CN-116106274-B - Robust anti-motion structured light illumination super-resolution microscopic imaging method based on principal component analysis

Abstract

The invention provides a steady Motion-resistant structured light illumination super-resolution microscopic imaging method (Motion-resistantstructuredilluminationmicroscopybasedonprincipal componentanalysis, mrPCA-SIM) based on principal component analysis, which can effectively compensate non-uniform pixel offset and phase error in an original illumination image. Experimental examples prove that compared with the traditional method, the method provided by the invention realizes more robust imaging quality under complex and unstable conditions, and is expected to realize more compatible and flexible live cell super-resolution imaging.

Inventors

• LIU YONGDAO
• HUANG YUXIA
• ZUO CHAO
• CHEN QIAN
• QIAN JIAMING
• WU HONGJUN
• Xu Kailong

Assignees

• 南京理工大学

Dates

Publication Date

20260505

Application Date

20221209

Claims (6)

1. 1. A robust anti-motion structured light illumination super-resolution microscopic imaging method based on principal component analysis is characterized by comprising the following specific processes: step 1, collecting three original structure light illumination images in different directions; step2, compensating pixel offset caused by motion in the original structure light illumination image; and 3, compensating phase errors caused by motion in an original structured light illumination image, wherein the specific method comprises the following steps of: Step 3.1, determining a primary spectrum integral pixel peak value of a spectrum image of the original structure light illumination image after pixel compensation, taking the primary spectrum integral pixel peak value as a center, extracting spectrum main energy by using a double window mask, carrying out inverse Fourier transform, and taking a phase to obtain an e index term of an illumination image phasor factor: where exp is an exponential function based on a natural constant e, angle is the phase of the complex number, For inverse fourier transform, C 1 is the primary spectral principal energy extracted using a double window mask; step 3.2, singular value decomposition is carried out on the e index term of the phasor factor obtained in the step 3.1, and the specific steps after decomposition are as follows: Wherein U 2 and V 2 are respectively left and right singular matrices of phasor factors, The superscript T is the transpose of the matrix for the eigenvalue matrix; Step 3.3, extracting the principal component of the phasor factor, and obtaining the phase error caused by movement by taking the phase of the principal component of the phasor factor at r=0 ; And 4, performing accurate spectrum separation on the compensated structured light illumination image, wherein the specific method comprises the following steps of: Wherein k ex and m are respectively the modulation frequency and modulation degree of the structured light, k is a frequency coordinate, O is an optical transfer function, S is sample information, superscript @ is Fourier transform of the original object, subscripts 0 and + -1 are spectrum orders of the sample, In order for the phase error to be caused by motion, For a set constant phase shift, A center spectrum of the sample +1 level spectrum after compensating for pixel offset and phase error; And 5, merging the separated frequency spectrums, and reconstructing a real-time super-resolution image by utilizing wiener deconvolution.
2. 2. The robust motion-resistant structured light illumination super-resolution microscopic imaging method based on principal component analysis according to claim 1, wherein the specific method for acquiring the original structured light illumination image in step 1 is as follows: And respectively illuminating the sample from three different directions by using a structured light illumination microscopy system, and collecting three-step phase-shift sinusoidal illumination images in all directions.
3. 3. The robust motion-resistant structured light illumination super-resolution microscopic imaging method based on principal component analysis according to claim 2, wherein the original structured light illumination image D n of a certain direction, n=1, 2,3, is represented as an acquired three-step phase-shifted sinusoidal illumination image of the original structure of the certain direction, in particular: Wherein r is the image space coordinate, S is the sample information, r n and For motion-induced pixel offset and phase errors, For a set constant phase shift, P is the point spread function, For convolution operation, k ex and m are the modulation frequency and modulation degree of the structured light, respectively.
4. 4. A robust motion-resistant structured light illumination super resolution microscopy imaging method based on principal component analysis according to claim 3, wherein the specific method of step 2 to compensate for motion-induced pixel shift in the original structured light illumination image is as follows: step 2.1, carrying out Fourier transform on the original structural light illumination image acquired in the step 1, wherein the obtained spectrum image is: wherein k is a frequency coordinate, an upper label is Fourier transform of an original object, subscripts 0 and +/-1 are spectrum levels of a sample, and O is an optical transfer function; Step 2.2, calculating normalized cross-correlation power spectrum with pixel offset image and first illumination direction image in original structured light illumination image to obtain approximation of pixel offset: In the formula, For the obtained normalized cross-correlation power spectrum, the superscript ∗ is the complex conjugate of the matrix, and Han is the hanning filter; And 2.3, extracting main energy of the normalized cross-correlation power spectrum by using a double window mask, wherein the main energy is specifically as follows: For normalized cross-correlation power spectrum Performing inverse fourier transform and locating an integer pixel spectrum peak of the inverse fourier transform spectrum; and taking the normalized cross-correlation power spectrum peak value as a center, extracting spectrum main energy by using a mask operator and carrying out Fourier transform: where NaN is the null point, 、 The left boundary and the lower boundary of the signal window in the directions of the vertical axis x and the vertical axis y in the double-window mask are respectively, 、 The right boundary and the upper boundary of the signal window in the double window mask in the directions of the longitudinal axis x and the vertical axis y are respectively, R is the size of the blank window in the double window mask in the directions of the longitudinal axis x and the vertical axis y, 、 Spatial coordinates along the longitudinal axis x and vertical axis y, respectively, where the signal window size 、 、 、 And the value of the blank window size R, in Obtaining, wherein N is the size of the complete frequency spectrum, and S is the ratio of the amplitude of the signal window to the complete amplitude; Step 2.4, performing singular value decomposition on the main energy of the normalized cross-correlation power spectrum obtained in the step 2.3, and extracting the offset r n of motion interference, wherein the decomposed normalized cross-correlation power spectrum specifically comprises: Wherein U 1 and V 1 are respectively the left singular matrix and the right singular matrix of the normalized cross-correlation power spectrum, The superscript T is the transposition of the matrix for the characteristic value matrix; And 2.5, extracting main components of the normalized cross-correlation power spectrum, wherein the main components specifically comprise: In the formula, A matrix in which the elements of the first column representing the first row are 1 and the other elements are all 0; 2.6, performing phase expansion on the first row element of the left singular matrix U 1 , and performing linear fitting on the first row element by using a least square method to obtain a slope, wherein the slope is a component of a sub-pixel precision offset r n caused by motion in the horizontal x direction; Step 2.7, compensating the center spectrum of the sample +1 level spectrum by the offset r n caused by the extracted motion: 。
5. 5. the robust anti-motion structured light illumination super resolution microscopy imaging method based on principal component analysis of claim 4, wherein the hanning filter has a radius of Peak distance of the level 0 and level 1 spectra.
6. 6. The robust anti-motion structured light illumination super-resolution microscopic imaging method based on principal component analysis according to claim 1, wherein the specific method of reconstructing the super-resolution image in step 5 is to combine the separated spectra, and reconstruct the real-time super-resolution image by wiener deconvolution: where the subscript i is the spectrum level, i=0, +1, -1, and the subscript d is the d-th illumination component, Is the wave vector of the d-th illumination direction, I-level spectral components in the d-th illumination direction, which are no optical transfer function and illumination parameter components, w is a wiener constant.

Description

Robust anti-motion structured light illumination super-resolution microscopic imaging method based on principal component analysis Technical Field The invention belongs to the technical field of super-resolution fluorescence microscopic imaging, and particularly relates to a steady anti-motion structured light illumination super-resolution microscopic imaging method based on principal component analysis, which is used for more compatibly and flexibly carrying out real-time super-resolution observation on living cells under complex and unstable conditions. Background Research on dynamic changes in subcellular structures on the nanoscale is of great importance in exploring the nature of life and major diseases. Over the last several decades, super-resolution techniques have bypassed the limits of abbe diffraction, achieved visualization of biomolecules at the nanoscale to single molecule level, and have evolved into powerful tools for life sciences research [ p.s.weiss, "Nobel prizes for super-resolution imaging," (2014) ]. Thanks to the advantages of rapid wide area imaging, low photodamage, non-specific requirements for fluorescent molecules, and the like, the structure illumination microscope (structured illumination microscopy, SIM) stands out from a plurality of super-resolution technologies, and is particularly suitable for real-time long-term dynamic observation of living cells [J.Qian,Y.Cao,K.Xu,Y.Bi,W.Xia,Q.Chen,and C.Zuo,"Robust frame-reduced structured illumination microscopy with accelerated correlation-enabled parameter estimation,"Appl.Phys.Lett.121,153701(2022).]. The SIM usually modulates the high frequency information that is originally higher than the cut-off frequency of the system through space structured light illumination, so as to expand the transverse resolution [G.Wen,S.Li,L.Wang,X.Chen,Z.Sun,Y.Liang,X.Jin,Y.Xing,Y.Jiu,Y.Tang et al.,"High-fidelity structured illumination microscopy by point-spread-function engineering,"Light.Sci.&Appl.10,1–12(2021).]., however, in the SIM image reconstruction, slight estimation errors all lead to serious reconstruction artifacts, so in order to realize high-quality super-resolution image reconstruction, it is required to accurately estimate the illumination parameters [X.Huang,J.Fan,L.Li,H.Liu,R.Wu,Y.Wu,L.Wei,H.Mao,A.Lal,P.Xi et al.,"Fast,long-term,super-resolution imaging with hessian structured illumination microscopy,"Nat.biotechnology 36,451–459(2018).]. because the illumination parameters are extremely susceptible to environmental disturbance, sample offset and other motion factors, if the imaging conditions are not strictly stable, the illumination parameters must be re-extracted for each reconstruction to realize the traditional parameter estimation method for dynamically observing the living cells [K.Wicker,"Non-iterative determination of pattern phase in structured illumination microscopy using auto-correlations in fourier space,"Opt.express 21,24692–24701(2013).]., such as the most widely used iterative cross Correlation (COR), and the initial spectral separation is usually performed with known constant phase shift in each illumination direction, and the residual parameters [M.G.Gustafsson,L.Shao,P.M.Carlton,C.R.Wang,I.N.Golubovskaya,W.Z.Cande,D.A.Agard,and J.W.Sedat,"Three dimensional resolution doubling in wide-field fluorescence microscopyby structured illumination,"Biophys.journal 94,4957–4970(2008).]. are compensated by depending on the overlapped sample signals of different spectral components. Disclosure of Invention The invention aims to provide a steady anti-motion structured light illumination super-resolution microscopic imaging method based on principal component analysis, which provides a steady, efficient, real-time, flexible, convenient and low-light damage super-resolution observation means for researching nano-scale subcellular structural features, motion states, interactions and protein functions in living cells under complex and unstable conditions. The technical scheme for realizing the purpose of the invention is that a steady anti-motion structured light illumination super-resolution microscopic imaging method based on principal component analysis comprises the following specific processes: step 1, collecting three original structure light illumination images in different directions; step2, compensating pixel offset caused by motion in the original structure light illumination image; step 3, compensating phase errors caused by movement in the original structured light illumination image; step 4, performing accurate spectrum separation on the compensated structured light illumination image; And 5, merging the separated frequency spectrums, and reconstructing a real-time super-resolution image by utilizing wiener deconvolution. Preferably, the specific method for acquiring the original structured light illumination image in step 1 is as follows: And respectively illuminating the sample from three different directions by u