Optical sectioning microscopy can provide highly detailed three dimensional (3D) images of biological samples. However, it requires acquisition of many images per volume, and is therefore time consuming, and may not be suitable for live cell 3D imaging. We propose the use of the modified Gerchberg-Saxton phase retrieval algorithm to enable full 3D imaging of gold nanoparticles tagged sample using only two images. The reconstructed field is free space propagated to all other focus planes using post processing, and the 2D z-stack is merged to create a 3D image of the sample with high fidelity. Because we propose to apply the phase retrieving on nano particles, the regular ambiguities typical to the Gerchberg-Saxton algorithm, are eliminated. The proposed concept is then further enhanced also for tracking of single fluorescent particles within a three dimensional (3D) cellular environment based on image processing algorithms that can significantly increases localization accuracy of the 3D point spread function in respect to regular Gaussian fitting. All proposed concepts are validated both on simulated data as well as experimentally.
Super-resolution localization microscopy can overcome the diffraction limit and achieve a tens of order improvement in resolution. It requires labeling the sample with fluorescent probes followed with their repeated cycles of activation and photobleaching. This work presents an alternative approach that is free from direct labeling and does not require the activation and photobleaching cycles. Fluorescently labeled gold nanoparticles in a solution are distributed on top of the sample. The nanoparticles move in a random Brownian motion, and interact with the sample. By obscuring different areas in the sample, the nanoparticles encode the sub-wavelength features. A sequence of images of the sample is captured and decoded by digital post processing to create the super-resolution image. The achievable resolution is limited by the additive noise and the size of the nanoparticles. Regular nanoparticles with diameter smaller than 100nm are barely seen in a conventional bright field microscope, thus fluorescently labeled gold nanoparticles were used, with proper
This paper presents a method for modifying the point spread function (PSF) into a doughnut-like shape, through the utilization of the plasma dispersion effect (PDE) of silicon-coated gold nanoparticles. This modified PSF has spatial components smaller than the diffraction limit, and by scanning the sample with it, super-resolution can be achieved. The sample is illuminated using two laser beams. The first is the pump, with a wavelength in the visible region that creates a change in the refractive index of the silicon coating due to the PDE. This creates a change in the localized surface plasmon resonance wavelength. Since the pump beam has a Gaussian profile, the high intensity areas of the beam experience the highest refractive index change. When the second beam (i.e., the probe) illuminates the sample with a near-infrared wavelength, this change in the refractive index is transformed into a change in the PSF profile. The ordinary Gaussian shape is transformed into a doughnut shape, with higher spatial frequencies, which enables one to achieve super-resolution by scanning the specimen using this PSF. This is a step toward the creation of a nonfluorescent nanoscope.
This work presents the use of flickering nanoparticles for imaging biological samples. The method has high noise immunity, and it enables the detection of overlapping types of GNPs, at significantly sub-diffraction distances, making it attractive for super resolving localization microscopy techniques. The method utilizes a lock-in technique at which the imaging of the sample is done using a time-modulated laser beam that match the number of the types of gold nanoparticles (GNPs) that label a given sample, and resulting in the excitation of the temporal flickering of the scattered light at known temporal frequencies. The final image where the GNPs are spatially separated is obtained using post processing where the proper spectral components corresponding to the different modulation frequencies are extracted. This allows the simultaneous super resolved imaging of multiple types of GNPs that label targets of interest within biological samples. Additionally applying the post-processing algorithm of the K-factor image decomposition algorithm can further improve the performance of the proposed approach.
In this paper we present gold nanoparticles coated with silicon that switch the order between the scattering and the absorption magnitude at the resonance peak and tune the plasmon resonance over the spectrum. This is obtained by modifying the refractive index of the silicon coating of the nanoparticle by illuminating it with a pumping light due to the plasma dispersion effect in silicon. We also report how changing the diffraction limited point spread function through the utilization of plasma dispersion effect of the above mentioned silicon coated nanoparticles allows doing imaging with sub wavelength resolution. The plasma dispersion effect can increase the absorption coefficient of the silicon, when illuminated with a focused laser beam and as explained above it can also tune the absorption versus scattering properties of the nanoparticle. Due to the Gaussian nature of the laser illumination which has higher intensity at its peak, the plasma dispersion effect is more significant at the center of the illumination. As a consequence, the reflected light from probe beam at the near infra-red region has a sub wavelength dip that overlaps with the location of the pump illumination peak. This dip has a higher spatial frequency than an ordinary Gaussian, which enables to achieve super resolution.
We propose the use of a two dimensional Barker-based array in order to improve the performance of the standard time multiplexing super resolution system. The Barker-based array is a 2D generalization of the standard 1D Barker code. It enables achieving a two dimensional super resolution image using only one dimensional scan, by exploiting its unique auto correlation property. A sequence of low resolution images are captured at different lateral positions of the array, and are decoded properly using the same array. In addition, we present the use of a mismatched array for the decoding process. The cross correlation between the Barker-based array and the mismatched array has a perfect peak to sidelobes ratio, making it ideal for the super resolution process. Also, we propose the projection of this array onto the object using a phase-only spatial light modulator. Projecting the array eliminates the need for printing it, mechanically shifting it, and having a direct contact with the object, which is not feasible in many imaging applications. The proposed method is presented analytically, demonstrated via numerical simulation, and validated by laboratory experiments.
This work presents a technique for a full 3D imaging of biological samples tagged with gold-nanoparticles (GNPs) using only two images, rather than many images per volume as is currently needed for 3D optical sectioning microscopy. The proposed approach is based on the Gerchberg-Saxton (GS) phase retrieval algorithm. The reconstructed field is free space propagated to all other focus planes using post processing, and the 2D z-stack is merged to create a 3D image of the sample with high fidelity. Because we propose to apply the phase retrieving on nano particles, the regular ambiguities typical to the Gerchberg-Saxton algorithm, are eliminated. In addition, since the method requires the capturing of two images only, it can be suitable for 3D live cell imaging. The proposed concept is presented and validated both on simulated data as well as experimentally.
Proc. SPIE. 9713, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXIII
KEYWORDS: Microscopes, Point spread functions, Biomedical optics, 3D acquisition, 3D image enhancement, Data modeling, Image processing, Microscopy, Luminescence, Computer simulations, 3D modeling, Super resolution microscopy, 3D image processing
The ability to track single fluorescent particles within a three dimensional (3D) cellular environment can provide valuable insights into cellular processes. In this paper, we present a modified nonlinear image decomposition technique called K-factor that reshapes the 3D point spread function (PSF) of an XYZ image stack into a narrow Gaussian profile. The method increases localization accuracy by ~60% with compare to regular Gaussian fitting, and improves minimal resolvable distance between overlapping PSFs by ~50%. The algorithm was tested both on simulated data and experimentally. <p> </p>This work presets a novel use of the nonlinear image decomposition technique called K-factor that reshapes the three dimensional (3D) point spread function (PSF) of an XYZ image stack into a narrow Gaussian profile. The experimentally obtained PSF of a Z-stack raw data that is acquired by a widefield microscope has a more elaborate shape that is given by the Gibson and Lanni model. This shape increases the computational complexity associated with the localization routine, when used in localization microscopy techniques. Furthermore, due to its nature, this PSF spreads over a larger volume, making the problem of overlapping emitters detection more pronounced. The ability to use Gaussian fitting with high accuracy on 3D data can facilitate the computational complexity, hence reduce the processing time required for the generation of the 3D superresolved image. In addition it allows the detection of overlapping PSFs and reduces the effects of the penetration of out of focus PSFs into in focused PSFs, therefore enables the increase in the activated fluorophore density by ~50%. The algorithm was tested both on simulated data and experimentally, where it yielded an increase in the localization accuracy by ~60% with compare to regular Gaussian fitting, and improved the minimal resolvable distance between overlapping PSFs by ~50%, making it extremely applicable to the field of 3D biomedical imaging,