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- ItemNumerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) Maltsi, Anieza; Niermann, Tore; Streckenbach, Timo; Tabelow, Karsten; Koprucki, ThomasWe present a mathematical model and a tool chain for the numerical simulation of TEM images of semiconductor quantum dots (QDs). This includes elasticity theory to obtain the strain profile coupled with the Darwin–Howie–Whelan equations, describing the propagation of the electron wave through the sample. We perform a simulation study on indium gallium arsenide QDs with different shapes and compare the resulting TEM images to experimental ones. This tool chain can be applied to generate a database of simulated TEM images, which is a key element of a novel concept for model-based geometry reconstruction of semiconductor QDs, involving machine learning techniques.
- ItemPatch-Wise Adaptive Weights Smoothing in R(Los Angeles, Calif. : UCLA, Dept. of Statistics, 2020) Polzehl, Jörg; Papafitsoros, Kostas; Tabelow, KarstenImage reconstruction from noisy data has a long history of methodological development and is based on a variety of ideas. In this paper we introduce a new method called patch-wise adaptive smoothing, that extends the propagation-separation approach by using comparisons of local patches of image intensities to define local adaptive weighting schemes for an improved balance of reduced variability and bias in the reconstruction result. We present the implementation of the new method in an R package aws and demonstrate its properties on a number of examples in comparison with other state-of-the art image reconstruction methods. © 2020, American Statistical Association. All rights reserved.
- ItemAxisymmetric diffusion kurtosis imaging with Rician bias correction: A simulation study(New York, NY : Wiley-Liss, 2022) Oeschger, Jan Malte; Tabelow, Karsten; Mohammadi, SiawooshPurpose: To compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC). Methods: Axisymmetric DKI is more robust against noise-induced variation in the measured signal than standard DKI because of its reduced parameter space. However, its susceptibility to Rician noise bias at low signal-to-noise ratios (SNR) is unknown. Here, we investigate two main questions: first, does RBC improve estimation accuracy of axisymmetric DKI?; second, is estimation accuracy of axisymmetric DKI increased compared to standard DKI? Estimation accuracy was investigated on the five axisymmetric DKI tensor metrics (AxTM): the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor, using a noise simulation study based on synthetic data of tissues with varying fiber alignment and in-vivo data focusing on white matter. Results: RBC mainly increased accuracy for the parallel AxTM in tissues with highly to moderately aligned fibers. For the perpendicular AxTM, axisymmetric DKI without RBC performed slightly better than with RBC. However, the combination of axisymmetric DKI with RBC was the overall best performing algorithm across all five AxTM in white matter and axisymmetric DKI itself substantially improved accuracy in axisymmetric tissues with low fiber alignment. Conclusion: Combining axisymmetric DKI with RBC facilitates accurate DKI parameter estimation at unprecedented low SNRs ((Formula presented.)) in white matter, possibly making it a valuable tool for neuroscience and clinical research studies where scan time is a limited resource. The tools used here are available in the open-source ACID toolbox for SPM.
- ItemCorrection to: Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021) Maltsi, Anieza; Niermann, Tore; Streckenbach, Timo; Tabelow, Karsten; Koprucki, ThomasCorrection to: Optical and Quantum Electronics (2020) 52:257 https://doi.org/10.1007/s11082-020-02356-y