Forty-fifth Annual Report for the Fiscal Year Ending June 30, 1968

Binary tomography is concerned with the recovery of binary images from a few of their projections (i.e., sums of the pixel values along various directions). To reconstruct an image from noisy projection data, one can pose it as a constrained least-squares problem. As the constraints are nonconvex, many approaches for solving it rely on either relaxing the constraints or heuristics. In this paper, we propose a novel convex formulation, based on the Lagrange dual of the constrained least-squares problem. The resulting problem is a generalized least absolute shrinkage and selection operator problem, which can be solved efficiently. It is a relaxation in the sense that it can only be guaranteed to give a feasible solution, not necessarily the optimal one. In exhaustive experiments on small images (2 × 2, 3 × 3, 4 × 4), we find, however, that if the problem has a unique solution, our dual approach finds it. In the case of multiple solutions, our approach finds the commonalities between the solutions. Further experiments on realistic numerical phantoms and an experiment on the X-ray dataset show that our method compares favorably to Total Variation and DART

Single-shot tomography of discrete dynamic objects

This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time. Addressing the challenge of limited measurements per time point, we propose a technique that synergistically incorporates spatial and temporal information of the dynamic objects. This is achieved through the application of the level-set method for image segmentation and the representation of motion via a sinusoidal basis. The result is a computationally efficient and easily optimizable variational framework that enables the reconstruction of high-quality 2D or 3D image sequences with a single projection per frame. Compared to current methods, our proposed approach demonstrates superior performance on both synthetic and pseudo-dynamic real X-ray tomography datasets. The implications of this research extend to improved visualization and analysis of dynamic processes in tomographic imaging, finding potential applications in diverse scientific and industrial domains

Full-waveform inversion with Mumford-Shah regularization

Full-waveform inversion (FWI) is a non-linear procedure to estimate subsurface rock parameters from surface measurements of induced seismic waves. This procedure is ill-posed in nature and hence, requires regularization to enhance some structure depending on the prior information. Recently, Total-Variation (TV) regularization has gained popularity due to its ability to produce blocky structures. Contrary to this, the earth behaves more like a piecewise smooth function. TV regularization fails to enforce this prior information into FWI. We propose a Mumford-Shah functional to incorporate the piecewise smooth spatial structure in the FWI procedure. The resulting optimization problem is solved by a splitting method. We show the improvement in results against TV regularization on two synthetic camembert examples

mzeegers/ADJUST: ADJUST v1.0.2

Minor release for Zenodo archiving. Code for ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography For a full description, please visit https://github.com/mzeegers/ADJUS

Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D

Electron tomography is a widely used technique for 3D structural analysis of nanomaterials, but it can cause damage to samples due to high electron doses and long exposure times. To minimize such damage, researchers often reduce beam exposure by acquiring fewer projections through tilt undersampling. However, this approach can also introduce reconstruction artifacts due to insufficient sampling. Therefore, it is important to determine the optimal number of projections that minimizes both beam exposure and undersampling artifacts for accurate reconstructions of beam-sensitive samples. Current methods for determining this optimal number of projections involve acquiring and post-processing multiple reconstructions with different numbers of projections, which can be time-consuming and requires multiple samples due to sample damage. To improve this process, we propose a protocol that combines golden ratio scanning and quasi-3D reconstruction to estimate the optimal number of projections in real-time during a single acquisition. This protocol was validated using simulated and realistic nanoparticles, and was successfully applied to reconstruct two beam-sensitive metal-organic framework complexes

Optimized 3D reconstruction of large, compact assemblies of metallic nanoparticles

3D characterization of assemblies of nanoparticles is of great importance to determine their structure-property connection. Such investigations become increasingly more challenging when the assemblies become larger and more compact. In this paper, we propose an optimized approach for electron tomography to minimize artifacts related to beam broadening in high angle annular dark-field scanning transmission electron microscopy mode. These artifacts are typically present at one side of the reconstructed 3D data set for thick nanoparticle assemblies. To overcome this problem, we propose a procedure in which two tomographic tilt series of the same sample are acquired. After acquiring the first series, the sample is flipped over 180°, and a second tilt series is acquired. By merging the two reconstructions, blurring in the reconstructed volume is minimized. Next, this approach is combined with an advanced three-dimensional reconstruction algorithm yielding quantitative structural information. Here, the approach is applied to a thick and compact assembly of spherical Au nanoparticles, but the methodology can we used to investigate a broad range of samples

Quantitative 3D investigation of nanoparticle assemblies by volumetric segmentation of electron tomography data sets

Morphological characterization of nanoparticle assemblies and hybrid nanomaterials is critical in determining their structure-property relationships as well as in the development of structures with desired properties. Electron tomography has become a widely utilized technique for the three-dimensional characterization of nanoparticle assemblies. However, the extraction of quantitative morphological parameters from the reconstructed volume can be a complex and labor-intensive task. In this study, we aim to overcome this challenge by automating the volumetric segmentation process applied to three-dimensional reconstructions of nanoparticle assemblies. The key to enabling automated characterization is to assess the performance of different volumetric segmentation methods in accurately extracting predefined quantitative descriptors for morphological characterization. In our methodology, we compare the quantitative descriptors obtained through manual segmentation with those obtained through automated segmentation methods, to evaluate their accuracy and effectiveness. To show generality, our study focuses on the characterization of assemblies of CdSe/CdS quantum dots, gold nanospheres and CdSe/CdS encapsulated in polymeric micelles, and silica-coated gold nanorods decorated with both CdSe/CdS or PbS quantum dots. We use two unsupervised segmentation algorithms: the watershed transform and the spherical Hough transform. Our results demonstrate that the choice of automated segmentation method is crucial for accurately extracting the predefined quantitative descriptors. Specifically, the spherical Hough transform exhibits superior performance in accurately extracting quantitative descriptors, such as particle size and interparticle distance, thereby allowing for an objective, efficient, and reliable volumetric segmentation of complex nanoparticle assemblies

3D characterization of the structural transformation undergone by Cu@Ag Core-Shell Nanoparticles following CO2 reduction reaction

The increasing use of metallic nanoparticles (NPs) is significantly advancing the field of electrocatalysis. In particular, Cu/Ag bimetallic interfaces are widely used to enhance the electrochemical CO2 reduction reaction (eCO2RR) toward CO and, more recently, C2 products. However, drastic changes in the product distribution and performance when Cu@Ag core-shell configurations are used can often be observed under electrochemical reaction conditions, especially during the first few minutes of the reaction. Possible structural changes that generate these observations remain underexplored; therefore, the structure-property relationship is hardly understood. In this study, we use electron tomography to investigate the structural transformation mechanism of Cu@Ag core-shells NPs during the critical first minutes of the eCO2RR. In this manner, we found that the crystallinity of the Cu seed determines whether the formation of a complete and homogeneous Ag shell is possible. Moreover, by tracking the particles’ transformations, we conclude that modifications of the Cu-Ag interface and Cu2O enrichment at the surface of the NPs are key factors contributing to the product generation changes. These insights provide a better understanding of how bimetallic core-shell NPs transform under electrochemical conditions

Low-Dose 4D-STEM tomography for beam-sensitive nanocomposites

Electron tomography is essential for investigating the three-dimensional (3D) structure of nanomaterials. However, many of these materials, such as metal-organic frameworks (MOFs), are extremely sensitive to electron radiation, making it difficult to acquire a series of projection images for electron tomography without inducing electron-beam damage. Another significant challenge is the high contrast in high-angle annular dark field scanning transmission electron microscopy that can be expected for nanocomposites composed of a metal nanoparticle and an MOF. This strong contrast leads to so-called metal artifacts in the 3D reconstruction. To overcome these limitations, we here present low-dose electron tomography based on four-dimensional scanning transmission electron microscopy (4D-STEM) data sets, collected using an ultrafast and highly sensitive direct electron detector. As a proof of concept, we demonstrate the applicability of the method for an Au nanostar embedded in a ZIF-8 MOF, which is of great interest for applications in various fields, including drug delivery

Quantitative 3D structural analysis of small colloidal assemblies under native conditions by liquid-cell fast electron tomography

Electron tomography has become a commonly used tool to investigate the three-dimensional (3D) structure of nanomaterials, including colloidal nanoparticle assemblies. However, electron microscopy is typically done under high-vacuum conditions, requiring sample preparation for assemblies obtained by wet colloid chemistry methods. This involves solvent evaporation and deposition on a solid support, which consistently alters the nanoparticle organization. Here, we suggest using electron tomography to study nanoparticle assemblies in their original colloidal liquid environment. To address the challenges related to electron tomography in liquid, we devise a method that combines fast data acquisition in a commercial liquid-cell with a dedicated alignment and reconstruction workflow. We present the advantages of this methodology in accurately characterizing two different systems. 3D reconstructions of assemblies comprising polystyrene-capped Au nanoparticles encapsulated in polymeric shells reveal less compact and more distorted configurations for experiments performed in a liquid medium compared to their dried counterparts. A similar expansion can be observed in quantitative analysis of the surface-to-surface distances of self-assembled Au nanorods in water rather than in a vacuum, in agreement with bulk measurements. This study, therefore, emphasizes the importance of developing high-resolution characterization tools that preserve the native environment of colloidal nanostructures