Scanning transmission microscopy with a 2 MeV alpha particle microbeam

Bead encoding is a key problem central to all multiplexed bead-based microarrays. Most existing bead-based microarrays require complicated and costly bead fabrication and/or sophisticated bead decoding hardware to achieve high multiplexing. Recently, the development of spatial bead encoding techniques have opened up the possibility of using pattern matching and image processing to develop highly multiplexed, high-throughput bead-based arrays. In this thesis, we improve the existing spatial bead encoding scheme, and develop a pipeline of computational methods, which allows for automated spatial bead encoding. Six novel computational methods, that automate the proposed bead encoding scheme, are developed in this thesis. The proposed scheme improves the previously reported spatial encoding schemes by making them better suited for use in real world scenarios. In the spatial bead encoding scheme, a sequential bead deposition method is used to capture the identities of the beads in bright or dark field images of the array. While, a second fluorescence image of the array is used to quantify the target analyte concentrations associated with the beads. By aligning the two images using the patterns formed by the beads, both the bead identities (i.e. the target analyte associate with the bead) and the analyte concentrations are decoded. The six computational methods developed in this thesis, are grouped into three categories: bright and dark field image processing, fluorescence image processing, and bead pattern matching. These methods are developed such that they require minimal parameter tuning, and are able to deal with noisy images acquired in uncontrolled environments As part of the bright and dark field image processing we have developed two novel methods: a fully automatic method for detecting the underlying micro-well grid structure in the images, and an unsupervised learning based method to classify the micro-wells as either empty or containing a bead. We show the ability of the proposed methods to deal with extreme amounts of noise and distortions using multiple datasets. While, microarray image gridding is a well-studied problem in the context of fluorescence images of planar microarrays, to the best of our knowledge no methods have yet been developed for processing either bright or dark field images of bead-based microarrays. Unlike in bright field images, where all the beads are visible, in fluorescence images only those beads that are expressed in the sample are visible. Therefore, in fluorescence image analysis the first step is to detect the beads in the image. To this end, we have developed an unsupervised, simultaneous bead detection and segmentation method which uses the coherence in shape and size of the beads to circumvent the need for parameter optimization. The main challenge in this segmentation is the large dynamic range across which the beads are expressed. We have also developed a probabilistic non-linear gridding method that attempts to establish the micro-well grid structure from the detected beads. While similar gridding techniques have been developed in the past, we make two contributions in this regard: first, we put the gridding method in a probabilistic framework, and second, we show that in certain cases it is impossible to ascertain if the correct grid has been detected without using additional information about the physical properties of the array. The final step in the proposed bead encoding scheme is to establishing one-to-one correspondences between the beads detected in the bright field and fluorescence images. We have developed two methods for this matching. When the micro-well grid structure in the fluorescence image is successfully established, the grid information can be used to reduce the matching problem to a binary grid alignment problem. A grid matching method that is very fast and can handle a large number of beads (>50,000) has been developed for this purpose. Finally, to perform matching when the fluorescence image grid is not available, a novel point pattern matching method that is affine invariant and robust to small non-linear distortions has been developed. The point pattern matching method addresses the problem of matching large point sets in the presence of large amounts of outliers and occlusion. The proposed method is evaluated using several real and simulated datasets.

The methods used to characterize interfaces from diffraction contrast images are normally based on the contrast exhibited by the edge fringes in these images. In one approach to this problem, Amelinckx et al. have characterized the edge fringes due to a displacement at an interface as being α fringes and the edge fringes which are due to a difference in orientation across the boundary as being δ fringes. The general properties of so called symmetrical δ fringes can be summarized as follows: bright field images are asymmetrical (i.e. one edge fringe is dark while the other is bright) while dark field fringes are symmetrical. Of particular interest in the generation of δ fringes are situations in which the reflections, g and h in Fig. 1b (and therefore 0 and g - h in the same figure) are very close. Under these circumstances both bright and dark field images will be formed with two reflections (either 0 and g-h or g and h) passing through the objective aperture.

X-ray diffraction techniques, including both small- and wide-angle scattering, are valuable for scanning imaging when samples contain molecularly packed structures. These structures offer dark-field imaging contrasts, which are useful for visualizing various biological fibers such as collagens, axons, and muscles. Utilizing these contrasts, tomograms exceeding three dimensions can be reconstructed, exemplified by SAXS tensor tomography. Typically, dark-field imaging requires complementary bright-field imaging for a comprehensive understanding of the materials.Recently, we have developed a beamline, 12-ID-E, for simultaneous SAXS and ptychographic imaging. This instrument not only provides both dark and bright field imaging simultaneously but also captures multi-scale pictures of hierarchical structures. The beamline is equipped with two Pilatus detectors and a hexapod-based ptychography setup. In this presentation, we will detail the setup and showcase scientific examples involving the self-assembly of nanoobjects.

A new LEEM/PEEM apparatus has been designed for the study of surfaces and surface processes in Ultra High Vacuum (UHV). The design follows the general layout of the microscope operated succesfully by Prof. Bauer and coworkers, but differs significantly in a number of details. We designed a magnetic/electrostatic cathode lens with a resolution limit close to that of the uniform electrostatic accelerating field between sample and lens. The projection system consists of transfer lens, intermediate lens and projector lens. Total magnification is between 1,000x and 10,000x, with a field of view between 4 and 40 micrometer. The illuminating system features a continuously adjustable range of illumination, such that the loss of image intensity at high magnification can be balanced by a reduction of the illumiated area, without significant loss of electron beam intensity. Both bright field and dark field imaging are possible. The location of the diffraction aperture in the projection column allows for more flexible operation of the microscope in dark field mode. Care has been taken in the design of the microscope to integrate the stringent vacuum requirements with the geometric constraints of the electron optical components. A sample stage, featuring two orthogonal translations and two (eucentric) tilts, sample heating, as well as in situ sample exchange capability has been constructed. All motions are driven by remote control, UHV compatible piezoelectric actuators, eliminating the need for mechanical feedthroughs, and ensuring a short mechanical path between sample and cathode lens. In situ deposition, using e-beam evaporators and/or Knudsen cells will be possible. A sample preparation chamber contains standard surface cleaning and analytical equipment. We will report on the progress in the construction of this machine.

A multimodal method, which uses different kinds of imaging methods, was applied to extract cell membranes in microscopic images of hematoxylin and eosin-stained hepatic histological sections. Cell membrane extraction in hepatic histological images is difficult because the color difference between the cell membrane and the cytoplasm is small in bright-field images. Three kinds of imaging methods, bright-field, dark-field, and phase-contrast imaging, were used because they are readily available for general pathologists. These imaging methods can be switched easily by revolving a combined condenser with the same phase-contrast objective lens. Therefore, little additional time and cost are needed for this approach. Experimental results show the effectiveness of this approach. The correct rate was improved by using additional color information obtained by dark-field and phase-contrast images compared to conventional bright-field images. The best correct rate was obtained when color information of all three images was used. A graphical user interface to calculate the N/C ratio was developed by combining cell membrane extraction with conventional cell nucleus extraction.

Evaluation of X-ray dark-field imaging in visualization of nearly clinical articular cartilage

Precision in neon ion energy loss measurements with a silicon surface barrier detector

The use of second-harmonic generation (SHG) microscopy in biomedical research is rapidly increasing. This is due in large part to the wide spread interest of using this imaging technique to examine the role of fibrillar collagen organization in diseases such as cancer. The co-examination of SHG images and traditional bright-field (BF) images of hematoxylin and eosin (H&E) stained tissue as a gold standard clinical validation is usually required. However, image registration of these two modalities has been mostly done by manually selecting corresponding landmarks which is labor intensive and error prone. We designed, implemented, and validated the first image intensity-based registration method capable of automatically aligning SHG images and BF images. In our algorithmic approach, a feature extractor is used to pre-process the BF image to block the content features not visible in SHG images and the output image is then aligned with the SHG image by maximizing the common image features. An alignment matrix maximizing the image mutual information is found by evolutionary optimization and the optimization is facilitated using a hierarchical multiresolution framework. The automatic registration results were compared to traditional manual registration to assess the performance of the algorithm. The proposed algorithm has been successfully used in several biomedical studies such as pancreatic and kidney cancer studies and shown great efficacy.

Dark-field microscopy is a powerful technique for enhancing the imaging resolution and contrast of small unstained samples. In this study, we report a method based on end-to-end convolutional neural network to reconstruct high-resolution dark-field images from low-resolution bright-field images. The relation between bright- and dark-field which was difficult to deduce theoretically can be obtained by training the corresponding network. The training data, namely the matched bright- and dark-field images of the same object view, are simultaneously obtained by a special designed multiplexed image system. Since the image registration work which is the key step in data preparation is not needed, the manual error can be largely avoided. After training, a high-resolution numerical dark-field image is generated from a conventional bright-field image as the input of this network. We validated the method by the resolution test target and quantitative analysis of the reconstructed numerical dark-field images of biological tissues. The experimental results show that the proposed learning-based method can realize the conversion from bright-field image to dark-field image, so that can efficiently achieve high-resolution numerical dark-field imaging. The proposed network is universal for different kinds of samples. In addition, we also verify that the proposed method has good anti-noise performance and is not affected by the unstable factors caused by experiment setup.

The ultrastructure of ferritin macromolecules: I. Ultrahigh voltage electron microscopy (1–3 MeV)

Various TEM imaging modes, such as bright-field imaging, STEM and energyfiltering and so on, have been being utilized in electron tomography [1]. In contrast to the above imaging modes, dark-field (DF) TEM imaging is useful for observing crystalline microstructures such as lattice defects, grain boundaries, ordered domain structures, etc. However, it is not generally considered that dark-field (DF) TEM imaging is applicable to electron tomography, since the image contrast is not a simple function of thickness and density. One has to control the diffraction condition precisely to get well-defined DF images, because the image contrast is quite sensitive also to the direction of the incident electron beam. Contrary to the general understanding, we have shown a technique to obtain a DF TEM tilt series and its applicability to electron tomography for crystalline specimens [2]. In this paper, we discuss our latest results of the tomographic DF TEM observation of variant structures in a Ni4Mo ordering alloy from the view point of 3D morphology of different variants.

In-air scanning transmission ion microscopy of cultured cancer cells

Quantification of microstructures is crucial for understanding processing-structure and structure-property relationships in polycrystalline materials. Delineating grain boundaries in bright-field transmission electron micrographs, however, is challenging due to complex diffraction contrast in images. Conventional edge detection algorithms are inadequate; instead, manual tracing is usually required. This study demonstrates the first successful machine learning approach for grain boundary detection in bright-field transmission electron micrographs. The proposed methodology uses a U-Net convolutional neural network trained on carefully constructed data from bright-field images and hand tracings available from prior studies, combined with targeted postprocessing algorithms to preserve fine features of interest. The image processing pipeline accurately estimates grain boundary positions, avoiding segmentation in regions with intragrain contrast and identifying low-contrast boundaries. Our approach is validated by directly comparing microstructural markers (i.e., grain centroids) identified in U-Net predictions with those identified in hand tracings; furthermore, the grain size distributions obtained from the two techniques show notable overlap when compared using t-test, Kolmogorov-Smirnov test, and Cramér-von Mises test. The technique is then successfully applied to interpret new microstructures having different image characteristics from the training data, with preliminary results from platinum and palladium microstructures presented, highlighting the versatility of our approach for grain boundary identification in bright-field micrographs.

Nano-characterization of unusual polarization in electron ceramics

Fe doped ZnO nanowires, nanorods, and urchin-like nanostructures have been grown using an evaporation-deposition method with compacted mixtures of ZnS and Fe2O3 powders, with different Fe contents as precursors. Treatments at 950 °C under argon flow lead to the growth of iron doped nanowires, nanorods, and other nanostructures on the surface of the compacted sample. The incorporation of iron into the nanostructures has been investigated via energy dispersive spectroscopy as well as by cathodoluminescence in a scanning electron microscope and photoluminescence in an optical microscope. The iron content in the structures is limited to the range of 0.5–0.7 at.% and does not depend on the content in the precursor. Bright and dark field imaging and twist contour analysis via transmission electron microscopy support the possibility of a dislocation driven growth of the nanowires.