The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images

Metal-organic Frameworks (MOFs) are a group of crystalline and highly porous materials consisting of inorganic metal ions/clusters (nodes) that are coordinated by organic linkers. The ability to create a wide range of porous structures, where the pore size can be easily changed in size and shape offers the potential for many applications in gas storage/separation and catalysis [1]. The presence of the organic linkers or "struts" in the sample creates challenges for high resolution microscopy as the sample itself is very sensitive to beam damage. A key challenge for understanding the structures of MOFs and how the applications can be modified by doping the nodes and changing the nature of the organic linkers, is therefore to be able to image the samples on the sub-nm length scale (the nodes are ~1 nm) [2].The study of organics, where large single crystals with long-range order cannot be synthesized, is usually performed by either electron crystallography or direct imaging in the (scanning) transmission electron microscope (S/TEM). In the (S)TEM, large single crystals are not needed as the electron beam can be focused to a very small area (sub-nm if needed). The downside to this ability to see small areas is that because the electron beam has a strong interaction with the sample, it can cause significant levels of electron beam damage. However, the last 40 years of protein crystallography and more recently the use of in-situ liquid stages to study chemical reactions in the (S)TEM [3], have shown that this beam damage effect can in most cases be mitigated by the use of extremely low-dose imaging (a dose rate of less than 0.1 electrons/angstrom 2 /s and a cumulative dose of less than 10 electrons/angstrom 2 ). In addition to simply lowering the dose through conventional means (changing the emission current and probe dwell time), more recent use of compressive sensing/in-painting methods for STEM has also been shown to lower the effective dose and dose rate [4]. Figure 1 shows a Z-contrast image obtained from an NU-1000 MOF [2] obtained from a probe corrected FEI 80-300kV Titan operating in the low-dose mode described above. The sample can clearly survive under these conditions long enough to obtain a full scan with a probe size of ~0.1 nm. Results obtained from doped NU-1000 MOF structures (not shown) indicate that the atomic structure of the nodes themselves can be resolved under certain imaging conditions (including the use of in-painting methods). In this presentation the results from these samples will be described in more detail, along with the experimental strategy to obtain higher resolution images in the future [5] [6].

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confidence: 99%

Low-Dose and In-Painting Methods for (Near) Atomic Resolution STEM Imaging of Metal Organic Frameworks (MOFs)

Mehdi¹,

Stevens²,

Moeck

et al. 2017

“…Technological limitations are also associated with obtaining images faster; in the case of the TEM low-dose imaging requirements exceed the framerate of the camera, while in STEM the hysteresis in the scan coils limits the overall scanning speed that can be obtained. To overcome some of these limitations it is possible to use a set of image reconstruction methods such as compressive sensing and in-painting [1,2] to reduce the overall number of readouts/pixels in the TEM/STEM images/movies and lower the overall electron dose while at the same time increasing the speed of the image and reducing the overall size of the dataset acquired [3,4].An example of the use of in-painting to recover pixels in a sub-sampled STEM image is shown in Figure 1. Here the image is sub-sampled after acquisition and recovered to demonstrate that high resolution images typically acquired in a TEM/STEM are over-sampled.…”

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confidence: 99%

Implementing Sub-sampling Methods for Low-Dose (Scanning) Transmission Electron Microscopy (S/TEM)

Browning

Stevens²,

Kovařík³

et al. 2017

Self Cite

In many practical applications of high resolution (scanning) transmission electron microscopy, the resolution obtainable in an image is determined solely by the stability of the sample. This is particularly true in the era of aberration corrected S/TEM where the dose on the sample for the highest resolution images can easily exceed 10 5 electrons/Å 2 during routine operation. This dose sensitivity presents extreme constraints on applications such as 3-D imaging, spectroscopic imaging and in-situ experiments, where prolonged exposure to the beam can result in damage occurring before the experiment can be completed. To ensure that observations are free from damage artifacts, it is therefore essential that images are acquired under conditions where the maximum information can be extracted from the minimum amount of electron dose.Traditionally low-dose imaging is performed by either acquiring images faster or changing the emission conditions of the gun. However, when there is a change in the emission the microscope alignment changes and it is not always possible to run the automated corrector alignments in low-dose mode to obtain the highest resolution images. Technological limitations are also associated with obtaining images faster; in the case of the TEM low-dose imaging requirements exceed the framerate of the camera, while in STEM the hysteresis in the scan coils limits the overall scanning speed that can be obtained. To overcome some of these limitations it is possible to use a set of image reconstruction methods such as compressive sensing and in-painting [1,2] to reduce the overall number of readouts/pixels in the TEM/STEM images/movies and lower the overall electron dose while at the same time increasing the speed of the image and reducing the overall size of the dataset acquired [3,4].An example of the use of in-painting to recover pixels in a sub-sampled STEM image is shown in Figure 1. Here the image is sub-sampled after acquisition and recovered to demonstrate that high resolution images typically acquired in a TEM/STEM are over-sampled. Of course, such post-acquisition sub sampling does not decrease the dose on the sample. Setting up the STEM to acquire sub-sampled images directly allows an immediate benefit in terms of the number of pixels sampled (dose, speed and data transfer). Figure 2 shows an image of CaCo3 acquired using a sub-sampling STEM mode of operation [5]. Here the scan proceeds through a pixel hopping mechanism so that a small fraction of the pixels is actually acquired (this also benefits from an increase in speed of acquisition by reducing the number of scan flyback/stabilization procedures during the scan). In this presentation, the use of compressive sensing and in-painting methods to increase the flexibility and speed while decreasing the dose of all modes of TEM/STEM imaging and spectroscopy will be described. The major limitations and the potential benefits of active learning for the acquisition of dynamic movies at vastly increased framerates during in-situ measurements will als...

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confidence: 99%

Less is More: Bigger Data from Compressive Measurements

Stevens

Browning

2017

Self Cite

Compressive sensing approaches are beginning to take hold in (scanning) transmission electron microscopy (S/TEM) [1,2,3]. Compressive sensing is a mathematical theory about acquiring signals in a compressed form (measurements) and the probability of recovering the original signal by solving an inverse problem [4]. The inverse problem is underdetermined (more unknowns than measurements), so it is not obvious that recovery is possible. Compression is achieved by taking inner products of the signal with measurement weight vectors. Both Gaussian random weights and Bernoulli (0,1) random weights form a large class of measurement vectors for which recovery is possible. The measurements can also be designed through an optimization process. The key insight for electron microscopists is that compressive sensing can be used to increase acquisition speed and reduce dose.Building on work initially developed for optical cameras, this new paradigm will allow electron microscopists to solve more problems in the engineering and life sciences. We will be collecting orders of magnitude more data than previously possible. The reason that we will have more data is because we will have increased temporal/spatial/spectral sampling rates, and we will be able to interrogate larger classes of samples that were previously too beam sensitive to survive the experiment. For example consider an in-situ experiment that takes 1 minute. With traditional sensing, we might collect 5 images per second for a total of 300 images. With compressive sensing, each of those 300 images can be expanded into 10 more images, making the collection rate 50 images per second, and the decompressed data a total of 3000 images [3].But, what are the implications, in terms of data, for this new methodology? Acquisition of compressed data will require downstream reconstruction to be useful. The reconstructed data will be much larger than traditional data, we will need space to store the reconstructions during analysis, and the computational demands for analysis will be higher. Moreover, there will be time costs associated with reconstruction.Deep learning [5] is an approach to address these problems. Deep learning is a hierarchical approach to find useful (for a particular task) representations of data. Each layer of the hierarchy is intended to represent higher levels of abstraction. For example, a deep model of faces might have sinusoids, edges and gradients in the first layer; eyes, noses, and mouths in the second layer, and faces in the third layer. There has been significant effort recently in deep learning algorithms for tasks beyond image classification such as compressive reconstruction [6] and image segmentation [7]. A drawback of deep learning, however, is that training the model requires large datasets and dedicated computational resources (to reduce training time to a few days). A second issue is that deep learning is not userfriendly and the meaning behind the results is usually not interpretable. We have shown it is possible to reduce the data set size ...