8,491 research outputs found
Capturing Nucleation at 4D Atomic Resolution
Nucleation plays a critical role in many physical and biological phenomena
ranging from crystallization, melting and evaporation to the formation of
clouds and the initiation of neurodegenerative diseases. However, nucleation is
a challenging process to study in experiments especially in the early stage
when several atoms/molecules start to form a new phase from its parent phase.
Here, we advance atomic electron tomography to study early stage nucleation at
4D atomic resolution. Using FePt nanoparticles as a model system, we reveal
that early stage nuclei are irregularly shaped, each has a core of one to few
atoms with the maximum order parameter, and the order parameter gradient points
from the core to the boundary of the nucleus. We capture the structure and
dynamics of the same nuclei undergoing growth, fluctuation, dissolution,
merging and/or division, which are regulated by the order parameter
distribution and its gradient. These experimental observations differ from
classical nucleation theory (CNT) and to explain them we propose the order
parameter gradient (OPG) model. We show the OPG model generalizes CNT and
energetically favours diffuse interfaces for small nuclei and sharp interfaces
for large nuclei. We further corroborate this model using molecular dynamics
simulations of heterogeneous and homogeneous nucleation in liquid-solid phase
transitions of Pt. We anticipate that the OPG model is applicable to different
nucleation processes and our experimental method opens the door to study the
structure and dynamics of materials with 4D atomic resolution.Comment: 42 pages, 5 figures, 12 supplementary figures and one supplementary
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Non-convex image reconstruction via Expectation Propagation
Tomographic image reconstruction can be mapped to a problem of finding
solutions to a large system of linear equations which maximize a function that
includes \textit{a priori} knowledge regarding features of typical images such
as smoothness or sharpness. This maximization can be performed with standard
local optimization tools when the function is concave, but it is generally
intractable for realistic priors, which are non-concave. We introduce a new
method to reconstruct images obtained from Radon projections by using
Expectation Propagation, which allows us to reframe the problem from an
Bayesian inference perspective. We show, by means of extensive simulations,
that, compared to state-of-the-art algorithms for this task, Expectation
Propagation paired with very simple but non log-concave priors, is often able
to reconstruct images up to a smaller error while using a lower amount of
information per pixel. We provide estimates for the critical rate of
information per pixel above which recovery is error-free by means of
simulations on ensembles of phantom and real images.Comment: 12 pages, 6 figure
Robustness and Accuracy of Feature-Based Single Image 2-Dâ3-D Registration Without Correspondences for Image-Guided Intervention
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OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios
Recently, linear computed tomography (LCT) systems have actively attracted
attention. To weaken projection truncation and image the region of interest
(ROI) for LCT, the backprojection filtration (BPF) algorithm is an effective
solution. However, in BPF for LCT, it is difficult to achieve stable interior
reconstruction, and for differentiated backprojection (DBP) images of LCT,
multiple rotation-finite inversion of Hilbert transform (Hilbert
filtering)-inverse rotation operations will blur the image. To satisfy multiple
reconstruction scenarios for LCT, including interior ROI, complete object, and
exterior region beyond field-of-view (FOV), and avoid the rotation operations
of Hilbert filtering, we propose two types of reconstruction architectures. The
first overlays multiple DBP images to obtain a complete DBP image, then uses a
network to learn the overlying Hilbert filtering function, referred to as the
Overlay-Single Network (OSNet). The second uses multiple networks to train
different directional Hilbert filtering models for DBP images of multiple
linear scannings, respectively, and then overlays the reconstructed results,
i.e., Multiple Networks Overlaying (MNetO). In two architectures, we introduce
a Swin Transformer (ST) block to the generator of pix2pixGAN to extract both
local and global features from DBP images at the same time. We investigate two
architectures from different networks, FOV sizes, pixel sizes, number of
projections, geometric magnification, and processing time. Experimental results
show that two architectures can both recover images. OSNet outperforms BPF in
various scenarios. For the different networks, ST-pix2pixGAN is superior to
pix2pixGAN and CycleGAN. MNetO exhibits a few artifacts due to the differences
among the multiple models, but any one of its models is suitable for imaging
the exterior edge in a certain direction.Comment: 13 pages, 13 figure
Reconstruction of coronary arteries from X-ray angiography: A review.
Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research
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