch. Mikrosk. Anat. |

2. | T. A. Klar and S. W. Hell, “Subdiffraction resolution in far-field fluorescence microscopy,” Opt. Lett. |

3. | M. Hofmann, C. Eggeling, S. Jakobs, and S. W. Hell, “Breaking the diffraction barrier in fluorescence microscopy at low light intensities by using reversibly photoswitchable proteins,” PNAS |

4. | S. T. Hess, T. P. K. Girirajan, and M. D. Mason, “Ultra-high resolution imaging by fluorescence photoactivation localization microscopy,” Biophys. J. |

5. | E. Betzig, G. H. Patterson, R. Sougrat, W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science |

6. | M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods |

7. | R. Heintzmann and C. Cremer, “Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating,” Proc. SPIE |

8. | M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. |

9. | J. T. Frohn, “Super-resolution fluorescence microscopy by structured light illumination,” Ph.D. thesis, Eidgenössische Technische Hochschule Zürich, Switzerland (2000). |

10. | R. Heintzmann, T. M. Jovin, and C. Cremer, “Saturated patterned excitation microscopy - a concept for optical resolution improvement,” JOSA A |

11. | M. G. L. Gustafsson, “Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution,” PNAS |

12. | L. Hirvonen, O. Mandula, K. Wicker, and R. Heintzmann, “Structured illumination microscopy using photo-switchable fluorescent proteins,” Proc. SPIE |

13. | E. H. Rego, L. Shao, J. Macklin, L. Winoto, G. A. Johansson, N. Kamps-Hughes, M. W. Davidson, and M. G. L. Gustafsson, “Nonlinear structured-illumination microscopy with a photoswitchable protein reveals cellular structures at 50-nm resolution” PNAS |

14. | S. A. Shroff, J. R. Fienup, and D. R. Williams, “Phase-shift estimation in sinusoidally illuminated images for lateral superresolution,” JOSA A |

15. | R. Fiolka, M. Beck, and A. Stemmer, “Structured illumination in total internal reflection fluorescence microscopy using a spatial light modulator,” Opt. Lett. |

16. | P. Kner, B. B. Chhun, E. R. Griffis, L. Winoto, and M. G. L. Gustafsson, “Super-resolution video microscopy of live cells by structured illumination,” Nat. Methods |

17. | E. Mudry, K. Belkebir, J. Girard, J. Savatier, E. Le Moal, C. Nicoletti, M. Allain, and A. Sentenac, “Structured illumination microscopy using unknown speckle patterns,” Nature Photon. |

18. | M. G. L. Gustafsson, L. Shao, P. M. Carlton, C. J. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. |

19. | R. Penrose, “A generalized inverse for matrices,” Proc. Cambridge Philos. Soc. |

20. | L. P. Yaroslavsky and H. J. Caulfield, “Deconvolution of multiple images of the same object,” Appl. Opt. |

21. | M. Schmidt, “minfunc.m,” http://www.di.ens.fr/~mschmidt/Software/minFunc.html (2012). |

22. | G. Best, R. Amberger, D. Baddeley, T. Ach, S. Dithmar, R. Heintzmann, and C. Cremer, “Structured illumination microscopy of autofluorescent aggregations in human tissue,” Micron |

23. | K. Wicker, “Increasing resolution and light efficiency in fluorescence microscopy,” Ph.D. thesis, King’s College London, U.K. (2010). |

**OCIS Codes**

(100.2000) Image processing : Digital image processing

(100.6640) Image processing : Superresolution

(180.2520) Microscopy : Fluorescence microscopy

**ToC Category:**

Microscopy

**History**

Original Manuscript: September 28, 2012

Revised Manuscript: January 6, 2013

Manuscript Accepted: January 7, 2013

Published: January 18, 2013

**Virtual Issues**

Vol. 8, Iss. 2 *Virtual Journal for Biomedical Optics*

**Citation**

Kai Wicker, Ondrej Mandula, Gerrit Best, Reto Fiolka, and Rainer Heintzmann, "Phase optimisation for structured illumination microscopy," Opt. Express **21**, 2032-2049 (2013)

http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-21-2-2032

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### References

- E. Abbe, “Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung,” Arch. Mikrosk. Anat.9, 413–468 (1873). [CrossRef]
- T. A. Klar and S. W. Hell, “Subdiffraction resolution in far-field fluorescence microscopy,” Opt. Lett.24, 954–956 (1999). [CrossRef]
- M. Hofmann, C. Eggeling, S. Jakobs, and S. W. Hell, “Breaking the diffraction barrier in fluorescence microscopy at low light intensities by using reversibly photoswitchable proteins,” PNAS102, 17565–17569 (2005). [CrossRef] [PubMed]
- S. T. Hess, T. P. K. Girirajan, and M. D. Mason, “Ultra-high resolution imaging by fluorescence photoactivation localization microscopy,” Biophys. J.91, 4258–4272 (2006). [CrossRef] [PubMed]
- E. Betzig, G. H. Patterson, R. Sougrat, W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science313, 1642–1645 (2006). [CrossRef] [PubMed]
- M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods3, 793–796 (2006). [CrossRef] [PubMed]
- R. Heintzmann and C. Cremer, “Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating,” Proc. SPIE3568, 185–196 (1999). [CrossRef]
- M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc.198, 82–87 (2000). [CrossRef] [PubMed]
- J. T. Frohn, “Super-resolution fluorescence microscopy by structured light illumination,” Ph.D. thesis, Eidgenössische Technische Hochschule Zürich, Switzerland (2000).
- R. Heintzmann, T. M. Jovin, and C. Cremer, “Saturated patterned excitation microscopy - a concept for optical resolution improvement,” JOSA A19, 1599–1609” (2002). [CrossRef] [PubMed]
- M. G. L. Gustafsson, “Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution,” PNAS102, 13081–13086 (2005). [CrossRef] [PubMed]
- L. Hirvonen, O. Mandula, K. Wicker, and R. Heintzmann, “Structured illumination microscopy using photo-switchable fluorescent proteins,” Proc. SPIE6861, 68610L (2008). [CrossRef]
- E. H. Rego, L. Shao, J. Macklin, L. Winoto, G. A. Johansson, N. Kamps-Hughes, M. W. Davidson, and M. G. L. Gustafsson, “Nonlinear structured-illumination microscopy with a photoswitchable protein reveals cellular structures at 50-nm resolution” PNAS109, E135–E143 (2012). [CrossRef]
- S. A. Shroff, J. R. Fienup, and D. R. Williams, “Phase-shift estimation in sinusoidally illuminated images for lateral superresolution,” JOSA A26, 413–424 (2009). [CrossRef] [PubMed]
- R. Fiolka, M. Beck, and A. Stemmer, “Structured illumination in total internal reflection fluorescence microscopy using a spatial light modulator,” Opt. Lett.33, 1629–1631 (2008). [CrossRef] [PubMed]
- P. Kner, B. B. Chhun, E. R. Griffis, L. Winoto, and M. G. L. Gustafsson, “Super-resolution video microscopy of live cells by structured illumination,” Nat. Methods6, 339–342 (2009). [CrossRef] [PubMed]
- E. Mudry, K. Belkebir, J. Girard, J. Savatier, E. Le Moal, C. Nicoletti, M. Allain, and A. Sentenac, “Structured illumination microscopy using unknown speckle patterns,” Nature Photon.6, 312–315 (2012). [CrossRef]
- M. G. L. Gustafsson, L. Shao, P. M. Carlton, C. J. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J.94, 4957–4970 (2008). [CrossRef] [PubMed]
- R. Penrose, “A generalized inverse for matrices,” Proc. Cambridge Philos. Soc.51, 406–413 (1955). [CrossRef]
- L. P. Yaroslavsky and H. J. Caulfield, “Deconvolution of multiple images of the same object,” Appl. Opt.33, 2157–2162 (1994). [CrossRef] [PubMed]
- M. Schmidt, “minfunc.m,” http://www.di.ens.fr/~mschmidt/Software/minFunc.html (2012).
- G. Best, R. Amberger, D. Baddeley, T. Ach, S. Dithmar, R. Heintzmann, and C. Cremer, “Structured illumination microscopy of autofluorescent aggregations in human tissue,” Micron42, 330–335 (2011). [CrossRef]
- K. Wicker, “Increasing resolution and light efficiency in fluorescence microscopy,” Ph.D. thesis, King’s College London, U.K. (2010).

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