Shape from Projections via Differentiable Forward Projector for Computed Tomography

In computed tomography, the reconstruction is typically obtained on a voxel grid. In this work, however, we propose a mesh-based reconstruction method. For tomographic problems, 3D meshes have mostly been studied to simulate data acquisition, but not for reconstruction, for which a 3D mesh means the inverse process of estimating shapes from projections. In this paper, we propose a differentiable forward model for 3D meshes that bridge the gap between the forward model for 3D surfaces and optimization. We view the forward projection as a rendering process, and make it differentiable by extending recent work in differentiable rendering. We use the proposed forward model to reconstruct 3D shapes directly from projections. Experimental results for single-object problems show that the proposed method outperforms traditional voxel-based methods on noisy simulated data. We also apply the proposed method on electron tomography images of nanoparticles to demonstrate the applicability of the method on real data

Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions

X-ray computed tomography scans can provide detailed information about the state of the material after manufacture and in service. X-ray computed tomography aided engineering (XAE) was recently introduced as an automated process to transfer 3D image data to finite element models. The implementation of a structure tensor code for material orientation analysis in combination with a newly developed integration point-wise fibre orientation mapping allows an easy applicable, computationally cheap, fast, and accurate model set-up. The robustness of the proposed approach is demonstrated on a non-crimp fabric glass fibre reinforced composite for a low resolution case with a voxel size of 64 μm corresponding to more than three times the fibre diameter. Even though 99.8% of the original image data is removed, the simulated elastic modulus of the considered non-crimp fabric composite is only underestimated by 4.7% compared to the simulation result based on the original high resolution scan

Psycho-social factors associated with mental resilience in the Corona lockdown.

The SARS-CoV-2 pandemic is not only a threat to physical health but is also having severe impacts on mental health. Although increases in stress-related symptomatology and other adverse psycho-social outcomes, as well as their most important risk factors have been described, hardly anything is known about potential protective factors. Resilience refers to the maintenance of mental health despite adversity. To gain mechanistic insights about the relationship between described psycho-social resilience factors and resilience specifically in the current crisis, we assessed resilience factors, exposure to Corona crisis-specific and general stressors, as well as internalizing symptoms in a cross-sectional online survey conducted in 24 languages during the most intense phase of the lockdown in Europe (22 March to 19 April) in a convenience sample of N = 15,970 adults. Resilience, as an outcome, was conceptualized as good mental health despite stressor exposure and measured as the inverse residual between actual and predicted symptom total score. Preregistered hypotheses (osf.io/r6btn) were tested with multiple regression models and mediation analyses. Results confirmed our primary hypothesis that positive appraisal style (PAS) is positively associated with resilience (p < 0.0001). The resilience factor PAS also partly mediated the positive association between perceived social support and resilience, and its association with resilience was in turn partly mediated by the ability to easily recover from stress (both p < 0.0001). In comparison with other resilience factors, good stress response recovery and positive appraisal specifically of the consequences of the Corona crisis were the strongest factors. Preregistered exploratory subgroup analyses (osf.io/thka9) showed that all tested resilience factors generalize across major socio-demographic categories. This research identifies modifiable protective factors that can be targeted by public mental health efforts in this and in future pandemics

Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision

Minimum cut / maximum flow (min-cut/max-flow) algorithms are used to solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. This makes it difficult to choose an optimal algorithm for a given problem - especially for parallel algorithms, which have not been thoroughly compared. In this paper, we review the state-of-the-art min-cut/max-flow algorithms for unstructured graphs in computer vision. We evaluate run time performance and memory use of various implementations of both serial and parallel algorithms on a set of graph cut problems. Our results show that the Hochbaum pseudoflow algorithm is the fastest serial algorithm closely followed by the Excesses Incremental Breadth First Search algorithm, while the Boykov-Kolmogorov algorithm is the most memory efficient. The best parallel algorithm is the adaptive bottom-up merging approach by Liu and Sun. Additionally, we show significant variations in performance between different implementations the same algorithms highlighting the importance of low-level implementation details. Finally, we note that existing parallel min-cut/max-flow algorithms can significantly outperform serial algorithms on large problems but suffers from added overhead on small to medium problems. Implementations of all algorithms are available at https://github.com/patmjen/maxflow_algorithmsComment: 15 pages, 8 figures, submitted for revie

Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites

SEM data sets, Matlab codes, and output from the local fiber volume fraction analysis from the publications: Mortensen, U.A., Rasmussen, S., Mikkelsen, L.P., Fraisse, A., Andersen, T.L. The impact of the fibre volume fraction on the fatigue performance of glass fiber composites used in the wind turbine industry, Composites Part A, submitted Dec 2022. Mikkelsen, Lars P., Fæster, S., Dahl, V.A. Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites. Data in Brief, Submitted, 2023. to where a reference should be given. The files are structured in the following way. L1, L2: Low FVF cases 1 and 2; see the publication. H1, H2: High FVF case 1 and 2; see the publication. SEM images: *.bmp: Single SEM scan *.bmp.hdr: settings for single SEM scan *.tif: Stitched SEM scan *.tif.hdr: settings for stitched SEM scan *_BundleAnalysis.m: Script for manual segmentation of the individual bundles. *.zip files: Functions used by the ..._BundleAnalysis.m scripts *-CURVES_AND_AREAS.mat: Matlab saving of bundle definitions used by the ...BUndlesAnalysis.m script *BWbundles.mat: Output from the _BundleAnalysis.m script *.m: Local fiber volume fraction analysis script of SEM-scan based on output from BundleAnalysis tool Figxx.png: Reference to specific figures in the publications shown for all 4 cases Fig7.m: Matlab script for plotting figure