Single-particle imaging (SPI) at an X-ray free electron laser (XFEL) is demonstrating its potential to support the imaging and structure determination of biological specimens at atomic resolution without the need for crystallization. According to a principle of diffraction before damage, diffraction patterns of specimens with random orientations injected to the focus of ultra-short and extremely bright XFEL can be recorded before the specimens are damaged. For a successful image reconstruction, robust algorithms are needed. So far, fast and robust reconstruction algorithms from noisy and incomplete diffraction patterns have been challenges for XFEL SPI. Here we briefly outline the workflow of SPI and discuss key challenges and corresponding approaches to both phase retrieval and orientations determination. We also give an outlook of the promising algorithms for SPI by means of machine learning or deep learning and believe that the current computational challenges of XFEL SPI can be handled by utilizing techniques of modern data processing and artificial intelligence for imaging at atomic resolution and femtosecond temporal resolution in the future.
We present an intelligent imaging platform named as AiLab, which is a type of remote comprehensive experiment environment designed on a browser-server architecture. The AiLab enables users to access the server through a network utilizing a client browser for achieving the goal of remotely controlling instruments and monitoring the security status of the experimental environment. It consists of a suit of hardware and intelligent software. Some preliminary experiments demonstrate that the AiLab has simplified the development, deployment, maintenance and use of software. The core modules of AiLab are installed on the server, only requiring the client to have a browser that can access the network, without any other restrictions on the user's hardware and operating system. Users can access the AiLab through an internet using whatever any type of browser on either mobile phone, tablet, laptop or a desktop computer, easily achieving the goal of remote control of experimental equipment. If there is a need to update or expand system functions in the future, simply update the AiLab software on the server without making any modifications to the client browser. Possible application cases of the AiLab including remote experiment platform in emergency situations such as epidemic outbreaks, automatic online diseases diagnosis in rural and remote underdeveloped areas, etc.
Recently, experiments of X-ray free electron lasers (XFEL) single-particle coherent diffraction imaging (CDI) have demonstrated the potential to reconstruct the three-dimensional (3D) structure of non-crystalline biological samples with high spatial resolution. For successful reconstructions, a complete 3D Fourier transform of the particle is to be assembled by a lot of diffraction patterns from identical particles. In practice, this is difficult because the orientations of the injected particles are random. Incomplete 3D Fourier transform composed of limited projection orientations may cause lower resolution of 3D reconstruction. To get superior 3D reconstructions, equally sloped tomography (EST) may be applied to the incomplete 3D Fourier transform data set of single-particle CDI. In this paper, some challenges and corresponding approaches to CDI are briefly discussed. In principle, CDI can yield wavelength-limited resolution without any of the limitations of physical lenses. At present, CDI is deceptively simple to implement. Successful application of CDI to experimental data, however, is not universal and requires considerable mathematical physics skills and interdisciplinary experiences. Whether or not lensless microscopy can outweigh the lens-based one will depend on the competition between the computational power and the precise manufacturing technique of lens in the future, especially in the field of real time imaging. As a type of quickly developing algorithmic microscopy, researchers still should improve the precision, robustness and convergence speed of CDI reconstruction algorithms.
We present a type of diffractive lenses “Zernike apodized photon sieves” (ZAPS), which structure is based on the combination of two concepts: apodized photon sieves and Zernike phase-contrast. In combination with the synchrotron light sources, the ZAPS can be used as an objective for high-resolution X-ray phase-contrast microscopy in physical and life sciences. The ZAPS is a single optic that integrates the appropriate ±π/2 radians phase shift through selective zone placement shifts in an apodized photon sieve. The focusing properties of the ZAPS can be easily controlled by apodizing its pupil function. An apodized photon sieve with Gaussian pupil was fabricated by lithographic technique and showed that the side-lobes have been significantly suppressed at the expense of slightly widening the width of the main lobe.
A fiber-optic interferometer to measure differences in temperature between two single-mode fiber arms is described.
Temperature changes are observed as a motion of an optical interference fringe pattern. Values are calculated for the
temperature dependence of the fringe motion. Temperature measurements are made with the interferometer, and the
experimental results for sensitivity are in good agreement with the theoretical values.
The goal of deconvolution microscopy for phase-contrast imaging is to reassign the optical blur to its original position
and to reduce statistical noise, thus visualizing the cellular structures of living cells in three dimensions and at
subresolution scale. The major features of this technology for a phase-contrast microscopy are discussed through a series of theoretical analyses. A few of possible sources of aberrations and image degradation processes are presented. The theoretical and experimental results have shown that deconvolution microscopy can enhance resolution and contrast by either subtracting or reassigning out-of-focus blur.
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