System and method for image reconstruction using structure illumination microscope

Abstract

In the described embodiment, a method for image reconstruction is implemented, comprising acquiring a set of original images corresponding to a plurality of phases of an illumination pattern. The image reconstruction method further includes separating the original spectrum of each original image into a baseband spectrum and at least two modulation shift spectrums using an inverse matrix, and demodulating the set of original images to generate a demodulated image. A high pass filter is applied to the baseband spectrum and a low pass filter is applied to the spectrum of the demodulated image in order to generate a corrected baseband spectrum. The corrected baseband spectrum is combined with the at least two modulation shifted spectrums to generate a synthesized spectrum. An inverse Fourier transform is performed on the synthetic spectrum to produce a reconstructed image.

Description

Technical Field

[0001] The present application generally relates to methods and systems for optical microscopy, and more particularly to methods and systems for super-resolution imaging and image reconstruction using structured illumination microscopy. Background Art

[0002] As a fundamental tool in biological research, fluorescence microscopy is commonly used to observe cellular and tissue morphology and processes. Its widespread application is primarily due to its molecular specificity, high sensitivity, and ability to resolve subcellular details. Within the field of fluorescence microscopy, two main categories have emerged: widefield fluorescence microscopy and laser scanning microscopy.

[0003] Widefield fluorescence microscopy is known for its simple implementation and rapid imaging capabilities. It employs a constant, uniform illumination pattern and instantly captures images using a two-dimensional image sensor. However, the application of widefield fluorescence microscopy is generally limited to thin samples, such as cultured cells or thinly sectioned tissues. This limitation arises from the inability to distinguish in-focus and out-of-focus fluorescence photons in thicker samples. This results in out-of-focus background that obscures in-focus information, indicating a lack of optical sectioning capability in standard widefield fluorescence microscopy, making it unsuitable for complex three-dimensional biological samples.

[0004] In contrast, laser scanning microscopy, including point-scanning and line-scan confocal microscopy (LSCM), has become a more suitable choice for thick tissue imaging. These microscopes use a convergent illumination pattern that scans across the field of view, utilizing spatial filters (such as pinholes or slits) to reject unwanted background photons. Point-scanning confocal microscopy, in particular, provides effective suppression of out-of-focus light, thereby enabling proper optical sectioning. However, point-to-point scanning is inherently time-consuming, typically limits imaging speed to a few hertz, and is more susceptible to photobleaching and photodamage to the sample. In contrast, LSCM, with its line illumination approach, provides increased imaging speed, but at the expense of reduced optical sectioning capability and contrast, primarily due to significant background photon leakage through the confocal slit.

[0005] Recent advances in widefield fluorescence microscopy have introduced techniques such as structured illumination microscopy (SIM) and selective plane illumination microscopy (SPIM), which are effective for optical sectioning in thin and transparent samples. These methods maintain the high-speed imaging and low photobleaching properties of traditional widefield microscopy, with SIM gaining recognition for its ease of implementation and minimal sample preparation requirements. SIM utilizes a high-frequency structured light pattern projected onto the sample. This pattern decays rapidly with increasing defocus, allowing only the structure in the focal plane to be significantly modulated. This property facilitates the separation of target signals from unstructured background emissions by computational image processing. The development of optical sectioning SIM (OS-SIM) has further improved this process by reducing the number of raw images required for effective reconstruction.

[0006] However, current SIM techniques face significant challenges when used on complex or thick biological tissues. In these situations, a large number of background photons reach the image sensor, severely hindering the accuracy of image reconstruction. These background photons overload the sensor, leading to high noise levels and amplified artifacts in the image due to inaccuracies in the image reconstruction mathematical model. Consequently, image quality deteriorates rapidly with increasing imaging depth.

[0007] It would therefore be desirable to provide methods and systems that combine spatial filtering with structured illumination to achieve super-resolution imaging and image reconstruction that address the shortcomings or limitations of the prior art, or at least provide the public with a useful alternative. Summary of the Invention

[0008] The present invention is directed to providing new and useful methods and systems for optical microscopy, and in particular to providing methods and systems for super-resolution imaging and image reconstruction using structured illumination and spatial filtering.

[0009] Broadly speaking, the present invention provides a microscope system comprising: an illumination enabling light source configured to emit a light beam; an optical unit configured to separate the light beam into a plurality of light beams in a focal plane so as to generate a plurality of illumination patterns; and a focusing lens configured to focus the plurality of light beams along a first direction to form a focal line on a sample. The microscope system may include a scanning module comprising one or more scanning mirrors for scanning the focal line across the sample along a second direction and an emission collection mirror for rescanning corresponding fluorescent emissions from the sample. One way to implement the system is to include a confocal slit for filtering out-of-focus light associated with the fluorescent emissions from the sample so as to provide filtered fluorescent emissions. It will be understood from the described embodiments that the microscope system may include a detection unit for acquiring a plurality of images of the sample corresponding to the filtered fluorescent emissions.

[0010] In certain embodiments, the microscope system may include an image processor for reconstructing a high-resolution image characterizing the sample based on a combination of a set of captured raw images corresponding to the three phase illumination patterns.

[0011] In an implementation, reconstructing a high-resolution image characterizing a sample may include separating frequency components of an original image, adjusting a rescanning ratio along a scanning direction to obtain one or more reconstructed original images, and restoring the rescanning ratio of the reconstructed original image along the scanning direction.

[0012] In some embodiments, reconstruction of a high-resolution image characterizing a sample can be achieved without rotating the multiple illumination patterns.

[0013] In an implementation, the sample may be a biological tissue specimen.

[0014] In some embodiments, the scanning module may be implemented in the form of two galvanometer mirrors that operate synchronously to scan the sample and rescan the fluorescence emission on the image sensor.

[0015] In an implementation, the optical unit may include a retarder for adjusting a polarization component of at least one separated light beam in order to generate a plurality of illumination patterns with adjusted modulation depths.

[0016] The optical unit may include a wave plate for adjusting polarization distribution of the light beam so that the light beam is suitable for being separated into a plurality of light beams.

[0017] In an implementation, the optical unit may include an electro-optical modulator for phase-shifting a separated light beam relative to another separated light beam.

[0018] In an embodiment, the focusing lens may be a cylindrical lens, and the first direction may be orthogonal to the second direction.

[0019] In an implementation, multiple illumination patterns may be generated based on detected interference between multiple separated light beams.

[0020] In certain embodiments, the confocal slit may be fixed in angular position or orientation.

[0021] The optical unit may include a Wollaston prism for separating the light beam into a plurality of light beams, wherein a separation angle of the Wollaston prism is determined.

[0022] The microscope system may include determining a modulation frequency corresponding to the Wollaston prism based on image resolution and signal-to-noise ratio.

[0023] In an implementation, the confocal slit may be optically conjugated to the focal line to filter out-of-focus light.

[0024] In an embodiment, the detection unit may include a scientific complementary metal oxide semiconductor (CMOS) camera synchronized with one or more scanning mirrors and emission collection mirrors to achieve one-dimensional image rescanning.

[0025] The scanning module can be configured to increase the angular velocity of one or more scanning mirrors relative to the emission collection mirror to achieve a predetermined rescan ratio that is optimal for resolution enhancement.

[0026] Broadly speaking, the present invention provides a light microscopy method that may include emitting a light beam, splitting the light beam into a plurality of light beams in a focal plane to generate a plurality of illumination patterns, and focusing the plurality of light beams along a first direction to form a focal line on a sample. The light microscopy method may include scanning the focal line across the sample along a second direction and rescanning corresponding fluorescence emission from the sample, filtering out-of-focus light associated with the fluorescence emission from the sample to provide filtered fluorescence emission, and acquiring a plurality of images of the sample corresponding to the filtered fluorescence emission.

[0027] The present invention also proposes a method for image reconstruction, comprising: acquiring a set of original images corresponding to multiple phases of an illumination pattern, and applying a Fourier transform (FT) to each original image in the acquired set to obtain a corresponding original spectrum of each original image. One way of employing the image reconstruction method includes using an inverse matrix to separate the original spectrum of each original image into a baseband spectrum and at least two modulation shift spectra, and demodulating the set of original images to generate a demodulated image. It can be understood from the described embodiment that the image reconstruction method may include: applying multiple high-pass filters to the baseband spectrum and applying a low-pass filter to the spectrum of the demodulated image to generate a corrected baseband spectrum, combining the corrected baseband spectrum with at least two modulation shift spectra to generate a composite spectrum, and performing an inverse Fourier transform on the composite spectrum to produce a reconstructed image.

[0028] The image reconstruction method may include resizing the reconstructed image to correct aspect ratio distortion, thereby generating a reconstructed image having a reduced horizontal size.

[0029] Demodulating the set of raw images may include suppressing out-of-focus light and systematic deviations from the raw images, wherein the systematic deviations include camera dark current.

[0030] In an implementation, the phases of the illumination pattern may include 0 degrees, 120 degrees, and 240 degrees.

[0031] In an embodiment, the corrected baseband spectrum may be generated by adding a high-pass filtered baseband spectrum to a low-pass filtered spectrum of the demodulated image, wherein the high-pass filter and the low-pass filter use the same predetermined cutoff frequency.

[0032] The demodulated image may correspond to a standard resolution image, and the generated synthetic spectrum includes a frequency range extending in the vertical direction.

[0033] In an implementation, the resizing step may be performed based on a rescanning ratio of two, thereby achieving super-resolution along at least the horizontal direction of the reconstructed image.

[0034] Thus, embodiments described herein may provide methods and systems for structured illumination microscopy and image rescanning, resulting in one or more of the following advantages:

[0035] Background photons in deep tissue imaging are reduced by integrating a confocal slit, which mitigates shot noise and laser intensity noise.

[0036] Image acquisition is enhanced by eliminating the need to rotate the modulated illumination pattern at multiple angular orientations.

[0037] Super-resolution in both lateral directions is achieved by adapting the image rescanning technique to a line-scan configuration and integrating both with structured illumination microscopy.

[0038] Maintain high image quality and resolution at higher imaging depths by effectively addressing background fluorescence emission issues commonly encountered in deep tissue imaging.

[0039] The spectral accuracy is enhanced and the background effect is reduced based on the assumption that the background is usually dominated by low-frequency components and is independent of the modulation phase.

[0040] The above description is provided as an overview of some implementations of the present invention. Further descriptions of these implementations and other implementations are described in more detail below. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Embodiments of the present invention will now be explained for purposes of example only with reference to the following drawings, in which:

[0042] Figure 1 is a schematic representation of a confocal rescanning structured illumination microscopy (CR-SIM) system according to embodiments herein.

[0043] Figure 2A is an optical diagram illustrating image formation of a point object using normal scanning (η=1) and image rescanning (η=2) configurations according to an embodiment of this document.

[0044] Figure 2B is a graphical representation comparing point spread function (PSF) widths for normal scan (η=1) and image rescan (η=2) configurations according to embodiments herein.

[0045] Figure 3A is a graphical representation of simulated PSFs for various slit sizes according to embodiments herein.

[0046] Figure 3B is a graphical representation of simulated optical transfer functions (OTFs) for various slit sizes according to embodiments herein.

[0047] Figure 4 is a schematic representation of the optical path and computational workflow used in CR-SIM image reconstruction according to embodiments herein.

[0048] Figure 5 This is an example of an embodiment according to this invention Figure 4 Flowchart of the high-level processing steps of the image reconstruction process.

[0049] Figure 6A is an image capture of 100 nm fluorescent beads using a laser scanning confocal microscope (LSCM) system according to an embodiment herein.

[0050] Figure 6B is an image capture of 100 nm fluorescent beads using a CR-SIM system according to an embodiment herein.

[0051] Figure 6C is the Richardson-Lucy deconvolution after 10 iterations according to the embodiment of this article Figure 6B Processed images of 100 nm fluorescent beads are shown.

[0052] Figure 6D is a comparison according to the embodiments of this article as shown in Figures 6A to 6C Graphical representation of the intensity distribution captured in , along the vertical and horizontal lines passing through the center of the fluorescent bead.

[0053] Figure 7is a comparative set of fluorescence microscopy images at varying depths of focus (0, 250, and 500 μm) within a thick tissue phantom using WF-SIM, LSCM, and CR-SIM according to embodiments herein.

[0054] Figure 8A is a graphical representation of signal-to-background ratio (SBR) as a function of depth for fluorescent beads imaged by WF-SIM, LSCM, and CR-SIM according to embodiments herein.

[0055] Figure 8B is a graphical representation of signal-to-noise ratio (SNR) as a function of depth for fluorescent beads imaged by WF-SIM, LSCM, and CR-SIM according to embodiments herein.

[0056] Figure 9 is a three-dimensional volume rendering of a thick bead phantom according to embodiments herein showing fluorescence intensity distribution as imaged by wide-field structured illumination microscopy (WF-SIM), LSCM, and CR-SIM.

[0057] Figure 10A is an image capture of fixed HeLa cells using LSCM to identify F-actin structures according to an embodiment herein.

[0058] Figure 10B According to the embodiment of this article Figure 10A Shown are images of the same identified F-actin structures captured within fixed HeLa cells but using CR-SIM imaging.

[0059] Figure 10C The comparative F-actin structure according to the examples herein is along Figure 10A and Figure 10B Graphical representation of the normalized intensity distribution of the identified regions in .

[0060] Figure 10D According to the embodiment of this article Figure 10A Magnified image of F-actin structures identified using LSCM.

[0061] Figure 10E According to the embodiment of this article Figure 10B Magnified image of the F-actin structure identified using CR-SIM.

[0062] Figure 11A is a volume rendering of a CR-SIM image stack of a Thy1-EGFP transgenic mouse brain slice according to an example herein.

[0063] Figure 11Bare comparative images showing side-by-side visualization of neuronal somata at Z=23 μm using both LSCM (left) and CR-SIM (right) systems, according to embodiments herein.

[0064] Figure 11C According to the embodiment of this article, Figure 11B A magnified image of a 2.4 μm × 2.4 μm area at the center of a neuronal soma captured by the CR-SIM system in Figure 5.

[0065] Figure 11D According to the embodiment of this article Figure 11C Graphical representation of the intensity distribution along the horizontal and vertical lines within the region shown.

[0066] Figure 11E is a three-dimensional rendering of a partial volume scan of a mouse brain tissue section obtained by CR-SIM according to an embodiment herein.

[0067] Figures 11F to 11H The CR-SIM system is used according to the embodiment of this article at 113 μm ( Figure 11F )、132μm( Figure 11G ) and 197μm( Figure 11H ) images of neuronal structures within brain slices of Thy1-EGFP transgenic mice at different depths.

[0068] Figures 11I to 11K The same Thy1-EGFP transgenic mouse brain slice is magnified 4 times according to the examples in this article. Figures 11F to 11H A magnified image of the area in captured. DETAILED DESCRIPTION

[0069] The embodiments will now be discussed with reference to the accompanying drawings, which depict one or more exemplary embodiments. These embodiments are described in sufficient detail to enable those skilled in the art to practice them, and it should be understood that mechanical, logical, and other changes may be made without departing from the scope of these embodiments. Thus, the embodiments may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein, shown in the drawings, and / or described below.

[0070] Unless otherwise defined, all terms (including technical and scientific terms) used herein should be interpreted as commonly used in the art. It should also be understood that commonly used terms should also be interpreted as commonly used in the relevant art.

[0071] Figure 1 is a schematic representation of a confocal rescanning structured illumination microscopy (CR-SIM) system 100 according to embodiments herein.

[0072] Laser excitation

[0073] In an implementation, the CR-SIM system 100 may include an illumination-enabled light source corresponding to a 473 nm laser 102 and a 561 nm laser 104 configured to emit a light beam for sample illumination.

[0074] CR-SIM system 100 may include optical elements such as Wollaston prism (WP) 108, electro-optic modulator (EOM) 106, liquid crystal retarder (LCR) 110, and half-wave plate (HWP) 144. These optical elements manipulate the characteristics of the light beam in preparation for the imaging process.

[0075] Bundle Preparation

[0076] The light from lasers 102 and 104 can be manipulated by various optical components, such as a dichroic beam splitter (DB) 146 to direct the combined laser beam toward the sample, and spherical lenses (L1-L8) 112, 114, 120, 124, 126, 130, 134 and 140 to collimate or focus the beam.

[0077] polarization:

[0078] The half-wave plate (HWP) 144 and the liquid crystal retarder (LCR) 110 can adjust the polarization of light, which is necessary for the proper operation of subsequent components of the system 100, such as the Wollaston prism (WP) 108.

[0079] Structured lighting:

[0080] A Wollaston prism (WP) may split the laser beam into two polarized beams, which may then be further converged by a cylindrical lens (CL) 118. The CL may serve as a focusing lens that focuses the separated beams in one direction to form a focal line on the sample.

[0081] These beams may be further directed to an objective lens (OBJ) 154 where they converge to produce an interference pattern - structured illumination - on the sample.

[0082] Phase Modulation:

[0083] The electro-optic modulator (EOM) 106 can dynamically shift the phase of the structured illumination, which is necessary for the SIM process.

[0084] scanning:

[0085] The scanning lens (SL) 148 can further focus the laser beam to form a line on the sample stage. The scanning lens (SL) can be configured to operate in conjunction with two separate scanning modules (GM1 and GM2) 128 and 138 to facilitate proper scanning of the beam across the sample. In an embodiment, the scanning modules 128 and 138 can correspond to galvanometer mirrors configured to scan a line of structured light across the sample. The first galvanometer mirror (GM1) 128 can adjust the position of the excitation line, and the second galvanometer mirror (GM2) 138 synchronized with GM1 128 can achieve rescanning of the sample at different speeds.

[0086] Fluorescence emission and reverse scanning:

[0087] The sample, when excited, emits fluorescent light, which is collected by the objective lens (OBJ) 154. The emitted light is back-scanned by the GM1 128, which means that it reverses the path of the scanning motion to stabilize the image.

[0088] Tube lens (TL) 150 can relay the back-scanned fluorescence light so that it is properly focused for imaging.

[0089] Emission path:

[0090] The fluorescent light may travel back through OBJ 154 and be reflected by dichroic mirror (DM) 122 toward the detection optics of sCMOS camera 142. Emission filter (EM) 136 may selectively allow only the fluorescent signal to pass, thereby filtering out the excitation wavelength.

[0091] Confocal detection:

[0092] The fluorescent light passes through the confocal slit 132 to further reject out-of-focus light. The confocal slit 132 provides filtered emission, which enhances image contrast and resolution, especially in thick tissue.

[0093] The filtered fluorescence is finally captured by a detection unit (eg, sCMOS camera 142), which converts the light into electrical signals that can be processed into an image.

[0094] Image acquisition and processing:

[0095] Captured images including structured illumination patterns at various phase shifts are computationally processed to reconstruct a high-resolution image of the sample with improved contrast, ideal for thick tissue samples.

[0096] Lenses and mirrors:

[0097] The focusing lenses used to form the focal lines may include spherical lenses (L1-L8) 112, 114, 120, 124, 126, 130, 134, and 140 with specified focal lengths, which are used to shape and focus the light paths for excitation and emission. Additional mirrors (M1 and M2) 116 and 152 may be used to appropriately direct light through the system 100.

[0098] In an implementation, an illumination line 156 is used as a specific form of excitation light as the excitation light interacts with the sample. In an example, an illumination region 158 may include the region of the sample illuminated by the illumination line 156 at a given moment. The illumination region 156 may include the region in which the illumination line 156 interacts with the sample to excite fluorescent markers within that specific region. A scanning direction 160 may correspond to the direction in which the illumination line 156 moves across the sample. The scanning mechanism is responsible for moving the line illumination 156 across the sample plane to systematically excite different regions, thereby enabling the entire region of the sample of interest to be imaged over time.

[0099] Multiple images can be captured at different phase shifts 162 (e.g., 0, 120, and 240 degrees). These phase shifts change the positions of the light and dark bars in the pattern. By capturing images at different phases and combining them, the microscope can reconstruct an image with a higher resolution than would be possible with a single phase.

[0100] In an example implementation of the CR-SIM system 100, the second galvanometer mirror (GM2) is adjusted to operate at an increased angular velocity defined by the rescan ratio η compared to the first galvanometer mirror (GM1). As described above, optimal resolution enhancement is achieved when η is set to 2. A detailed description of one-dimensional image rescanning using a slit and a comparative analysis of system behavior for rescan ratios of 1 and 2 is set forth below.

[0101] Principle of 1D image rescanning

[0102] In order to intuitively illustrate the one-dimensional image rescanning principle, a simplified optical system is used to describe the formation of the point spread function, as shown in Figure 2A and Figure 2B shown. Figure 2A is an optical diagram illustrating image formation of a point object using normal scanning (η=1) and image rescanning (η=2) configurations according to an embodiment of this document. Figure 2A The left side of φ shows the optical path of the imaging process in the case where the line focus is scanned to an offset s from the origin for each of the sample plane, the slit plane, and the detection plane. Figure 2A The corresponding illumination or detection intensity distribution at each plane is plotted on the right side of . Figure 2Bis a graphical representation comparing point spread function (PSF) widths for normal scan (η=1) and image rescan (η=2) configurations according to embodiments herein.

[0103] The imaging system can be modeled using two 4f systems, including the sample plane (x1), the intermediate slit plane (x2), and the detection plane (x3). For simplicity, all optical magnifications are assumed to be 1. The diffraction-limited field point spread functions for fluorescence excitation and emission are denoted as h ex (x) and h em (x). These field point spread functions are calculated by Debye integration, especially when cylindrical lenses are involved:

[0104]

[0105] where P(ξ) is the pupil function, the coordinate ξ is the lateral displacement in the pupil plane, normalized by the pupil size from -1 to 1, and v is the optical coordinate given by v = 2πNA·x / λ.

[0106] To obtain the system point spread function, locate the fluorescent point object at the origin of the sample plane, which can be expressed as δ(x1). When the illumination line focus is scanned to an offset distance s from the origin, the excitation field point spread function at the sample plane is simply h ex (x1+s). Without loss of generality, the fluorescence quantum yield is assumed to be 1. Therefore, the emission field from the object is given by:

[0107] E1(x1,s)=|h ex (x1+s)|δ(x1)

[0108] The emission field is scanned in reverse by the first scanner (GM1) and propagates to the slit plane. Its distribution can be determined by shifting the center from the origin by |h ex (s)|h em The slit can be placed on the slit plane as a spatial filter.

[0109] In this optical system, the modulation direction is aligned parallel to the slit. This parallel orientation plays a role in the effectiveness of the slit as a spatial filter, which affects the overall quality and resolution of the image reconstruction process.

[0110] For a finite-sized slit, its aperture can be modeled by a piecewise function:

[0111]

[0112] Where 2d is the slit width. The emission field through the slit is simply:

[0113] E2(x2,s)=|h ex (s)|h em (x2-s)D(x2)

[0114] The emission intensity is then rescanned by the second scanner (GM2) and mapped to the detection plane. Its distribution is also given by h em OK, but shift s ′ , which is an offset that depends on the angular amplitude of GM2, which may be different from the angular amplitude of GM1.

[0115] Here, we define the sweep factor (rescan ratio) η such that s ′ =ηs. Therefore, the instantaneous field distribution in the detection plane is given by:

[0116] E3(x3,s)=∫dx2E2(x2)h em (x3+x2-s ′ )

[0117] =∫dx2|h ex (s)|h em (x3+x2-s ′ )h em (x2-s)D(x2)

[0118] The intensity point spread function at the detection plane can be obtained by integrating over s:

[0119] I3(x3)=∫ds|E3(x3,s)| 2 =∫ds|h ex (s)| 2 |∫dx2h em (x3+x2-s′)h em (x2-s)D(x2)| 2

[0120] Consider two extreme cases. If an infinitely narrow slit is used, the slit function is equivalent to a delta function expressed as δ(x2). Under these conditions, the equation for I3(x3) simplifies to the following form:

[0121]

[0122] in, Represents the convolution operation. PSF c and PSF w They are the diffraction-limited confocal point spread function and the widefield point spread function, respectively.

[0123] If no slit is used, the slit function becomes constant (D(x2)=1). We then define s ″=(η-1)s, so that the equation for I3(x3) can be rewritten as:

[0124]

[0125] This detection plane PSF can be mapped back to the sample plane and it becomes:

[0126]

[0127] When η = 1, the system works in normal scanning mode, in which the intensity point spread function is reduced to |h em (x1)| 2 , which is equivalent to PSF w In the case of η>1, the system PSF is the convolution of two wide-field PSFs, which are compressed by a factor of and η.

[0128] like Figure 2B For example, the best case is η = 2 when both widefield PSFs are squeezed equally by a factor of two. In this case, w In comparison, the FWHM (full width at half maximum) I1(x1) is increased by about 1.5 times.

[0129] Further numerical investigation of the system behavior is performed when the slit size is finite and the rescanning ratio is kept at η = 2.

[0130] Figure 3A is a graphical representation of simulated PSF for various slit sizes according to embodiments herein. Coordinate x1 is normalized to r0 = 0.61λ / NA. PSF w Also plotted in the same figure for comparison. For slit sizes d = 0.1r0, 0.5r0, r0, and 5r0, the PSF FWHM of the image rescanning system is reduced by a factor of 1.17, 1.25, 1.37, and 1.49, respectively. A slit size of d = 5r0 produces a resolution enhancement very close to that of the open slit case, while d = r0 appears to be a good compromise between super-resolution and background suppression.

[0131] Figure 3B is a graphical representation of simulated optical transfer functions (OTFs) for various slit sizes according to embodiments herein. The OTF of the widefield system is plotted at the cutoff spatial frequency f c =1 / r0. On the contrary, when d=5r0, the spatial frequency band is stretched to 2f cIt implies that if an appropriate deconvolution process is followed to reshape the spectrum, the resolution can be enhanced by a factor of 2 instead of 1.5. For slit sizes more relevant to optical sectioning (e.g. = r0), resolution enhancements exceeding 1.6 can be easily achieved with the aid of deconvolution.

[0132] Figure 4 is a schematic representation of an optical path and computational workflow 400 used in CR-SIM image reconstruction according to embodiments herein. Figure 5 This is an example of an embodiment according to this invention Figure 4 Flowchart of the high-level processing steps of the image reconstruction process 500. In the following description, Figure 5 Refer to the process steps outlined in Figure 4 The optical components and computational modules are illustrated in order to explain the functions of the components in the CR-SIM image reconstruction process. The reconstruction process 500 is crucial for separating and recombining the frequency components shifted by spatial modulation, and enhancing the resolution in the modulation direction (i.e., parallel to the confocal slit 132).

[0133] In an example implementation, the CR-SIM image reconstruction process 500 begins by acquiring raw images at different illumination pattern phases in step 502. In an embodiment, the raw images I may be captured by a camera for illumination pattern phases of 0 degrees, 120 degrees, and 240 degrees. 0° 402、I 120° 408、I 240° 414. Due to the rescan ratio η=2, the original images 402, 408 and 414 can be further stretched in the horizontal direction.

[0134] In step 504, each of the original images 402, 408, and 414 may be subjected to a two-dimensional Fourier transform (FT) to convert each image to a corresponding frequency domain 404, 410, and 416. Application of the FT to the original images produces three separate spectral representations.

[0135] In an exemplary embodiment, corresponding to The spectrum of the original image can be related to the sample spectrum by the following formula:

[0136]

[0137] in, and is and can correspond to Spend, Degree and where H(k) is the optical transfer function (OTF), Py is the spatial frequency of the modulated illumination pattern, M is the modulation matrix, and N is random noise.

[0138] In an embodiment, even if the EOM 106 driving voltage has been pre-calibrated, the actual phase may deviate from the above ideal values (0 degrees, 120 degrees, and 240 degrees). Therefore, the inverse modulation matrix 442 can be used to estimate the actual phase shift.

[0139] In step 506, the inverse modulation matrix M may be used -1 442 to retrieve the baseband (S0) 418 and the frequency shift spectrum (S +p and S -p )406 and 412.

[0140] It can be seen that in order to best estimate the OTF, a weighted sample spectrum can be generated by inverse modulation of the matrix M.

[0141] However, in thick tissue imaging, background fluorescence emission from out-of-focus regions cannot be adequately modeled by OTF alone. After reconstruction using the standard algorithm described above, the background will obscure the spectrum estimate (especially the baseband spectrum estimate). ) distortion. As a result, the effective resolution and image quality may be significantly degraded.

[0142] Assuming that the background is dominated by low-frequency components and is independent of the modulation phase, we assume that the estimated spectrum using the above equation for the spatial spectrum of the original image is and It is not obviously offset by the background, but the background is concentrated in the center.

[0143] In step 508, a demodulated image (I d ) 438. The algorithm 440 may correspond to an optical sectioning SIM algorithm that substantially suppresses out-of-focus emission and systematic biases (e.g., camera dark current). FT is applied to the demodulated image 438 to generate a spectrum (S d )436.

[0144] In step 510, a high-pass filter 432 may be applied to the baseband component 418 to filter out low-frequency components, and a low-pass filter 434 may be applied to the spectrum 436 to filter out high-frequency components. In an example embodiment, the high-pass filter 432 and the low-pass filter 434 may use the same cutoff frequency P. o To filter the components.

[0145] In step 512, the output of the high pass filter 432 and the output of the low pass filter 434 may be combined 420 to produce a corrected baseband spectrum. In an exemplary embodiment, the combining algorithm 420 includes using baseband (So )418 corresponding area replacement spectrum (S d )436 so as to produce a corrected baseband spectrum 430 which is a background-free spectrum.

[0146] In an example method of the demodulation process as provided in steps 508, 510, and 512, a low cutoff frequency P0 can be selected empirically to define a circular frequency range in which the spectrum needs to be corrected. The demodulation method can be used to retrieve background-free low-frequency content from its frequency-shifted spectrum. For example, the demodulated image can be obtained by the following equation

[0147]

[0148] In this demodulated image equation, the background is essentially eliminated by subtraction. This is now given by Corrected version of: in, are high-pass and low-pass filters with the same cutoff frequency P0, and F[·] denotes a two-dimensional FT.

[0149] In step 514, the corrected baseband spectrum 430 may be combined with the frequency-shifted spectra 406 and 412 to produce a composite spectrum 422 whose frequency range is extended in the vertical direction.

[0150] In step 516, an inverse FT (FT -1 ) to convert the spectrum 422 back to the spatial domain, thereby producing a reconstructed image 424.

[0151] For example, the estimated spectra above can be combined and to form a synthetic spectrum 422, which is used to produce a reconstructed SIM image super-resolution with suppressed background along the modulation direction.

[0152] In step 518, the reconstructed image 424 may be adjusted to correct for any distortion caused by image rescanning, where the image scale may have changed. For example, during super-resolution acquisition of the image 424, the rescan ratio may have been set to 2. Thus, the original image is stretched by a factor of 2 in the scan direction (orthogonal to the slit).

[0153] In step 518, the image 424 may be resized in the scan direction (e.g., reduced by a factor of 2) to restore the original aspect ratio using the rescan inversion technique 426. The rescan inversion 426 may reduce the horizontal size of the image 424 by half by applying the rescan inversion 426 by a factor of 2.

[0154] In an exemplary embodiment, the rescan inversion 426 effectively doubles the frequency content of the image 424 in the direction of the distortion. This frequency doubling enhances the resolution of the output image 428 in that particular direction, thereby contributing to the super-resolution effect of the CR-SIM image 428.

[0155] Experimental results

[0156] The following section describes various experiments performed to evaluate embodiments of the present invention. Some of these experiments illustrate embodiments of the present invention in addition to those discussed above.

[0157] A. Quantitative Verification of CR-SIM

[0158] A major technical advantage of CR-SIM is its imaging speed. Conventional SR-SIM systems require the acquisition of at least nine raw images and involve rotation of the illumination pattern. In contrast, CR-SIM requires only three raw images to reconstruct a super-resolution image. Significantly improved imaging speed is achieved without compromising image quality. The resolution enhancement provided by CR-SIM has been verified and characterized using 100nm yellow-green fluorescent beads and a 60× / 1.2NA OBJ lens. CR-SIM image resolution was measured at 175 and 179nm in the horizontal and vertical directions, respectively, which is 1.47 to 1.51 times better than the diffraction limit. After Richardson-Lucy deconvolution, the resolution improvement can reach 1.93 to 1.99 times, as further detailed below with respect to the spatial resolution enhancement characterized by fluorescent beads.

[0159] Enhanced spatial resolution of characterization by fluorescent beads

[0160] The lateral resolution of the CR-SIM system was characterized using 100 nm fluorescent beads, which were emission filtered using a bandpass (20 nm bandwidth) filter centered at 520 nm. To verify the high-resolution performance, a water immersion objective lens (UPlanSApo 60X / 1.20W, Olympus) with a high numerical aperture (NA) of 1.2 and a high magnification of 60x was used for imaging experiments.

[0161] Figure 6A is an image capture of 100 nm fluorescent beads using a laser scanning confocal microscope (LSCM) system according to an embodiment herein. Figure 6B is an image capture of the same 100 nm fluorescent beads using a CR-SIM system according to an embodiment herein, which highlights the difference in imaging quality. Figure 6C is the Richardson-Lucy deconvolution after 10 iterations according to the embodiment of this article Figure 6BProcessed image of 100 nm fluorescent beads shown. The deconvolution parameters were chosen to achieve the highest resolution improvement while avoiding over-amplification of noise that can lead to ringing artifacts. Figure 6D According to the comparison of the embodiments of this article Figures 6A to 6C Graphical representation of the intensity distribution captured in [1]. The spatial resolution measured from the LSCM bead image is 291 nm horizontally and 294 nm vertically. In contrast, the CR-SIM image resolution is 175 nm horizontally and 179 nm vertically. This represents a 1.64-1.66-fold improvement over the LSCM and a 1.47-1.51-fold improvement over the diffraction limit. After deconvolution, the resolution is 1.93-1.99 times greater than the diffraction limit, nearly doubling the resolution band limit.

[0162] Another fundamental technical improvement of CR-SIM is its powerful optical sectioning capability, which is desirable for deep tissue imaging. The fixed orientation of the illumination pattern enables the inclusion of a slit to block significant background emission. This physical spatial filter, combined with the additional background suppression provided by the CR-SIM reconstruction algorithm, results in outstanding signal-to-background ratio (SBR) and signal-to-noise ratio (SNR).

[0163] A thick tissue phantom made of 2 μm fluorescent beads in a 2% lipid emulsion was used to quantify both specifications. The phantom was scanned three-dimensionally from the surface down to a depth of 500 μm using CR-SIM, LSCM, and WF-SIM in 1 μm increments using a 20× / 0.95 NA (XLUMPFLN, Olympus) OBJ lens. To simplify comparison, WF-SIM images were acquired using the same CR-SIM imaging platform, but with the slit fully open. For a fair comparison, the exposure time for each raw image of WF-SIM / CR-SIM was set to 100 ms, while the exposure time for LSCM images was set to 300 ms. The image acquisition time for the entire volume stack was 200 s.

[0164] Figure 7 is a comparative set of fluorescence microscopy images at varying depths of focus (0, 250, and 500 μm) within a thick tissue phantom using WF-SIM, LSCM, and CR-SIM according to embodiments herein. Figure 8A is a graphical representation of signal-to-background ratio (SBR) as a function of depth for fluorescent beads imaged by WF-SIM, LSCM, and CR-SIM according to embodiments herein. Figure 8Bis a graphical representation of the signal-to-noise ratio (SNR) as a function of depth for fluorescent beads imaged by WF-SIM, LSCM, and CR-SIM according to embodiments herein. The SBR and SNR values for beads imaged by WF-SIM, LSCM, and CR-SIM were measured across a depth range extending from the surface to 500 μm.

[0165] Figure 7 as well as Figure 8A and Figure 8B Imaging data presented in

[15] demonstrate that WF-SIM, obtained using a reconstruction algorithm capable of optical sectioning, can remove low-frequency background at a similar level to that removed by LSCM, and both provide closely matching SBRs. However, this computational approach fails to suppress random noise associated with the background, which can be more effectively eliminated using a physical slit. At a depth of 500 μm, the microbeads are barely distinguishable from the noisy background in WF-SIM images. On the other hand, they remain visible in LSCM images, even with considerably lower contrast and very poor effective resolution. Because the CR-SIM technique combines both background suppression mechanisms, the SBR is further improved by approximately 20 dB across the entire depth range. Surprisingly, the SBR of CR-SIM at depths exceeding 300 μm matches that of both WF-SIM and LSCM at the surface. Furthermore, CR-SIM inherits the strong noise reduction provided by the slits integrated into the system. Although its SNR is slightly lower than that of LSCM due to modulation and demodulation losses, it is approximately 8.9 to 19.6 dB better than that of WF-SIM.

[0166] Figure 9 Shown are three-dimensional volume renderings of a thick bead phantom according to an embodiment of the present invention, illustrating the fluorescence intensity distribution. The left rendering was obtained using wide-field structured illumination microscopy (WF-SIM), the middle rendering is from laser scanning confocal microscopy (LSCM), and the right rendering was obtained using confocal rescanning structured illumination microscopy (CR-SIM). These renderings were generated using IMARIS volume rendering software and cover a depth range from the surface to 500 microns. Figure 9 A comparative visualization of the entire bead mold stack is provided, which presents the differences in imaging quality and fluorescence intensity distribution between the three microscopy methods.

[0167] B. Cell Sample Imaging

[0168] The effectiveness of the following imaging technique was initially demonstrated by imaging fixed HeLa cells, where F-actin was detected using Texas Red TM-X phalloidin stain, which emits at a peak of 608 nm. The laser (561 nm) power reaching the sample surface was <1 mW, and a 60× / 1.2 NAOBJ was used. It took 300 ms to acquire three raw images.

[0169] Figure 10 presents a series of images showing F-actin structure within fixed HeLa cells, captured using LSCM and CR-SIM techniques. Figure 10A is an image capture of fixed HeLa cells using LSCM to identify F-actin structures according to an embodiment herein. Figure 10B According to the embodiment of this article Figure 10A Shown are images of the same identified F-actin structures captured within fixed HeLa cells but using CR-SIM imaging. Figure 10D According to the embodiment of this article Figure 10A Magnified view of the region of interest (ROI) for the identification of F-actin structures using LSCM. Figure 10E According to the embodiment of this article Figure 10B Magnified view of the region of interest (ROI) for identification of F-actin structure using CR-SIM.

[0170] LSCM images are formed by simply finding the average of three raw images. They include a background component due to the camera's dark current. Although this uniform background can be digitally removed in post-processing, a certain amount of inhomogeneous background is evident in LSCM images, even for very thin specimens. This is likely caused by residual out-of-focus light leaking through the slit. CR-SIM images, on the other hand, are essentially free of background contamination. Furthermore, resolution is isotropically enhanced compared to LSCM images.

[0171] Figure 10C The comparative F-actin structure according to the examples herein is along Figure 10A and Figure 10B Graphical representation of the normalized intensity distribution of the identified regions in . Figure 10C By presenting along Figure 10A and Figure 10B Line profiles are obtained from identified regions of interest (ROIs) to provide a quantitative assessment of image quality improvement.

[0172] The LSCM intensity is essentially above zero in the presumed dark regions. The minimum distance between barely discernible peaks in the LSCM profile is 317 nm, closely matching the diffraction-limited resolution of 309 nm. In contrast, the CR-SIM profile demonstrates that CR-SIM automatically and efficiently removes all types of background signal. F-actin fibers with spacings less than 200 nm can be readily separated, demonstrating a resolution enhancement of more than 1.55-fold.

[0173] C. In vitro imaging of GFP mouse brain thick sections

[0174] Super-resolution fluorescence imaging experiments were performed using thick brain slices from Thy1-EGFP transgenic mice, in which neurons were labeled with EGFP. CR-SIM images were acquired using a 473 nm laser and a 40× / 1.3 NA (UPlanFLN, Olympus) OBJ lens. Fluorescence emission was filtered through a bandpass filter centered at 520 nm, resulting in a diffraction-limited resolution of 244 nm.

[0175] Figure 11A is a volume rendering of a CR-SIM image stack of a Thy1-EGFP transgenic mouse brain slice according to an example herein. Figure 11A The volume rendering was captured from an imaging area of 33.6 μm × 37.8 μm × 138 μm (width × height × depth). The depth range was scanned in 1 μm increments, resulting in 138 en face image slices. The exposure time for acquiring one raw image was 100 ms, and a total of 45 s were spent to complete the 3D volume scanning process. Neuronal somata, axon segments, and numerous dendrites / spines are clearly visible with high definition throughout the entire depth range. Fluorescently labeled structures are densely packed in the space, which would result in an overwhelming background in the WF-SIM image.

[0176] Figure 11B are comparative images showing side-by-side visualization of neuronal somata at Z=23 μm using both LSCM (left) and CR-SIM (right) systems, according to embodiments herein. Figure 11B Combined images (LSCM vs. CR-SIM) of a neuronal soma at Z = 23 μm were formed to provide a side-by-side comparison of image quality. Background emission from the sample is so strong that a significant number of photons leak through the slit, compromising the contrast and effective resolution of the LSCM image. CR-SIM, on the other hand, provides richer and more detailed morphological information.

[0177] Figure 11C A magnified view of a 2.4 μm × 2.4 μm area near the center of the neuronal soma is provided, as shown in Figure 11B The images are highlighted in the box and were captured using the CR-SIM system. Figure 11CAnnotated with the labels "H" and "V" to indicate specific horizontal and vertical lines within the imaging area, respectively. Figure 11D A graphical representation of the intensity distribution along these lines is provided. Figure 11D There are two graphs: one corresponding to the horizontal line (marked "H") and the other corresponding to the vertical line (marked "V"), as shown in Figure 11C It can be seen in. Figure 11D The intensity distribution in exemplifies multiple peaks approximately 160 nm apart along the “H” and “V” directions. These peaks indicate a significant enhancement in resolution, exceeding the conventional diffraction limit by a factor of more than 1.5, as demonstrated in the CR-SIM image.

[0178] Figure 11E is a three-dimensional rendering of a partial volume scan of a mouse brain tissue section obtained by CR-SIM according to an embodiment herein. Figure 11E A partial volume scan of a tissue stack is presented, illustrating detailed neuronal structure, where connections of axons, dendrites, and dendritic spines are visualized in a 3D rendered view.

[0179] Figures 11F to 11H The CR-SIM system is used according to the embodiment of this article at 113 μm ( Figure 11F )、132μm( Figure 11G ) and 197μm( Figure 11H ). To better illustrate the finer structures in these images, some small areas were selected (indicated by dotted boxes) and further magnified.

[0180] Figures 11I to 11K The same Thy1-EGFP transgenic mouse brain slice is magnified 4 times according to the examples in this article. Figures 11F to 11H Image capture of the region in the image. Relatively thin dendrites with small apparent diameter but strong SNR were selected for size estimation. Figures 11I to 11K The minimum apparent diameter in each panel is marked directly on the image. The median value of 160 nm is consistent with the Figure 11D The estimated spatial resolution is in good agreement with the thick tissue imaging results, providing concrete evidence that CR-SIM can image deep into biological tissues with super-resolution and excellent contrast.

[0181] Comments on the experimental results

[0182] As can be seen, CR-SIM effectively combines structured illumination, image rescanning, and confocal detection to achieve significant technological breakthroughs in imaging speed and depth. It can perform high-quality super-resolution fluorescence imaging in cell samples and thick tissues. The significantly reduced image acquisition time is highly desirable for capturing fast dynamic biological processes, delaying fading in time-lapse imaging experiments, and minimizing phototoxicity. In addition, the computational load for SIM reconstruction becomes significantly lower. This enables real-time rendering of super-resolution images.

[0183] Similar to WF-SIM, CR-SIM can achieve better resolution in the modulation direction by using a higher modulation frequency, which was set to 0.5× the resolution limit for the in vitro imaging experiments reported in this paper. The trade-off will be a lower modulation depth in the modulation pattern and an increased noise level in the reconstructed image. Moreover, the resolution along the image rescanning dimension can be further improved by using an appropriate deconvolution process. The 1.5-fold enhancement is estimated based on the full width at half maximum of the point spread function (as described above in the principle of one-dimensional image rescanning). Although the OTF is more attenuated in the high-frequency range, the non-zero bandwidth still extends to twice the diffraction limit. Therefore, it is feasible to numerically reshape the OTF and achieve a maximum resolution enhancement of 2 times. In practice, this must be done carefully to find the best compromise between spatial resolution and SNR.

[0184] CR-SIM technology significantly improves imaging speed, signal-to-background ratio (SBR), and signal-to-noise ratio (SNR). Using this method, high-quality, super-resolution fluorescence images of EGFP-labeled neuronal structures were successfully captured from mouse brain tissue sections at a depth of 209 μm, exceeding the capabilities of conventional wide-field structured illumination microscopy (WF-SIM). These technical improvements position CR-SIM technology as highly suitable for a wide range of biomedical applications, including studies involving cell samples and small animal models.

[0185] Sample preparation

[0186] This section outlines the protocols employed in preparing the various samples for imaging. This includes the generation of fluorescent bead phantoms, the preparation of HeLa cell samples, and the careful sectioning of mouse brain tissue, each of which is critical for the subsequent imaging process.

[0187] Preparation of fluorescent bead phantom

[0188] To characterize the imaging performance of our imaging system, we used the Yellow-green microspheres with an excitation maximum at 441 nm and an emission maximum at 486 nm. To quantify super-resolution enhancement, we prepared a thin layer of gel mixed with 1 μL of 100 nm fluorescent beads (Cat. No. 17150) and 1 mL of 2% agarose solution. Agarose is used to immobilize the beads and also provides a transparent and non-scattering background during imaging. The bead mixture was then vortexed on a vortex mixer, poured into a center well dish (MatTek, P35G-0-10-C), and allowed to stand until solidified for imaging.

[0189] To demonstrate imaging performance in thick scattering media, a thick bead phantom was prepared to simulate the scattering properties of biological tissue. To increase signal levels in deep regions, large beads (2 μm diameter) were used in the scattering medium. This is because beads with a large diameter can allow a larger surface area for exciting fluorophores and thus generate more fluorescence photons at the same excitation power. We evenly dispersed 10 μL of an aqueous suspension of 2 μm fluorescent beads (Catalog No. 18140-2) in 40 μL of a 20% Lipofundin MCT / LCT emulsion (B. Braun Melsungen AG, Germany). 350 μL of molten agarose solution was then added to the above solution. A total of 400 μL of the solution was pipetted into the well of a single concave glass microscope slide (1.2-1.3 mm thick) and then sealed with a coverslip using nail polish. Thus, the bead mold had a concentration of 2% lipid emulsion and a density of 1137 fluorescent beads / μL. According to the calculation using Mie theory calculator, the isotropic scattering factor g is about 0.715 and the scattering coefficient μ is s About 113.3cm -1 , which gives a reduced scattering coefficient μ s =(1-g)μ s =32.3cm -1 This value is close to the average reduced scattering coefficient of biological tissue.

[0190] Preparation of HeLa cell samples

[0191] To prepare fluorescent slides of living HeLa cells, we used Texas Red TM -X phalloidin (Invitrogen) was used to label F-actin of HeLa cells. First, HeLa cells (ATCC) were cultured in high glucose Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 units / mg penicillin, and 100 μg / mL streptomycin at 37°C and 5% CO2. TM-X phalloidin labeled HeLa cells were plated at 7 × 10 cells per well in 0.5 ml growth medium. 4 Cells were seeded at a density of 100 μg / mL on coverslips in 24-well plates and allowed to grow for 20-24 hours. Next, the cells were washed three times with 1x PBS, fixed with 4% paraformaldehyde, and permeabilized with 0.1% Triton X-100. The cells were then stained with phalloidin solution (0.165 mM) for 30 minutes and washed with PBS. Finally, the coverslips with fixed cells were mounted on slides with Fluoshield™ (Sigma) and prepared for imaging.

[0192] Preparation of mouse brain slices

[0193] In order to prepare mouse brain slices, Thy1-EGFP transgenic mouse brain was fixed in 4% paraformaldehyde (w / v) in PBS at 4°C overnight. 250 μm vibrating microtome slices were generated by slicing the tissue embedded in 2% agarose with a vibrating microtome (Leica), and permeabilized in 2% TritonX-100 (v / v) in PBS at 4°C overnight. According to the manufacturer's instructions, RapiClear 1.52 (Sun Jin Laboratory (SunJin Lab) company) was used to make the slices transparent, which is a water-soluble clearing agent for enhancing biological sample visualization. After 1 hour, the mouse brain slices became transparent and were placed in iSpacer microchamber (Sun Jin Laboratory company). The space outside the microchamber was filled with nail polish to ensure a safe seal.

Claims

1. A microscope system comprising: a light source comprising at least one laser configured to generate a combined light beam; an optical unit configured to separate the light beam into a plurality of light beams in a focal plane so as to generate a plurality of illumination patterns, wherein the optical unit comprises at least one Wollaston prism, an objective lens or an electro-optical modulator; a focusing lens configured to focus the plurality of light beams along a first direction to form a focal line on a sample; a scanning module comprising one or more scanning mirrors for scanning the focal line across the sample along a second direction and an emission collection mirror for rescanning corresponding fluorescent emissions from the sample; a confocal slit for filtering out-of-focus light associated with the fluorescent emission from the sample to provide filtered fluorescent emission; and A detection unit is configured to acquire a plurality of images of the sample corresponding to the filtered fluorescence emissions.

2. The microscope system according to claim 1, further comprising an image processor for reconstructing a high-resolution image characterizing the sample based on a combination of a set of captured raw images corresponding to the illumination patterns of the three phases.

3. The microscope system according to claim 1 or 2, wherein: The reconstruction of the high-resolution image representing the sample includes: separating the frequency components of the original image, adjusting the rescan ratio along the scanning direction to obtain one or more reconstructed original images, and restoring the rescan ratio of the reconstructed original image along the scanning direction.

4. The microscope system according to claim 2 or 3, wherein: The reconstruction of the high resolution image representing the sample is achieved without rotating the plurality of illumination patterns.

5. Microscope system according to any one of the preceding claims, wherein The sample is a biological tissue specimen.

6. Microscope system according to any one of the preceding claims, wherein: The scanning module is implemented in the form of two galvanometer mirrors that operate synchronously to scan and rescan the sample.

7. Microscope system according to any one of the preceding claims, wherein The optical unit further comprises a retarder for adjusting a polarization component of at least one of the separated light beams so as to generate the plurality of illumination patterns with adjusted modulation depths.

8. Microscope system according to any one of the preceding claims, wherein The optical unit further includes a wave plate for adjusting the polarization distribution of the light beam so that the light beam is suitable for being separated into the plurality of light beams.

9. Microscope system according to any one of the preceding claims, wherein: The electro-optic modulator phase-shifts one separated light beam relative to another separated light beam.

10. Microscope system according to any one of the preceding claims, wherein The focusing lens is a cylindrical lens, and the first direction is orthogonal to the second direction.

11. Microscope system according to any one of the preceding claims, wherein The plurality of illumination patterns are generated based on detected interference between the plurality of separated light beams.

12. Microscope system according to any one of the preceding claims, wherein: The confocal slit is fixed in angular position or orientation.

13. Microscope system according to any one of the preceding claims, wherein: The Wollaston prism separates the light beam into the plurality of light beams, wherein a separation angle of the Wollaston prism is determined.

14. The microscope system according to claim 13, wherein: A modulation frequency corresponding to the Wollaston prism is determined based on image resolution and signal-to-noise ratio.

15. Microscope system according to any one of the preceding claims, wherein: The confocal slit is optically conjugate to the focal line to filter out-of-focus light.

16. Microscope system according to any one of the preceding claims, wherein: The detection unit includes a scientific complementary metal oxide semiconductor (CMOS) camera synchronized with the one or more scanning mirrors and the emission collection mirror to achieve one-dimensional image rescanning.

17. Microscope system according to any one of the preceding claims, wherein: The scanning module is configured to increase an angular velocity of the one or more scanning mirrors relative to the emission collection mirror to achieve a predetermined rescan ratio that is optimal for resolution enhancement.

18. A light microscopy method comprising: Generate a beam; splitting the light beam into a plurality of light beams in a focal plane so as to generate a plurality of illumination patterns; focusing the plurality of light beams along a first direction to form a focal line on a sample; scanning the focal line across the sample along a second direction and rescanning corresponding fluorescence emission from the sample; filtering out-of-focus light associated with the fluorescent emission from the sample to provide filtered fluorescent emission; as well as A plurality of images of the sample corresponding to the filtered fluorescence emissions are acquired.

19. A method for image reconstruction, comprising: acquiring a set of raw images corresponding to a plurality of phases of the illumination pattern; Applying Fourier transform (FT) to each of the original images in the acquired group to obtain a corresponding original spectrum of each of the original images; Using an inverse matrix to separate the original spectrum of each of the original images into a baseband spectrum and at least two modulation shifted spectra; demodulating the set of original images to generate a demodulated image; applying a high-pass filter to the baseband spectrum and applying a low-pass filter to the spectrum of the demodulated image to generate a corrected baseband spectrum; combining the corrected baseband spectrum with the at least two modulation shifted spectra to generate a composite spectrum; and An inverse Fourier transform is performed on the synthesized spectrum to produce a reconstructed image.

20. The method according to claim 19, further comprising: The reconstructed image is resized to correct aspect ratio distortion, thereby producing the reconstructed image having a reduced horizontal dimension.

21. The method according to claim 19 or 20, wherein Demodulating the set of raw images further includes suppressing out-of-focus light and systematic deviations from the raw images, wherein the systematic deviations include camera dark current.

22. The method according to any one of claims 19 to 21, wherein The phases of the lighting pattern include 0 degrees, 120 degrees, and 240 degrees.

23. The method according to any one of claims 19 to 22, wherein The corrected baseband spectrum is generated by adding the high-pass filtered baseband spectrum to the low-pass filtered spectrum of the demodulated image, wherein the high-pass filter and the low-pass filter use the same predetermined cutoff frequency.

24. The method according to any one of claims 19 to 23, wherein The demodulated image corresponds to a standard resolution image, and the generated synthetic spectrum includes a frequency range extending in a vertical direction.

25. The method according to any one of claims 20 to 24, wherein The resizing step is performed based on a rescanning ratio of two, thereby achieving super-resolution along at least a horizontal direction of the reconstructed image.

Images