This project is concerned with biomedical images often with applications to Magnetic Resonance Imaging (MRI) problem, ranging from functional, diffusionweighted, quantitative MRI to image reconstruction in Magnetic Resonance Fingerprinting. The methods developed in this project arise from different areas of mathematics specifically from nonparametric statistics and nonsmooth variational methods.
Structural adaptive smoothing methods for imaging problems
Images are often characterized by qualitative properties of their spatial structure, e.g. spatially extended regions of homogeneity that are separated by discontinuities. Images or image data with such a property are the target of the methods considered in this research. The methods summarized under the term structural adaptive smoothing try to employ a qualitative assumption on the spatial structure of the data. This assumption is used to simultaneously describe the structure and efficiently estimate parameters like image intensities. Structural adaptive smoothing generalizes several concepts in nonparametric regression. The methods are designed to provide intrinsic balance between variability and bias of the reconstruction results.
A first attempt to use the idea of structural adaptive smoothing for image processing was proposed in Polzehl and Spokoiny (2000) under the name adaptive weights smoothing. This was generalized and refined especially in Polzehl and Spokoiny (2006) providing a theory for the case of oneparameter exponential families. This has become known under the name propagationseparation approach. Several extentions have been made to cover locally smooth images, color images (Polzehl and Tabelow, 2007) and applications from the neurosciences like functional Magnetic Resonance Imaging (fMRI) and diffusionweighted Magnetic Resonance Imaging (dMRI).
Nonsmooth Variational Models for Medical Image Reconstruction
Nonsmooth variational (energy minimizing) models form a powerful tool for addressing inverse medical imaging problems. The idea is to obtain the image reconstruction by inverting the operator which models a given medical imaging modality in a stable and robust way. Examples of such operators are the Radon transform for Positron Emission Tomography (PET) and a subsampling operator of a signal's Fourier coefficients for Magnetic Resonance Imaging (MRI). This inversion is typically an illposed problem and it is further complicated by the presence of random noise. Therefore, a Tikhonov regularization approach is employed where some a priori information in encoded in the regularization term. For a stable inversion, nonsmooth regularisers, e.g. of Total Variation (TV) type, are popular due to their ablilty to preserve prominent features in the reconstruction (edges).
There are several challenges that need to be resolved:
 The choice of the right regularizer is crucial as this is closely linked to exploiting the a priori information in a meaningful way.
 The choice and adaptation of the regularization weight must be finely tuned. This determines the amount of regularization (filtering) that is applied locally.
 An analysis in function space is of high importance. This provides an understanding of the regularizing mechanisms independently of discretization artifacts while it sets up the framework for the design of solution algorithms in function space which exhibit resolution independent convergence behaviour.
Adaptive noise reduction and signal detection in fMRI
In a series of publications we develop dedicated methods for noise reduction and signal inference in functional MRI based on the propagationseparation approach: In Tabelow et al. 2006 we proposed a new adaptive method for noise reduction of the statistical parametric map in a singlesubject fMRI dataset. The properties of this map after smoothing allow for the application of Random Field theory for signal detection. We demonstrated that the method is able to recover the signaltonoise loss when increasing the spatial resolution of the MRI acquisition (Tabelow et al. 2009) and its applicability for presurgical planning (Tabelow et al. 2008). Later, we were able to include the signal detection into a coherent statistical framework for adaptive fMRI analysis in a structural adaptive segmentation method (Polzehl et al. 2010), see Figure 1.
All adaptive methods for fMRI are implemented in the R software environment for statistical computing and graphics as a free contributed package fmri. It can be downloaded from the CRAN server. It is also listed at NITRC and part of the WIAS R packages for neuroimaging. The structural adaptive segmentation algorithm is available as Adaptive Smoothing Plugin for the neuroimaging software BrainVoyager QX.
Functional connectivity
coming soon
Analysis of diffusionweighted MRI data
The signal attenuation by the diffusion weighting in dMRI makes this imaging modality vulnerable to noise. We developed a structural adaptive smoothing method for Diffusion Tensor Imaging data (Tabelow et al. 2008; Polzehl and Tabelow 2009). The method uses local comparisons of the estimated diffusion tensor to define the local homogeneity regions for the propagationseparation approach. We developed a positionorientation adaptive smoothing algorithm for denoising of diffusionweigthed MR data (POAS). This algorithm works in the orientation space of the measurement and does not refer to a model for the spherical distribution of the data like the diffusion tensor (Becker et. al 2012). Recently, the method could be extended for multishell dMRI data as multishell POAS (msPOAS) (Becker et al. 2014), see Figure 2.
All adaptive methods for dMRI are implemented in the R software environment for statistical computing and graphics as a free contributed package dti. It can be downloaded from the CRAN server. It is also listed at NITRC and part of the WIAS R packages for neuroimaging. The msPOAS method is also implemented in Matlab as part on the ACIDToolbox for SPM (Tabelow et al. 2015). The method has shown to be an essential part of an improved processing pipeline for Diffusion Kurtosis Imaging (DKI) in Mohammadi et al. 2015.
The application of many processing methods to neuroimaging data, like the denoising method msPOAS, requires knowledge on the (local) noise level in the data. In Tabelow et al. 2015 we provide a new method LANE for the corresponding estimation problem. The knowledge about the local noise level then also allows for the characterization of the estimation bias in local diffusion models (Polzehl and Tabelow 2016), which is in particular important at low SNR.
The R package dti is capable of performing a full analysis of dMRI data and implements a large number of diffusion models for the data, e.g. the DTI model, the diffusion kurtosis model (DKI), and the orientation distribution function. We proposed a computationally feasible and interpretable tensor mixture model for the modelling of dMRI data (Tabelow et al. 2012).
The importance of adequate processing of neuroimaging data for diagnostic sensitivity became obvious in two publications together with colleagues from Universitätsklinikum Münster concerning multiple sclerosis (Deppe et al. 2016) and EHEC (Krämer et al. 2015).
Quantitative MRI with Multiparamter Mapping
coming soon
Bilevel optimization in medical imaging
In a series of papers (Hintermüller and Rautenberg 2017, Hintermüller et al. 2017, Hintermüller, Papafitsoros, and Rautenberg 2017, Hintermüller et al. 2017b), a new generalized formulation of the renowned total variation regularization was introduced and analyzed. The new regularization functional incorporates a distributed weight function which allows for a varying filter effect depending on local image features (details vs. homogeneous regions). The filter weight is determined automatically through a novel bilevel optimization framework (Hintermüller and Rautenberg 2017, Hintermüller et al. 2017). In this context, the parameterized image reconstruction problem represents the lower level problem, and the upper level problem aims at fixing the filter weight properly. Inspired by an unsupervised learning approach and the statistics of extremes, a localized variance corridor for the image residual is considered and its violation provides a suitable choice for the upper level objective. In the case of Parseval frames, an algorithmic alternative was discussed in Hintermüller et al. 2017b, where instead of the regularizing term, the data fidelity term was weighted and the algorithmic optimization was performed with a surrogate technique. Finally, a detailed study concerning analytical properties of the novel total variation generalization was presented in Hintermüller, Papafitsoros, and Rautenberg 2017.
Structural TV priors in function space
During the recent years, total variationtype functionals, which exploit structural similarity of the reconstruction to some a priori known information, have become increasingly popular. They typically incorporate gradient information in a pointwise fashion. These techniques are particularly relevant in multimodal medical imaging, where for instance, information from one modality e.g., MRI, can be exploited in the reconstruction process of another modality, e.g., PET. In a recently completed project (Hintermüller, Holler, and Papafitsoros 2017) we introduced and analyzed a function space framework for a large class of such structural total variation functionals that are typically used in the above context. This is particularly important, since in function space there is a thorough mathematical description of prominent image features, e.g., edges, which are modelled as discontinuities of functions that typically belong to the space of functions of bounded variation. We defined the structural TV functionals in function space, as appropriate relaxations (lower semicontinuous envelopes). We proved that these relaxations can have a precise integral representation only in certain restrictive cases. However we showed through a general duality result, that formulation of the Tikhonov regularisation problem in function space can still be understood via its equivalence to a corresponding saddlepoint formulation, where no knowledge of the precise formulation of the relaxation is needed. Thus, our work allows the function space formulation of a wide class of multimodal medical imaging problems, for instance MRguided PET reconstruction:
Mathematical framework for Magnetic Resonance Fingerprinting
The ECMath CH12 project Advanced Magnetic Resonance Imaging: Fingerprinting and Geometric Quantication, is focused on the precise mathematization and incorporation of analytical techniques which target to improve modern medical imaging modalities. We are currently developing a mathematical model for Magnetic Resonance Fingerprinting (MRF). MRF is a promising, recently introduced MRI acquisition scheme which allows the simultaneous quantification of the tissue parameters (e.g. T_{1}, T_{2} and others) using a single acquisition process. The procedure can be summarised as follows: The tissue of interest is excited through a random sequence of pulses without needing to wait for the system to return to equilibrium between pulses. After each pulse, a subset of the signal's Fourier coefficients is collected, as in classical MRI, and a reconstruction of the magnetisation image is performed. These reconstructions suffer from the presence of artefacts since the Fourier coefficients are not fully sampled. The formed sequence of image elements is then compared to a family of predicted sequences (dictionary of fingerprints) each of which corresponds to a specific combination of values of the tissue parameters. This dictionary is computed beforehand by solving the Bloch equations which characterise the dynamics of the magnetization. The idea is that, provided the dictionary is rich enough, every material element (voxel) can be then mapped to its parameter values. This projects focuses on:
 Mathematical characterization of the MRF process by performing analysis in function space.
 Further improvement of the quality of the reconstruction of the parameter maps by employing modern nonsmooth regularization techniques in the MRF process, see preliminary results in Figure 5.
 Development of efficient reconstruction algorithms for MRF.
Highlights
Many of the image processing tools especially in the context of neuroimaging are developed in the MATHEON project F10 "Image and signal processing in the biomedical sciences".
Methods developed in this project have been successfully applied in several highranked papers, e.g.:
 Functional MRI of the zebra finch brain during song stimulation suggests a lateralized response topography (Voss et al., PNAS, 2007)
 Dissociations between behavioral and fMRIbased evaluations of cognitive function after brain injury (Bardin et al., Brain, 2011)
The research activity of many international groups with respect to R and Medical Imaging has been recently summarized in a Special Volume of the Journal of Statistical Software "Magnetic Resonance Imaging in R" vol. 44 (2011) edited by K. Tabelow and B. Whitcher, see also Tabelow et al. 2011.
Software has been developed within the framework of the R Environment for Statistical Computing:
 adimpro  Adaptive Smoothing of Digital Images
 dti  DTI/DWI Analysis
 fmri  Analysis of fMRI Experiments
Further software packages and plugins are
 ACIDToolbox for Artifact Correction in dMRI for SPM
 aws4SPM  toolbox for SPM
 AWS for AMIRA  plugin for AMIRA (TM)
 Adaptive smoothing plugin for BrainVoyager QX
Publications
Monographs

F. Stonyakin, D. Dvinskikh, P. Dvurechensky, A. Kroshnin, O. Kuznetsova, A. Agafonov, A. Gasnikov, A. Tyurin, C. Uribe, D. Pasechnyuk, S. Artamonov, Gradient methods for problems with inexact model of the objective, M. Khachay, Y. Kochetov, P. Pardalos, eds., Mathematical Optimization Theory and Operations Research, Springer International Publishing AG, Cham, Switzerland, 2019, pp. 97114, (Chapter Published), DOI 10.1007/9783030226299_8 .

M. Hintermüller, K. Papafitsoros, Chapter 11: Generating Structured Nonsmooth Priors and Associated Primaldual Methods, in: Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, R. Kimmel, X.Ch. Tai, eds., 20 of Handbook of Numerical Analysis, Elsevier, 2019, pp. 437502, (Chapter Published), DOI 10.1016/bs.hna.2019.08.001 .

J. Polzehl, K. Tabelow, Magnetic resonance brain imaging: Modeling and data analysis using R, Use R!, Springer Nature, Cham, 2019, 231 pages, (Monograph Published), DOI 10.1007/9783030291846 .
Abstract
This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. The data processing pipelines described rely on R. The book is intended for readers from two communities: Statisticians who are interested in neuroimaging and looking for an introduction to the acquired data and typical scientific problems in the field; and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. Offering a practical introduction to the field, the book focuses on those problems in data analysis for which implementations within R are available. It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts. The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and MultiParameter Mapping. The book concludes with extended appendices providing details of the nonparametric statistics used and the resources for R and MRI data.The book also addresses the issues of reproducibility and topics like data organization and description, as well as open data and open science. It relies solely on a dynamic report generation with knitr and uses neuroimaging data publicly available in data repositories. The PDF was created executing the R code in the chunks and then running LaTeX, which means that almost all figures, numbers, and results were generated while producing the PDF from the sources. 
J. Polzehl, K. Tabelow, Chapter 4: Structural Adaptive Smoothing: Principles and Applications in Imaging, in: Mathematical Methods for Signal and Image Analysis and Representation, L. Florack, R. Duits, G. Jongbloed, M.C. VAN Lieshout, L. Davies, eds., 41 of Computational Imaging and Vision, Springer, London et al., 2012, pp. 6581, (Chapter Published).

K. Tabelow, B. Whitcher, eds., Magnetic Resonance Imaging in R, 44 of Journal of Statistical Software, American Statistical Association, 2011, 320 pages, (Monograph Published).
Articles in Refereed Journals

E.A. Vorontsova, A. Gasnikov, E.A. Gorbunov, P. Dvurechensky, Accelerated gradientfree optimization methods with a nonEuclidean proximal operator, Automation and Remote Control, 80 (2019), pp. 14871501.

L. Calatroni, K. Papafitsoros, Analysis and automatic parameter selection of a variational model for mixed Gaussian and salt & pepper noise removal, Inverse Problems and Imaging, 35 (2019), pp. 114001/1114001/37, DOI 10.1088/13616420/ab291a .
Abstract
We analyse a variational regularisation problem for mixed noise removal that was recently proposed in [14]. The data discrepancy term of the model combines L^{1} and L^{2} terms in an infimal convolution fashion and it is appropriate for the joint removal of Gaussian and Salt & Pepper noise. In this work we perform a finer analysis of the model which emphasises on the balancing effect of the two parameters appearing in the discrepancy term. Namely, we study the asymptotic behaviour of the model for large and small values of these parameters and we compare it to the corresponding variational models with L^{1} and L^{2} data fidelity. Furthermore, we compute exact solutions for simple data functions taking the total variation as regulariser. Using these theoretical results, we then analytically study a bilevel optimisation strategy for automatically selecting the parameters of the model by means of a training set. Finally, we report some numerical results on the selection of the optimal noise model via such strategy which confirm the validity of our analysis and the use of popular data models in the case of "blind” model selection. 
A. Gasnikov, P. Dvurechensky, F. Stonyakin, A.A. Titov, An adaptive proximal method for variational inequalities, Computational Mathematics and Mathematical Physics, 59 (2019), pp. 836841.

S. Guminov, Y. Nesterov, P. Dvurechensky, A. Gasnikov, Accelerated primaldual gradient descent with linesearch for convex, nonconvex, and nonsmooth optimization problems, Doklady Mathematics. Maik Nauka/Interperiodica Publishing, Moscow. English. Translation of the Mathematics Section of: Doklady Akademii Nauk. (Formerly: Russian Academy of Sciences. Doklady. Mathematics)., 99 (2019), pp. 125128.

K. Tabelow, E. Balteau, J. Ashburner, M.F. Callaghan, B. Draganski, G. Helms, F. Kherif, T. Leutritz, A. Lutti, Ch. Phillips, E. Reimer, L. Ruthotto, M. Seif, N. Weiskopf, G. Ziegler, S. Mohammadi, hMRI  A toolbox for quantitative MRI in neuroscience and clinical research, NeuroImage, 194 (2019), pp. 191210, DOI 10.1016/j.neuroimage.2019.01.029 .
Abstract
Quantitative magnetic resonance imaging (qMRI) finds increasing application in neuroscience and clinical research due to its sensitivity to microstructural properties of brain tissue, e.g. axon, myelin, iron and water concentration. We introduce the hMRItoolbox, an easytouse opensource tool for handling and processing of qMRI data presented together with an example dataset. This toolbox allows the estimation of highquality multiparameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD and magnetisation transfer MT) that can be used for calculation of standard and novel MRI biomarkers of tissue microstructure as well as improved delineation of subcortical brain structures. Embedded in the Statistical Parametric Mapping (SPM) framework, it can be readily combined with existing SPM tools for estimating diffusion MRI parameter maps and benefits from the extensive range of available tools for highaccuracy spatial registration and statistical inference. As such the hMRItoolbox provides an efficient, robust and simple framework for using qMRI data in neuroscience and clinical research. 
M. Hintermüller, K. Papafitsoros, C.N. Rautenberg, Analytical aspects of spatially adapted total variation regularisation, Journal of Mathematical Analysis and Applications, 454 (2017), pp. 891935, DOI 10.1016/j.jmaa.2017.05.025 .
Abstract
In this paper we study the structure of solutions of the one dimensional weighted total variation regularisation problem, motivated by its application in signal recovery tasks. We study in depth the relationship between the weight function and the creation of new discontinuities in the solution. A partial semigroup property relating the weight function and the solution is shown and analytic solutions for simply data functions are computed. We prove that the weighted total variation minimisation problem is wellposed even in the case of vanishing weight function, despite the lack of coercivity. This is based on the fact that the total variation of the solution is bounded by the total variation of the data, a result that it also shown here. Finally the relationship to the corresponding weighted fidelity problem is explored, showing that the two problems can produce completely different solutions even for very simple data functions. 
M. Hintermüller, C.N. Rautenberg, S. Rösel, Density of convex intersections and applications, Proceedings of the Royal Society of Edinburgh. Section A. Mathematics, 473 (2017), pp. 20160919/120160919/28, DOI 10.1098/rspa.2016.0919 .
Abstract
In this paper we address density properties of intersections of convex sets in several function spaces. Using the concept of Gammaconvergence, it is shown in a general framework, how these density issues naturally arise from the regularization, discretization or dualization of constrained optimization problems and from perturbed variational inequalities. A variety of density results (and counterexamples) for pointwise constraints in Sobolev spaces are presented and the corresponding regularity requirements on the upper bound are identified. The results are further discussed in the context of finite element discretizations of sets associated to convex constraints. Finally, two applications are provided, which include elastoplasticity and image restoration problems. 
M. Hintermüller, C.N. Rautenberg, T. Wu, A. Langer, Optimal selection of the regularization function in a generalized total variation model. Part II: Algorithm, its analysis and numerical tests, Journal of Mathematical Imaging and Vision, 59 (2017), pp. 515533.
Abstract
Based on the generalized total variation model and its analysis pursued in part I (WIAS Preprint no. 2235), in this paper a continuous, i.e., infinite dimensional, projected gradient algorithm and its convergence analysis are presented. The method computes a stationary point of a regularized bilevel optimization problem for simultaneously recovering the image as well as determining a spatially distributed regularization weight. Further, its numerical realization is discussed and results obtained for image denoising and deblurring as well as Fourier and wavelet inpainting are reported on. 
M. Hintermüller, C.N. Rautenberg, Optimal selection of the regularization function in a weighted total variation model. Part I: Modeling and theory, Journal of Mathematical Imaging and Vision, 59 (2017), pp. 498514.
Abstract
Based on the generalized total variation model and its analysis pursued in part I (WIAS Preprint no. 2235), in this paper a continuous, i.e., infinite dimensional, projected gradient algorithm and its convergence analysis are presented. The method computes a stationary point of a regularized bilevel optimization problem for simultaneously recovering the image as well as determining a spatially distributed regularization weight. Further, its numerical realization is discussed and results obtained for image denoising and deblurring as well as Fourier and wavelet inpainting are reported on. 
M. Deppe, K. Tabelow, J. Krämer, J.G. Tenberge, P. Schiffler, S. Bittner, W. Schwindt, F. Zipp, H. Wiendl, S.G. Meuth, Evidence for early, nonlesional cerebellar damage in patients with multiple sclerosis: DTI measures correlate with disability, atrophy, and disease duration, Multiple Sclerosis Journal, 22 (2016), pp. 7384, DOI 10.1177/1352458515579439 .

K. Schildknecht, K. Tabelow, Th. Dickhaus, More specific signal detection in functional magnetic resonance imaging by false discovery rate control for hierarchically structured systems of hypotheses, PLOS ONE, 11 (2016), pp. e0149016/1e0149016/21, DOI 10.1371/journal.pone.0149016 .

H.U. Voss, J.P. Dyke, K. Tabelow, N. Schiff, D. Ballon, Magnetic resonance advection imaging of cerebrovascular pulse dynamics, Journal of Cerebral Blood Flow and Metabolism, 37 (2017), pp. 12231235 (published online on 24.05.2016), DOI 10.1177/0271678x16651449 .

M. Deliano, K. Tabelow, R. König, J. Polzehl, Improving accuracy and temporal resolution of learning curve estimation for within and acrosssession analysis, PLOS ONE, 11 (2016), pp. e0157355/1e0157355/23, DOI 10.1371/journal.pone.0157355 .
Abstract
Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. In this approach, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors for single subjects as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from a shuttlebox avoidance experiment with Mongolian gerbils, our approach revealed performance changes occurring at multiple temporal scales within and across training sessions which were otherwise obscured in the conventional analysis. The proper assessment of the behavioral dynamics of learning at a high temporal resolution clarified and extended current descriptions of the process of avoidance learning. It further disambiguated the interpretation of neurophysiological signal changes recorded during training in relation to learning. 
J. Polzehl, K. Tabelow, Low SNR in diffusion MRI models, Journal of the American Statistical Association, 111 (2016), pp. 14801490, DOI 10.1080/01621459.2016.1222284 .
Abstract
Noise is a common issue for all magnetic resonance imaging (MRI) techniques such as diffusion MRI and obviously leads to variability of the estimates in any model describing the data. Increasing spatial resolution in MR experiments further diminish the signaltonoise ratio (SNR). However, with low SNR the expected signal deviates from the true value. Common modeling approaches therefore lead to a bias in estimated model parameters. Adjustments require an analysis of the data generating process and a characterization of the resulting distribution of the imaging data. We provide an adequate quasilikelihood approach that employs these characteristics. We elaborate on the effects of typical data preprocessing and analyze the bias effects related to low SNR for the example of the diffusion tensor model in diffusion MRI. We then demonstrate the relevance of the problem using data from the Human Connectome Project. 
K. Tabelow, S. Mohammadi, N. Weiskopf, J. Polzehl, POAS4SPM  A toolbox for SPM to denoise diffusion MRI data, Neuroinformatics, 13 (2015), pp. 1929.
Abstract
We present an implementation of a recently developed noise reduction algorithm for dMRI data, called multishell position orientation adaptive smoothing (msPOAS), as a toolbox for SPM. The method intrinsically adapts to the structures of different size and shape in dMRI and hence avoids blurring typically observed in nonadaptive smoothing. We give examples for the usage of the toolbox and explain the determination of experimentdependent parameters for an optimal performance of msPOAS. 
K. Tabelow, H.U. Voss, J. Polzehl, Local estimation of the noise level in MRI using structural adaptation, Medical Image Analysis, 20 (2015), pp. 7686.
Abstract
We present a method for local estimation of the signaldependent noise level in magnetic resonance images. The procedure uses a multiscale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximumlikelihood estimator for the local noise level. The validity of the method was evaluated on repeated diffusion data of a phantom and simulated data using T1data corrupted with artificial noise. Simulation results are compared with a recently proposed estimate. The method was applied to a highresolution diffusion dataset to obtain improved diffusion model estimation results and to demonstrate its usefulness in methods for enhancing diffusion data. 
J. Krämer, M. Deppe, K. Göbel, K. Tabelow, H. Wiendl, S.G. Meuth, Recovery of thalamic microstructural damage after Shiga toxin 2associated hemolyticuremic syndrome, Journal of the Neurological Sciences, 356 (2015), pp. 175183.

S. Mohammadi, K. Tabelow, L. Ruthotto, Th. Feiweier, J. Polzehl, N. Weiskopf, Highresolution diffusion kurtosis imaging at 3T enabled by advanced postprocessing, Frontiers in Neuroscience, 8 (2015), pp. 427/1427/14.

S. Becker, K. Tabelow, S. Mohammadi, N. Weiskopf, J. Polzehl, Adaptive smoothing of multishell diffusionweighted magnetic resonance data by msPOAS, NeuroImage, 95 (2014), pp. 90105.
Abstract
In this article we present a noise reduction method (msPOAS) for multishell diffusionweighted magnetic resonance data. To our knowledge, this is the first smoothing method which allows simultaneous smoothing of all qshells. It is applied directly to the diffusion weighted data and consequently allows subsequent analysis by any model. Due to its adaptivity, the procedure avoids blurring of the inherent structures and preserves discontinuities. MsPOAS extends the recently developed positionorientation adaptive smoothing (POAS) procedure to multishell experiments. At the same time it considerably simplifies and accelerates the calculations. The behavior of the algorithm msPOAS is evaluated on diffusionweighted data measured on a single shell and on multiple shells. 
M. Welvaert, K. Tabelow, R. Seurinck, Y. Rosseel, Adaptive smoothing as inference strategy: More specificity for unequally sized or neighboring regions, Neuroinformatics, 11 (2013), pp. 435445.
Abstract
Although spatial smoothing of fMRI data can serve multiple purposes, increasing the sensitivity of activation detection is probably its greatest benefit. However, this increased detection power comes with a loss of specificity when nonadaptive smoothing (i.e. the standard in most software packages) is used. Simulation studies and analysis of experimental data was performed using the R packages neuRosim and fmri. In these studies, we systematically investigated the effect of spatial smoothing on the power and number of false positives in two particular cases that are often encountered in fMRI research: (1) Single condition activation detection for regions that differ in size, and (2) multiple condition activation detection for neighbouring regions. Our results demonstrate that adaptive smoothing is superior in both cases because less false positives are introduced by the spatial smoothing process compared to standard Gaussian smoothing or FDR inference of unsmoothed data. 
S. Becker, K. Tabelow, H.U. Voss, A. Anwander, R.M. Heidemann, J. Polzehl, Positionorientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS), Medical Image Analysis, 16 (2012), pp. 11421155.
Abstract
We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both space and diffusion direction. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric and group operations, in this case the Lie group of threedimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. The positionorientation adaptive smoothing preserves the edges of the observed fine and anisotropic structures. The POASalgorithm is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting. 
K. Tabelow, H.U. Voss, J. Polzehl, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, Journal of Neuroscience Methods, 203 (2012), pp. 200211.
Abstract
In this paper we develop a tensor mixture model for diffusion weighted imaging data using an automatic model selection criterion for the order of tensor components in a voxel. We show that the weighted orientation distribution function for this model can be expanded into a mixture of angular central Gaussian distributions. We show properties of this model in extensive simulations and in a high angular resolution experimental data set. The results suggest that the model may improve imaging of cerebral fiber tracts. We demonstrate how inference on canonical model parameters may give rise to new clinical applications. 
K. Tabelow, J.D. Clayden, P. Lafaye DE Micheaux, J. Polzehl, V.J. Schmid, B. Whitcher, Image analysis and statistical inference in neuroimaging with R, NeuroImage, 55 (2011), pp. 16861693.
Abstract
R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988). The R language is part of the GNU project and offers versions that compile and run on almost every major operating system currently available. We highlight several R packages built specifically for the analysis of neuroimaging data in the context of functional MRI, diffusion tensor imaging, and dynamic contrastenhanced MRI. We review their methodology and give an overview of their capabilities for neuroimaging. In addition we summarize some of the current activities in the area of neuroimaging software development in R. 
K. Tabelow, J. Polzehl, Statistical parametric maps for functional MRI experiments in R: The package fmri, Journal of Statistical Software, 44 (2011), pp. 121.
Abstract
The package fmri is provided for analysis of single run functional Magnetic Resonance Imaging data. It implements structural adaptive smoothing methods with signal detection for adaptive noise reduction which avoids blurring of edges of activation areas. fmri provides fmri analysis from time series modeling to signal detection and publicationready images. 
J. Bardin, J. Fins, D. Katz, J. Hersh, L. Heier, K. Tabelow, J. Dyke, D. Ballon, N. Schiff, H. Voss, Dissociations between behavioral and fMRIbased evaluations of cognitive function after brain injury, Brain, 134 (2011), pp. 769782.
Abstract
Functional neuroimaging methods hold promise for the identification of cognitive function and communication capacity in some severely braininjured patients who may not retain sufficient motor function to demonstrate their abilities. We studied seven severely braininjured patients and a control group of 14 subjects using a novel hierarchical functional magnetic resonance imaging assessment utilizing mental imagery responses. Whereas the control group showed consistent and accurate (for communication) bloodoxygenleveldependent responses without exception, the braininjured subjects showed a wide variation in the correlation of bloodoxygenleveldependent responses and overt behavioural responses. Specifically, the braininjured subjects dissociated bedside and functional magnetic resonance imagingbased command following and communication capabilities. These observations reveal significant challenges in developing validated functional magnetic resonance imagingbased methods for clinical use and raise interesting questions about underlying brain function assayed using these methods in braininjured subjects. 
J. Polzehl, K. Tabelow, Beyond the Gaussian model in diffussionweighted imaging: The package dti, Journal of Statistical Software, 44 (2011), pp. 126.
Abstract
Diffusion weighted imaging is a magnetic resonance based method to investigate tissue microstructure especially in the human brain via water diffusion. Since the standard diffusion tensor model for the acquired data failes in large portion of the brain voxel more sophisticated models have bee developed. Here, we report on the package dti and how some of these models can be used with the package. 
E. Diederichs, A. Juditsky, V. Spokoiny, Ch. Schütte, Sparse nonGaussian component analysis, IEEE Transactions on Information Theory, 56 (2010), pp. 30333047.

J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation for statistical parametric mapping, NeuroImage, 52 (2010), pp. 515523.
Abstract
Functional Magnetic Resonance Imaging inherently involves noisy measurements and a severe multiple test problem. Smoothing is usually used to reduce the effective number of multiple comparisons and to locally integrate the signal and hence increase the signaltonoise ratio. Here, we provide a new structural adaptive segmentation algorithm (AS) that naturally combines the signal detection with noise reduction in one procedure. Moreover, the new method is closely related to a recently proposed structural adaptive smoothing algorithm and preserves shape and spatial extent of activation areas without blurring the borders. 
K. Tabelow, V. Piëch, J. Polzehl, H.U. Voss, Highresolution fMRI: Overcoming the signaltonoise problem, Journal of Neuroscience Methods, 178 (2009), pp. 357365.
Abstract
Increasing the spatial resolution in functional Magnetic Resonance Imaging (fMRI) inherently lowers the signaltonoise ratio (SNR). In order to still detect functionally significant activations in highresolution images, spatial smoothing of the data is required. However, conventional nonadaptive smoothing comes with a reduced effective resolution, foiling the benefit of the higher acquisition resolution. We show how our recently proposed structural adaptive smoothing procedure for functional MRI data can improve signal detection of highresolution fMRI experiments regardless of the lower SNR. The procedure is evaluated on human visual and sensorymotor mapping experiments. In these applications, the higher resolution could be fully utilized and highresolution experiments were outperforming normal resolution experiments by means of both statistical significance and information content. 
J. Polzehl, K. Tabelow, Structural adaptive smoothing in diffusion tensor imaging: The R package dti, Journal of Statistical Software, 31 (2009), pp. 124.
Abstract
Diffusion Weighted Imaging has become and will certainly continue to be an important tool in medical research and diagnostics. Data obtained with Diffusion Weighted Imaging are characterized by a high noise level. Thus, estimation of quantities like anisotropy indices or the main diffusion direction may be significantly compromised by noise in clinical or neuroscience applications. Here, we present a new package dti for R, which provides functions for the analysis of diffusion weighted data within the diffusion tensor model. This includes smoothing by a recently proposed structural adaptive smoothing procedure based on the PropagationSeparation approach in the context of the widely used Diffusion Tensor Model. We extend the procedure and show, how a correction for Rician bias can be incorporated. We use a heteroscedastic nonlinear regression model to estimate the diffusion tensor. The smoothing procedure naturally adapts to different structures of different size and thus avoids oversmoothing edges and fine structures. We illustrate the usage and capabilities of the package through some examples. 
K. Tabelow, J. Polzehl, A.M. Uluğ, J.P. Dyke, R. Watts, L.A. Heier, H.U. Voss, Accurate localization of brain activity in presurgical fMRI by structure adaptive smoothing, IEEE Transactions on Medical Imaging, 27 (2008), pp. 531537.
Abstract
An important problem of the analysis of fMRI experiments is to achieve some noise reduction of the data without blurring the shape of the activation areas. As a novel solution to this problem, the PropagationSeparation approach (PS), a structure adaptive smoothing method, has been proposed recently. PS adapts to different shapes of activation areas by generating a spatial structure corresponding to similarities and differences between time series in adjacent locations. In this paper we demonstrate how this method results in more accurate localization of brain activity. First, it is shown in numerical simulations that PS is superior over Gaussian smoothing with respect to the accurate description of the shape of activation clusters and and results in less false detections. Second, in a study of 37 presurgical planning cases we found that PS and Gaussian smoothing often yield different results, and we present examples showing aspects of the superiority of PS as applied to presurgical planning. 
K. Tabelow, J. Polzehl, V. Spokoiny, H.U. Voss, Diffusion tensor imaging: Structural adaptive smoothing, NeuroImage, 39 (2008), pp. 17631773.
Abstract
Diffusion Tensor Imaging (DTI) data is characterized by a high noise level. Thus, estimation errors of quantities like anisotropy indices or the main diffusion direction used for fiber tracking are relatively large and may significantly confound the accuracy of DTI in clinical or neuroscience applications. Besides pulse sequence optimization, noise reduction by smoothing the data can be pursued as a complementary approach to increase the accuracy of DTI. Here, we suggest an anisotropic structural adaptive smoothing procedure, which is based on the PropagationSeparation method and preserves the structures seen in DTI and their different sizes and shapes. It is applied to artificial phantom data and a brain scan. We show that this method significantly improves the quality of the estimate of the diffusion tensor and hence enables one either to reduce the number of scans or to enhance the input for subsequent analysis such as fiber tracking. 
D. Divine, J. Polzehl, F. Godtliebsen, A propagationseparation approach to estimate the autocorrelation in a timeseries, Nonlinear Processes in Geophysics, 15 (2008), pp. 591599.

V. Katkovnik, V. Spokoiny, Spatially adaptive estimation via fitted local likelihood techniques, IEEE Transactions on Signal Processing, 56 (2008), pp. 873886.
Abstract
This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modelling of observations and estimated signals. The approach is based on the assumption of a local homogeneity of the signal: for every point there exists a neighborhood in which the signal can be well approximated by a constant. The fitted local likelihood statistics is used for selection of an adaptive size of this neighborhood. The algorithm is developed for quite a general class of observations subject to the exponential distribution. The estimated signal can be uni and multivariable. We demonstrate a good performance of the new algorithm for Poissonian image denoising and compare of the new method versus the intersection of confidence interval (ICI) technique that also exploits a selection of an adaptive neighborhood for estimation. 
O. Minet, H. Gajewski, J.A. Griepentrog, J. Beuthan, The analysis of laser light scattering during rheumatoid arthritis by image segmentation, Laser Physics Letters, 4 (2007), pp. 604610.

H.U. Voss, K. Tabelow, J. Polzehl, O. Tchernichovski, K. Maul, D. SalgadoCommissariat, D. Ballon, S.A. Helekar, Functional MRI of the zebra finch brain during song stimulation suggests a lateralized response topography, Proceedings of the National Academy of Sciences of the United States of America, 104 (2007), pp. 1066710672.
Abstract
Electrophysiological and activitydependent gene expression studies of birdsong have contributed to the understanding of the neural representation of natural sounds. However, we have limited knowledge about the overall spatial topography of song representation in the avian brain. Here, we adapt the noninvasive functional MRI method in mildly sedated zebra finches (Taeniopygia guttata) to localize and characterize song driven brain activation. Based on the blood oxygenation leveldependent signal, we observed a differential topographic responsiveness to playback of bird's own song, tutor song, conspecific song, and a pure tone as a nonsong stimulus. The bird's own song caused a stronger response than the tutor song or tone in higher auditory areas. This effect was more pronounced in the medial parts of the forebrain. We found leftright hemispheric asymmetry in sensory responses to songs, with significant discrimination between stimuli observed only in the right hemisphere. This finding suggests that perceptual responses might be lateralized in zebra finches. In addition to establishing the feasibility of functional MRI in sedated songbirds, our results demonstrate spatial coding of song in the zebra finch forebrain, based on developmental familiarity and experience. 
J. Polzehl, K. Tabelow, Adaptive smoothing of digital images: The R package adimpro, Journal of Statistical Software, 19 (2007), pp. 117.
Abstract
Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used nonadaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the PropagationSeparation approach by Polzehl and Spokoiny (2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples. 
J. Polzehl, K. Tabelow, fmri: A package for analyzing fmri data, Newsletter of the R Project for Statistical Computing, 7 (2007), pp. 1317.

K. Tabelow, J. Polzehl, H.U. Voss, V. Spokoiny, Analyzing fMRI experiments with structural adaptive smoothing procedures, NeuroImage, 33 (2006), pp. 5562.
Abstract
Data from functional magnetic resonance imaging (fMRI) consists of time series of brain images which are characterized by a low signaltonoise ratio. In order to reduce noise and to improve signal detection the fMRI data is spatially smoothed. However, the common application of a Gaussian filter does this at the cost of loss of information on spatial extent and shape of the activation area. We suggest to use the propagationseparation procedures introduced by Polzehl and Spokoiny (2006) instead. We show that this significantly improves the information on the spatial extent and shape of the activation region with similar results for the noise reduction. To complete the statistical analysis, signal detection is based on thresholds defined by random field theory. Effects of ad aptive and nonadaptive smoothing are illustrated by artificial examples and an analysis of experimental data. 
G. Blanchard, M. Kawanabe, M. Sugiyama, V. Spokoiny, K.R. Müller, In search of nonGaussian components of a highdimensional distribution, Journal of Machine Learning Research (JMLR). MIT Press, Cambridge, MA. English, English abstracts., 7 (2006), pp. 247282.
Abstract
Finding nonGaussian components of highdimensional data is an important preprocessing step for efficient information processing. This article proposes a new em linear method to identify the “nonGaussian subspace” within a very general semiparametric framework. Our proposed method, called NGCA (NonGaussian Component Analysis), is essentially based on the fact that we can construct a linear operator which, to any arbitrary nonlinear (smooth) function, associates a vector which belongs to the low dimensional nonGaussian target subspace up to an estimation error. By applying this operator to a family of different nonlinear functions, one obtains a family of different vectors lying in a vicinity of the target space. As a final step, the target space itself is estimated by applying PCA to this family of vectors. We show that this procedure is consistent in the sense that the estimaton error tends to zero at a parametric rate, uniformly over the family. Numerical examples demonstrate the usefulness of our method. 
H. Gajewski, J.A. Griepentrog, A descent method for the free energy of multicomponent systems, Discrete and Continuous Dynamical Systems, 15 (2006), pp. 505528.

A. Goldenshluger, V. Spokoiny, Recovering convex edges of image from noisy tomographic data, IEEE Transactions on Information Theory, 52 (2006), pp. 13221334.

J. Polzehl, V. Spokoiny, Propagationseparation approach for local likelihood estimation, Probability Theory and Related Fields, 135 (2006), pp. 335362.
Abstract
The paper presents a unified approach to local likelihood estimation for a broad class of nonparametric models, including, e.g., regression, density, Poisson and binary response models. The method extends the adaptive weights smoothing (AWS) procedure introduced by the authors [Adaptive weights smoothing with applications to image sequentation. J. R. Stat. Soc., Ser. B 62, 335354 (2000)] in the context of image denoising. The main idea of the method is to describe a greatest possible local neighborhood of every design point in which the local parametric assumption is justified by the data. The method is especially powerful for model functions having large homogeneous regions and sharp discontinuities. The performance of the proposed procedure is illustrated by numerical examples for density estimation and classification. We also establish some remarkable theoretical nonasymptotic results on properties of the new algorithm. This includes the “propagation” property which particularly yields the root$n$ consistency of the resulting estimate in the homogeneous case. We also state an “oracle” result which implies rate optimality of the estimate under usual smoothness conditions and a “separation” result which explains the sensitivity of the method to structural changes. 
J. Griepentrog, On the unique solvability of a nonlocal phase separation problem for multicomponent systems, Banach Center Publications, 66 (2004), pp. 153164.

A. Goldenshluger, V. Spokoiny, On the shapefrommoments problem and recovering edges from noisy Radon data, Probability Theory and Related Fields, 128 (2004), pp. 123140.

J. Polzehl, V. Spokoiny, Image denoising: Pointwise adaptive approach, The Annals of Statistics, 31 (2003), pp. 3057.
Abstract
A new method of pointwise adaptation has been proposed and studied in Spokoiny (1998) in context of estimation of piecewise smooth univariate functions. The present paper extends that method to estimation of bivariate greyscale images composed of large homogeneous regions with smooth edges and observed with noise on a gridded design. The proposed estimator $, hatf(x) ,$ at a point $, x ,$ is simply the average of observations over a window $, hatU(x) ,$ selected in a datadriven way. The theoretical properties of the procedure are studied for the case of piecewise constant images. We present a nonasymptotic bound for the accuracy of estimation at a specific grid point $, x ,$ as a function of the number of pixel $n$, of the distance from the point of estimation to the closest boundary and of smoothness properties and orientation of this boundary. It is also shown that the proposed method provides a near optimal rate of estimation near edges and inside homogeneous regions. We briefly discuss algorithmic aspects and the complexity of the procedure. The numerical examples demonstrate a reasonable performance of the method and they are in agreement with the theoretical issues. An example from satellite (SAR) imaging illustrates the applicability of the method. 
J. Polzehl, V. Spokoiny, Functional and dynamic Magnetic Resonance Imaging using vector adaptive weights smoothing, Journal of the Royal Statistical Society. Series C. Applied Statistics, 50 (2001), pp. 485501.
Abstract
We consider the problem of statistical inference for functional and dynamic Magnetic Resonance Imaging (MRI). A new approach is proposed which extends the adaptive weights smoothing (AWS) procedure from Polzehl and Spokoiny (2000) originally designed for image denoising. We demonstrate how the AWS method can be applied for time series of images, which typically occur in functional and dynamic MRI. It is shown how signal detection in functional MRI and analysis of dynamic MRI can benefit from spatially adaptive smoothing. The performance of the procedure is illustrated using real and simulated data. 
J. Polzehl, V. Spokoiny, Adaptive Weights Smoothing with applications to image restoration, Journal of the Royal Statistical Society. Series B. Statistical Methodology, 62 (2000), pp. 335354.
Abstract
We propose a new method of nonparametric estimation which is based on locally constant smoothing with an adaptive choice of weights for every pair of datapoints. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on some simulated univariate and bivariate examples and compare it with other nonparametric methods. Finally we discuss applications of this procedure to magnetic resonance and satellite imaging.
Contributions to Collected Editions

M. Hintermüller, A. Langer, C.N. Rautenberg, T. Wu, Adaptive regularization for image reconstruction from subsampled data, in: Imaging, Vision and Learning Based on Optimization and PDEs IVLOPDE, Bergen, Norway, August 29  September 2, 2016, X.Ch. Tai, E. Bae, M. Lysaker, eds., Mathematics and Visualization, Springer International Publishing, Berlin, 2018, pp. 326, DOI 10.1007/9783319912745 .
Abstract
Choices of regularization parameters are central to variational methods for image restoration. In this paper, a spatially adaptive (or distributed) regularization scheme is developed based on localized residuals, which properly balances the regularization weight between regions containing image details and homogeneous regions. Surrogate iterative methods are employed to handle given subsampled data in transformed domains, such as Fourier or wavelet data. In this respect, this work extends the spatially variant regularization technique previously established in [15], which depends on the fact that the given data are degraded images only. Numerical experiments for the reconstruction from partial Fourier data and for wavelet inpainting prove the efficiency of the newly proposed approach. 
N. Buzun, A. Suvorikova, V. Spokoiny, Multiscale parametric approach for change point detection, in: Proceedings of Information Technology and Systems 2016  The 40th Interdisciplinary Conference & School, Institute for Information Transmission Problems (Kharkevich Institute), Moscow, pp. 979996.

K. Tabelow, J. Polzehl, SHOWCASE 21  Towards invivo histology, in: MATHEON  Mathematics for Key Technologies, M. Grötschel, D. Hömberg, J. Sprekels, V. Mehrmann ET AL., eds., 1 of EMS Series in Industrial and Applied Mathematics, European Mathematical Society Publishing House, Zurich, 2014, pp. 378379.

H. Lamecker, H.Ch. Hege, K. Tabelow, J. Polzehl, F2  Image processing, in: MATHEON  Mathematics for Key Technologies, M. Grötschel, D. Hömberg, J. Sprekels, V. Mehrmann ET AL., eds., 1 of EMS Series in Industrial and Applied Mathematics, European Mathematical Society Publishing House, Zurich, 2014, pp. 359376.

K. Tabelow, Viele Tests  viele Fehler, in: Besser als Mathe  Moderne angewandte Mathematik aus dem MATHEON zum Mitmachen, K. Biermann, M. Grötschel, B. LutzWestphal, eds., Reihe: Populär, Vieweg+Teubner, Wiesbaden, 2010, pp. 117120.

H. Gajewski, J.A. Griepentrog, A. Mielke, J. Beuthan, U. Zabarylo, O. Minet, Image segmentation for the investigation of scatteredlight images when laseroptically diagnosing rheumatoid arthritis, in: Mathematics  Key Technology for the Future, W. Jäger, H.J. Krebs, eds., Springer, Heidelberg, 2008, pp. 149161.
Talks, Poster

A. Gasnikov, P. Dvurechensky, E. Gorbunov, E. Vorontsova, D. Selikhanovych, C.A. Uribe, Nearoptimal method for highly smooth convex optimization, Conference on Learning Theory, COLT 2019, Phoenix, Arizona, USA, June 24  28, 2019.

A. Kroshnin, N. Tupitsa, D. Dvinskikh, P. Dvurechensky, A. Gasnikov, C.A. Uribe , On the complexity of approximating Wasserstein barycenters, Thirtysixth International Conference on Machine Learning, ICML 2019, Long Beach, CA, USA, June 9  15, 2019.

D. Dvinskikh, Distributed decentralized (stochastic) optimization for dual friendly functions, Optimization and Statistical Learning, Les Houches, France, March 24  29, 2019.

P. Dvurechensky, On the complexity of optimal transport problems, Computational and Mathematical Methods in Data Science, Berlin, October 24  25, 2019.

K. Papafitsoros, A function space framework for structural total variation regularization with applications in inverse problems, Applied Inverse Problems Conference, Minisymposium: MultiModality/MultiSpectral Imaging and Structural Priors, July 8  12, 2019, Grenoble, France, August 8, 2019.

K. Papafitsoros, Quantitative MRI: From fingerprinting to integrated physicsbased models, Synergistic Reconstruction Symposium CCP PETMR, November 3  6, 2019, Chester, UK, November 4, 2019.

J. Polzehl, K. Tabelow, Analyzing neuroimaging experiments within R, 2019 OHBM Annual Meeting, Rom, Italy, June 9  13, 2019.

K. Tabelow, Adaptive smoothing data from multiparameter mapping, 7th NordicBaltic Biometric Conference, June 3  5, 2019, Vilnius University, Faculty of Medicine, Lithuania, June 5, 2019.

K. Tabelow, Modelbased imaging for quantitative MRI, KoMSO ChallengeWorkshop Mathematical Modeling of Biomedical Problems, December 12  13, 2019, FriedrichAlexanderUniversity ErlangenNuremberg (FAU), December 12, 2019.

K. Tabelow, Neuroimaging workshop, Advanced Statistics, February 13  14, 2019, University of Zurich, Center for Reproducible Science, Switzerland.

K. Tabelow, Quantitative MRI for invivo histology, Doktorandenseminar, Berlin School of Mind and Brain, April 1, 2019.

M. Hintermüller, M. Holler, K. Papafitsoros, A function space framework for structural total variation regularization in inverse problems, MIA 2018  Mathematics and Image Analysis, HumboldtUniversität zu Berlin, January 15  17, 2018.

J. Polzehl, High resolution magnetic resonance imaging experiments  Lessons in nonlinear statistical modeling, 3rd Leibniz MMS Days, February 28  March 2, 2018, Wissenschaftszentrum Leipzig, March 1, 2018.

M. Hintermüller, (Pre)Dualization, dense embeddings of convex sets, and applications in image processing, HCM Workshop: Nonsmooth Optimization and its Applications, May 15  19, 2017, Hausdorff Center for Mathematics, Bonn, May 15, 2017.

M. Hintermüller, Bilevel optimization and applications in imaging, Workshop ``Emerging Developments in Interfaces and Free Boundaries'', January 22  28, 2017, Mathematisches Forschungsinstitut Oberwolfach.

M. Hintermüller, Bilevel optimization and applications in imaging, Mathematisches Kolloquium, Universität Wien, Austria, January 18, 2017.

M. Hintermüller, Bilevel optimization and some ``parameter learning'' applications in image processing, LMS Workshop ``Variational Methods Meet Machine Learning'', September 18, 2017, University of Cambridge, Centre for Mathematical Sciences, UK, September 18, 2017.

M. Hintermüller, On (pre)dualization, dense embeddings of convex sets, and applications in image processing, Seminar, Isaac Newton Institute, Programme ``Variational Methods and Effective Algorithms for Imaging and Vision'', Cambridge, UK, August 30, 2017.

M. Hintermüller, On (pre)dualization, dense embeddings of convex sets, and applications in image processing, University College London, Centre for Inverse Problems, UK, October 27, 2017.

J. Polzehl, Connectivity networks in neuroscience  Construction and analysis, Summer School 2017: Probabilistic and Statistical Methods for Networks, August 21  September 1, 2017, Technische Universität Berlin, Berlin Mathematical School.

J. Polzehl, Structural adaptation  A statistical concept for image denoising, Seminar, Isaac Newton Institute, Programme ``Variational Methods and Effective Algorithms for Imaging and Vision'', Cambridge, UK, December 5, 2017.

J. Polzehl, Toward invivo histology of the brain, NeuroStatistics: The Interface between Statistics and Neuroscience, University of Minnesota, School of Statistics (IRSA), Minneapolis, USA, May 5, 2017.

J. Polzehl, Towards invivo histology of the brain, Berlin Symposium 2017: Modern Statistical Methods From Data to Knowledge, December 14  15, 2017, organized by Indiana Laboratory of Biostatistical Analysis of Large Data with Structure (ILBALDS), Berlin, December 14, 2017.

K. Tabelow, Ch. D'alonzo, L. Ruthotto, M.F. Callaghan, N. Weiskopf, J. Polzehl, S. Mohammadi, Removing the estimation bias due to the noise floor in multiparameter maps, The International Society for Magnetic Resonance in Medicine (ISMRM) 25th Annual Meeting & Exhibition, Honolulu, USA, April 22  27, 2017.

K. Tabelow, Adaptive smoothing of multiparameter maps, Berlin Symposium 2017: Modern Statistical Methods From Data to Knowledge, December 14  15, 2017, organized by Indiana Laboratory of Biostatistical Analysis of Large Data with Structure (ILBALDS), Berlin, December 14, 2017.

K. Tabelow, High resolution MRI by variance and bias reduction, Channel Network Conference 2017 of the International Biometric Society (IBS), April 24  26, 2017, Hasselt University, Diepenbeek, Belgium, April 25, 2017.

K. Tabelow, To smooth or not to smooth in fMRI, Cognitive Neuroscience Seminar, Universitätsklinikum HamburgEppendorf, Institut für Computational Neuroscience, April 4, 2017.

T. Wu, Bilevel optimization and applications in imaging sciences, August 24  25, 2016, Shanghai Jiao Tong University, Institute of Natural Sciences, China.

M. Hintermüller, K. Papafitsoros, C. Rautenberg, A fine scale analysis of spatially adapted total variation regularisation, Imaging, Vision and Learning based on Optimization and PDEs, Bergen, Norway, August 29  September 1, 2016.

M. Hintermüller, Bilevel optimization and applications in imaging, Imaging, Vision and Learning based on Optimization and PDEs, August 29  September 1, 2016, Bergen, Norway, August 30, 2016.

M. Hintermüller, Shape and topological sensitivities in mathematical image processing, BMS Summer School ``Mathematical and Numerical Methods in Image Processing'', July 25  August 5, 2016, Berlin Mathematical School, Technische Universität Berlin, HumboldtUniversität zu Berlin, Berlin, August 4, 2016.

J. Polzehl, Assessing dynamics in learning experiments, Novel Statistical Methods in Neuroscience, June 22  24, 2016, OttovonGuerickeUniversität Magdeburg, Institut für Mathematische Stochastik, June 22, 2016.

J. Polzehl, Modeling high resolution MRI: Statistical issues, Mathematical and Statistical Challenges in Neuroimaging Data Analysis, January 31  February 5, 2016, Banff International Research Station (BIRS), Banff, Canada, February 1, 2016.

K. Tabelow, V. Avanesov, M. Deliano, R. König, A. Brechmann, J. Polzehl, Assessing dynamics in learning experiments, Challenges in Computational Neuroscience: Transition Workshop, Research Triangle Park, North Carolina, USA, May 4  6, 2016.

K. Tabelow, Ch. D'alonzo, J. Polzehl, M.F. Callaghan, L. Ruthotto, N. Weiskopf, S. Mohammadi, How to achieve very high resolution quantitative MRI at 3T?, 22th Annual Meeting of the Organization of Human Brain Mapping (OHBM 2016), Geneva, Switzerland, June 26  30, 2016.

K. Tabelow, Adaptive smoothing in quantitative imaging, Invivo histology/VBQ meeting, Max Planck Institute for Human Cognitinve and Brain Sciences, Leipzig, April 13, 2016.

N. Buzun, Multiscale parametric approach for change point detection, Information Technologies and Systems 2015, September 6  11, 2015, Russian Academy of Sciences, Institute for Information Transmission Problems, Sochi, Russian Federation, September 9, 2015.

J. Krämer, M. Deppe, K. Göbel, K. Tabelow, H. Wiendl, S. Meuth, Recovery of thalamic microstructural damage after Shiga toxin 2associated hemolyticuremic syndrome, 21th Annual Meeting of the Organization for Human Brain Mapping, Honolulu, USA, June 14  18, 2015.

H.U. Voss, J. Dyke, D. Ballon, N. Schiff, K. Tabelow, Magnetic resonance advection imaging (MRAI) depicts vascular anatomy, 21th Annual Meeting of the Organization for Human Brain Mapping, Honolulu, USA, June 14  18, 2015.

J. Polzehl, Analysing dMRI data: Consequences of low SNR, SAMSI Working group ``Structural Connectivity'', Statistical and Applied Mathematical Sciences Institute (SAMSI), Research Triangle Park, USA, December 8, 2015.

J. Polzehl, K. Tabelow, H.U. Voss, Towards higher spatial resolution in DTI using smoothing, 21th Annual Meeting of the Organization for Human Brain Mapping, Honolulu, USA, June 14  18, 2015.

J. Polzehl, K. Tabelow, Bias in low SNR diffusion MRI experiments: Problems and solution, 21th Annual Meeting of the Organization for Human Brain Mapping, Honolulu, USA, June 14  18, 2015.

J. Polzehl, Statistical problems in diffusion weighted MR, University of Minnesota, BiostatisticsStatistics Working Group in Imaging, Minneapolis, USA, January 30, 2015.

K. Tabelow, M. Deliano, M. Jörn, R. König, A. Brechmann, J. Polzehl, Towards a population analysis of behavioral and neural state transitions during associative learning, 21th Annual Meeting of the Organization for Human Brain Mapping, Honolulu, USA, June 14  18, 2015.

K. Tabelow, To smooth or not to smooth in fMRI, Seminar ``Bildgebende Verfahren in den Neurowissenschaften: Grundlagen und aktuelle Ergebnisse'', Universitätsklinikum Jena, IDIR, Medical Physics Group, April 17, 2015.

K. Tabelow, msPOAS  An adaptive denoising procedure for dMRI data, Riemannian Geometry in Shape Analysis and Computational Anatomy, February 23  27, 2015, Universität Wien, Erwin Schrödinger International Institute for Mathematical Physics, Austria, February 25, 2015.

S. Mohammadi, L. Ruthotto, K. Tabelow, T. Feiweier, J. Polzehl, N. Weiskopf, ACID  A postprocessing toolbox for advanced diffusion MRI, 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, June 8  12, 2014.

N. Angenstein, J. Polzehl, K. Tabelow, A. Brechmann, Categorical versus sequential processing of sound duration, 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, June 8  12, 2014.

J. Polzehl, Estimation of sparse precision matrices, MMSWorkshop ``large p small n'', WIASBerlin, April 15, 2014.

J. Polzehl, Quantification of noise in MR experiments, Statistical Challenges in Neuroscience, September 3  5, 2014, University of Warwick, Centre for Research in Statistical Methodology, UK, September 4, 2014.

J. Polzehl, Quantification of noise in MR experiments, International Workshop ``Advances in Optimization and Statistics'', May 15  16, 2014, Russian Academy of Sciences, Institute of Information Transmission Problems (Kharkevich Institute), Moscow, May 16, 2014.

J. Polzehl, Statistical problems in diffusion weighted MR, CoSy Seminar, University of Uppsala, Department of Mathematics, Sweden, November 11, 2014.

K. Tabelow, S. Mohammadi, N. Weiskopf, J. Polzehl, Adaptive noise reduction in multishell dMRI data with SPM by POAS4SPM, 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, June 8  12, 2014.

K. Tabelow, H.U. Voss, J. Polzehl, Local estimation of noise standard deviation in MRI images using propagation separation, 20th Annual Meeting of the Organization for Human Brain Mapping, Hamburg, June 8  12, 2014.

K. Tabelow, H.U. Voss, J. Polzehl, Local estimation of the noise level in MRI images using structural adaptation, 5th UltraHighfield MRI Scientific Symposium, Max Delbrück Center, Berlin, June 20, 2014.

K. Tabelow, Highresolution diffusion MRI by msPOAS, Statistical Challenges in Neuroscience, September 3  5, 2014, University of Warwick, Centre for Research in Statistical Methodology, UK, September 4, 2014.

K. Tabelow, S. Becker, S. Mohammadi, N. Weiskopf, J. Polzehl, Multishell positionorientation adaptive smoothing (msPOAS), 19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, USA, June 16  20, 2013.

K. Tabelow, H.U. Voss, J. Polzehl, Analyzing fMRI and dMRI experiments with R, 19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, USA, June 16  20, 2013.

K. Tabelow, Assessing the structure of the brain, WIASDay, WIAS Berlin, February 18, 2013.

K. Tabelow, Noise in diffusion MRI  Impact and treatment, Strukturelle MRBildgebung in der neuropsychiatrischen Forschung, September 13  14, 2013, Philipps Universität Marburg, September 13, 2013.

M. Welvaert, K. Tabelow, R. Seurinck, Y. Rosseel, Defining ROIs based on localizer studies: More specific localization using adaptive smoothing, 19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, USA, June 16  20, 2013.

S. Mohammadi, K. Tabelow, Th. Feiweier, J. Polzehl, N. Weiskopf, Highresolution diffusion kurtosis imaging (DKI) improves detection of graywhite matter boundaries, 19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, USA, June 16  20, 2013.

J. Polzehl, Diffusion weighted magnetic resonance imaging  Data, models and problems, Statistics Seminar, University of Minnesota, School of Statistics, USA, June 6, 2013.

J. Polzehl, Positionorientation adaptive smoothing (POAS) in diffusion weighted imaging, Neuroimaging Data Analysis, June 9  14, 2013, Statistical and Applied Mathematical Sciences Institute (SAMSI), Durham (NC), USA, June 9, 2013.

J. Polzehl, Positionorientation adaptive smoothing  Noise reduction in dMRI, Strukturelle MRBildgebung in der Neuropsychiatrischen Forschung, September 13  14, 2013, PhilippsUniversität Marburg, Klinik für Psychiatrie und Psychotherapie, Zentrum für Psychische Gesundheit, September 14, 2013.

J. Polzehl, dMRI modeling: An intermediate step to fiber tracking and connectivity, Neuroimaging Data Analysis, June 9  14, 2013, Statistical and Applied Mathematical Sciences Institute (SAMSI), Durham (NC), USA, June 9, 2013.

S. Becker, K. Tabelow, H.U. Voss, A. Anwander, R.M. Heidemann, J. Polzehl, Positionorientation adaptive smoothing (POAS) at 7T dMRI, UltraHighfield MRI Scientific Symposium, Max Delbrück Communication Center, Berlin, June 8, 2012.

S. Becker, Diffusion weighted imaging: Modeling and analysis beyond the diffusion tensor, Methodological Workshop: Structural Brain Connectivity: Diffusion ImagingState of the Art and Beyond, October 30  November 2, 2012, HumboldtUniversität zu Berlin, November 2, 2012.

S. Becker, Image processing via orientation scores, Workshop ``Computational Inverse Problems'', October 23  26, 2012, Mathematisches Forschungsinstitut Oberwolfach, October 25, 2012.

S. Becker, Revisiting: Propagationseparation approach for local likelihood estimation, PreMoLab: MoscowBerlinStochastic and Predictive Modeling, May 29  June 1, 2012, Russian Academy of Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Moscow, May 31, 2012.

K. Tabelow, Adaptive methods for noise reduction in diffusion weighted MRI  Position orientation adaptive smoothing (POAS), University College London, Wellcome Trust Centre for Neuroimaging, UK, November 1, 2012.

K. Tabelow, Functional magnetic resonance imaging: Estimation and signal detection, PreMoLab: MoscowBerlin Stochastic and Predictive Modeling, May 31  June 1, 2012, Russian Academy of Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Moscow, May 31, 2012.

K. Tabelow, Positionorientation adaptive smoothing (POAS) diffusion weighted imaging data, Workshop on Neurogeometry, November 15  17, 2012, Masaryk University, Department of Mathematics and Statistics, Brno, Czech Republic, November 16, 2012.

J. Polzehl, Adaptive methods for noise reduction in diffusion weighted MR, BRIC Seminar Series, University of North Carolina, School of Medicine, Chapel Hill, NC, USA, July 10, 2012.

J. Polzehl, Medical image analysis in R (tutorial), The 8th International R User Conference (Use R!2012), June 11  15, 2012, Vanderbilt University, Department of Biostatics, Nashville, TN, USA, June 12, 2012.

J. Polzehl, Modeling dMRI data: An introduction from a statistical viewpoint, Workshop on Neurogeometry, November 15  17, 2012, Masaryk University, Department of Mathematics and Statistics, Brno, Czech Republic, November 16, 2012.

J. Polzehl, Statistical issues in diffusion weighted MR (dMRI), PreMoLab: MoscowBerlin Stochastic and Predictive Modeling, May 31  June 1, 2012, Russian Academy of Sciences, Institute for Information Transmission Problems (Kharkevich Institute), Moscow, May 31, 2012.

K. Tabelow, S. Keller , S. Mohammadi, H. Kugel, J.S. Gerdes, J. Polzehl, M. Deppe, Structural adaptive smoothing increases sensitivity of DTI to detect microstructure alterations, 17th Annual Meeting of the Organization on Human Brain Mapping (HBM 2011), Quebec City, Canada, June 26  30, 2011.

K. Tabelow, H. Voss, J. Polzehl , Package dti: A framework for HARDI modeling in R, 17th Annual Meeting of the Organization on Human Brain Mapping (HBM 2011), Quebec City, Canada, June 26  30, 2011.

K. Tabelow, H. Voss, J. Polzehl , Structural adaptive smoothing methods for fMRI and its implementation in R, 17th Annual Meeting of the Organization on Human Brain Mapping (HBM 2011), Quebec City, Canada, June 26  30, 2011.

K. Tabelow, B. Whitcher, J. Polzehl, Performing tasks in medical imaging with R, 17th Annual Meeting of the Organization on Human Brain Mapping (HBM 2011), Quebec City, Canada, June 26  30, 2011.

K. Tabelow, Diffusion weighted imaging (DTI and beyond) using dti, The R User Conference 2011, August 15  18, 2011, University of Warwick, Department of Statistics, Coventry, UK, August 15, 2011.

K. Tabelow, Functional MRI using fmri, The R User Conference 2011, August 15  18, 2011, University of Warwick, Department of Statistics, Coventry, UK, August 15, 2011.

K. Tabelow, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, Cornell University, New York, Weill Medical College, USA, June 23, 2011.

K. Tabelow, Statistical parametric maps for functional MRI experiments in R: The package fmri, The R User Conference 2011, August 15  18, 2011, University of Warwick, Department of Statistics, Coventry, UK, August 18, 2011.

K. Tabelow, Structural adaptive smoothing fMRI and DTI data, SFB Research Center ``Mathematical Optimization and Applications in Biomedical Sciences'', KarlFranzensUniversität Graz, Institut für Mathematik und Wissenschaftliches Rechnen, Austria, June 8, 2011.

K. Tabelow, Structural adaptive smoothing fMRI and DTI data, Maastricht University, Faculty of Psychology and Neuroscience, Netherlands, September 28, 2011.

J. Polzehl, Statistical issues in modeling diffusion weighted magnetic resonance data, 3rd International Conference on Statistics and Probability 2011 (IMSChina), July 8  11, 2011, Institute of Mathematical Statistics, Xian, China, July 10, 2011.

J. Polzehl, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, Workshop on Statistics and Neuroimaging 2011, November 23  25, 2011, WIAS, November 24, 2011.

K. Tabelow, J.D. Clayden, P. Lafaye DE Micheaux, J. Polzehl, V.J. Schmid, B. Whitcher, Image analysis and statistical inference in NeuroImaging with R., Human Brain Mapping 2010, Barcelona, Spain, June 6  10, 2010.

K. Tabelow, J. Polzehl, S. Mohammadi, M. Deppe, Impact of smoothing on the interpretation of FA maps, Human Brain Mapping 2010, Barcelona, Spain, June 6  10, 2010.

K. Tabelow, Structural adaptive smoothing fMRI and DTI data, Workshop on Novel Reconstruction Strategies in NMR and MRI 2010, September 9  11, 2010, GeorgAugustUniversität Göttingen, Fakultät für Mathematik und Informatik, September 11, 2010.

J. Polzehl, K. Tabelow, Image and signal processing in the biomedical sciences: Diffusionweighted imaging modeling and beyond, 1st Annual Scientific Symposium ``Ultrahigh Field Magnetic Resonance'', Max Delbrück Center, Berlin, April 16, 2010.

J. Polzehl, Medical image analysis for structural and functional MRI, The R User Conference 2010, July 20  23, 2010, National Institute of Standards and Technology (NIST), Gaithersburg, USA, July 20, 2010.

J. Polzehl, Statistical issues in accessing brain functionality and anatomy, The R User Conference 2010, July 20  23, 2010, National Institute of Standards and Technology (NIST), Gaithersburg, USA, July 22, 2010.

J. Polzehl, Statistical problems in functional and diffusion weighted magnetic resonance, Uppsala University, Dept. of Mathematics, Graduate School in Mathematics and Computing, Sweden, May 27, 2010.

J. Polzehl, Structural adaptive smoothing in neuroscience applications, Statistische Woche Nürnberg 2010, September 14  17, 2010, FriedrichAlexanderUniversität ErlangenNürnberg, Naturwissenschaftliche Fakultät, September 16, 2010.

V. Spokoiny, Local parametric estimation, October 18  22, 2010, École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI), Rennes, France.

V. Spokoiny, Semidefinite nonGaussian component analysis, Bivariate Penalty Choice in Model Selection, Deutsches Diabetes Zentrum Düsseldorf, June 17, 2010.

K. Tabelow, J. Polzehl, H.U. Voss, Structural adaptive smoothing methods for highresolution fMRI, 15th Annual Meeting of the Organization for Human Brain Mapping (HBM 2009), San Francisco, USA, June 18  22, 2009.

K. Tabelow, A3  Image and signal processing in the biomedical sciences: diffusion weighted imaging  modeling and beyond, Center Days 2009 (DFG Research Center scshape Matheon), March 30  April 1, 2009, Technische Universität Berlin, March 30, 2009.

K. Tabelow, Structural adaptive methods in fMRI and DTI, Biomedical Imaging Research Seminar Series, Weill Cornell Medical College, Department of Radiology & Citigroup Biomedical Imaging Center, New York, USA, June 25, 2009.

K. Tabelow, Structural adaptive methods in fMRI and DTI, Memorial SloanKettering Cancer Center, New York, USA, June 25, 2009.

K. Tabelow, Structural adaptive smoothing in fMRI and DTI, Workshop on Recent Developments in fMRI Analysis Methods, Bernstein Center for Computational Neuroscience Berlin, January 23, 2009.

J. Polzehl, K. Tabelow, Structural adaptive smoothing diffusion tensor imaging data: The Rpackage dti, 15th Annual Meeting of the Organization for Human Brain Mapping (HBM 2009), San Francisco, USA, June 18  22, 2009.

N. Serdyukova, Local parametric estimation under noise misspecification in regression problem, Workshop on structure adapting methods, November 6  8, 2009, WIAS, November 7, 2009.

V. Spokoiny, Adaptive local parametric estimation, Université Joseph Fourier Grenoble I, Équipe de Statistique et Modélisation Stochastique, Laboratoire Jean Kuntzmann, France, February 26, 2009.

V. Spokoiny, Adaptive local parametric methods in imaging, Technische Universität Kaiserslautern, Fachbereich Mathematik, January 23, 2009.

V. Spokoiny, Modern nonparametric statistics (block lecture), October 2  13, 2009, École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI), Rennes, France.

V. Spokoiny, Modern nonparametric statistics (block lecture), October 18  29, 2009, Yale University, New Haven, USA.

V. Spokoiny, Modern nonparametric statistics (block lecture), January 13  16, 2009, École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI), Rennes, France.

V. Spokoiny, Parameter tuning in statistical inverse problem, European Meeting of Statisticians (EMS2009), July 20  22, 2009, Université Paul Sabatier, Toulouse, France, July 21, 2009.

V. Spokoiny, Saddle point model selection, Université Toulouse 1 Capitole, Toulouse School of Economics, France, November 24, 2009.

V. Spokoiny, Saddle point model selection, Workshop on structure adapting methods, November 6  8, 2009, WIAS, November 7, 2009.

V. Spokoiny, Sparse nonGaussian component analysis, Workshop ``Sparse Recovery Problems in High Dimensions: Statistical Inference and Learning Theory'', March 15  21, 2009, Mathematisches Forschungsinstitut Oberwolfach, March 16, 2009.

K. Tabelow, A3  Image and signal processing in medicine and biosciences, Center Days 2008 (DFG Research Center scshape Matheon), April 7  9, 2008, Technische Universität Berlin, April 7, 2008.

K. Tabelow, Structure adaptive smoothing medical images, 22. Treffpunkt Medizintechnik: Fortschritte in der medizinischen Bildgebung, Charité, Campus Virchow Klinikum Berlin, May 22, 2008.

K. Tabelow, Strukturadaptive Bild und Signalverarbeitung, Workshop of scshape Matheon with Siemens AG (Health Care Sector) in cooperation with Center of Knowledge Interchange (CKI) of Technische Universität (TU) Berlin and Siemens AG, TU Berlin, July 8, 2008.

J. Polzehl, New developments in structural adaptive smoothing: Images, fMRI and DWI, University of Tromsoe, Norway, May 27, 2008.

J. Polzehl, Smoothing fMRI and DWI data using the propagationseparation approach, University of Utah, Computing and Scientific Imaging Institute, Salt Lake City, USA, September 11, 2008.

J. Polzehl, Structural adaptive smoothing in diffusion tensor imaging, Workshop on ``Locally Adaptive Filters in Signal and Image Processing'', November 24  26, 2008, EURANDOM, Eindhoven, Netherlands, November 25, 2008.

J. Polzehl, Structural adaptive smoothing using the propagationseparation approach, University of Chicago, Department of Statistics, USA, September 3, 2008.

K. Tabelow, J. Polzehl, H.U. Voss, Increasing SNR in high resolution fMRI by spatially adaptive smoothing, Human Brain Mapping Conference 2007, Chicago, USA, June 10  14, 2007.

K. Tabelow, J. Polzehl, H.U. Voss, Reducing the number of necessary diffusion gradients by adaptive smoothing, Human Brain Mapping Conference 2007, Chicago, USA, June 10  14, 2007.

K. Tabelow, A3: Image and signal processing in medicine and biosciences, ADay des sc Matheon, KonradZuseZentrum für Informationstechnik Berlin (ZIB), December 5, 2007.

K. Tabelow, Improving data quality in fMRI and DTI by structural adaptive smoothing, Cornell University, Weill Medical College, New York, USA, June 18, 2007.

K. Tabelow, Structural adaptive signal detection in fMRI and structure enhancement in DTI, International Workshop on Image Analysis in the Life Sciences, Theory and Applications, February 28  March 2, 2007, Johannes Kepler Universität Linz, Austria, March 2, 2007.

K. Tabelow, Structural adaptive smoothing in medical imaging, Seminar ``Visualisierung und Datenanalyse'', KonradZuseZentrum für Informationstechnik Berlin (ZIB), January 30, 2007.

J. Polzehl, Propagationseparation procedures for image processing, International Workshop on Image Analysis in the Life Sciences, Theory and Applications, February 28  March 2, 2007, Johannes Kepler Universität Linz, Austria, March 2, 2007.

J. Polzehl, Structural adaptive smoothing in imaging problems, Spring Seminar Series, University of Minnesota, School of Statistics, College of Liberal Arts, USA, May 24, 2007.

J. Polzehl, Structural adaptive smoothing procedures by propagationseparation methods, Final meeting of the DFG Priority Program 1114, November 7  9, 2007, Freiburg, November 7, 2007.

K. Tabelow, J. Polzehl, H.U. Voss, V. Spokoiny, Analyzing fMRI experiments with structural adaptive smoothing methods, Human Brain Mapping Conference, Florence, Italy, June 12  15, 2006.

K. Tabelow, J. Polzehl, V. Spokoiny, J.P. Dyke, L.A. Heier, H.U. Voss, Accurate localization of functional brain activity using structure adaptive smoothing, ISMRM 14th Scientific Meeting & Exhibition, Seattle, USA, May 10  14, 2006.

K. Tabelow, Analyzing fMRI experiments with structural adaptive smoothing methods, BCCN PhD Symposium 2006, June 7  8, 2006, Bernstein Center for Computational Neuroscience Berlin, Bad Liebenwalde, June 8, 2006.

K. Tabelow, Image and signal processing in medicine and biosciences, Evaluation Colloquium of the DFG Research Center sc Matheon, Berlin, January 24  25, 2006.

J. Polzehl, Structural adaptive smoothing by propagationseparation, 69th Annual Meeting of the IMS and 5th International Symposium on Probability and its Applications, July 30  August 4, 2006, Rio de Janeiro, Brazil, July 30, 2006.

K. Tabelow, J. Polzehl, Structure adaptive smoothing procedures in medical imaging, 19. Treffpunkt Medizintechnik ``Imaging und optische Technologien für die Medizin'', Berlin, June 1, 2005.

K. Tabelow, Adaptive weights smoothing in the analysis of fMRI data, LudwigMaximiliansUniversität München, SFB 386, December 8, 2005.

K. Tabelow, Detecting shape and borders of activation areas infMRI data, Forschungsseminar ''Mathematische Statistik'', WIAS, Berlin, November 23, 2005.

K. Tabelow, Spatially adaptive smoothing infMRI analysis, Neuroimaging Center, Cahrité, Berlin, November 10, 2005.

J. Polzehl, Adaptive smoothing by propagationseparation, Australian National University, Center of Mathematics and its Applications, Canberra, March 31, 2005.

J. Polzehl, Image reconstruction and edge enhancement by structural adaptive smoothing, 55th Session of the International Statistical Institute (ISI), April 5  12, 2005, Sydney, Australia, April 8, 2005.

J. Polzehl, Propagationseparation at work: Main ideas and applications, National University of Singapore, Department of Probability Theory and Statistics, March 24, 2005.

J. Polzehl, Spatially adaptive smoothing: A propagationseparation approach for imaging problems, Joint Statistical Meetings, August 7  11, 2005, Minneapolis, USA, August 11, 2005.

J. Polzehl, Structural adaptive smoothing by propagationseparation methods, LudwigMaximiliansUniversität München, SFB 386, December 7, 2005.

J. Polzehl, Local likelihood modeling by structural adaptive smoothing, University of Minnesota, School of Statistics, Minneapolis, USA, September 9, 2004.

J. Polzehl, Smoothing by adaptive weights: An overview, Chalmers University of Technology, Department of Mathematical Statistics, Gothenburg, Sweden, May 11, 2004.

J. Polzehl, Structural adaptive smoothing methods, GeorgAugustUniversität Göttingen, Institut für Mathematische Stochastik, January 14, 2004.

J. Polzehl, Structural adaptive smoothing methods, TandemWorkshop on Nonlinear Optimization at the Crossover of Discrete Geometry and Numerical Analysis, July 15  16, 2004, Technische Universität Berlin, Institut für Mathematik, July 15, 2004.

J. Polzehl, Structural adaptive smoothing methods and possible applications in imaging, Charité Berlin, NeuroImaging Center, Berlin, July 1, 2004.

J. Polzehl, Structural adaptive smoothing methods for imaging problems, Annual Conference of Deutsche MathematikerVereinigung (DMV), September 13  17, 2004, Heidelberg, September 14, 2004.

J. Polzehl, Structural adaptive smoothing methods for imaging problems, GermanIsraeli Binational Workshop, October 20  22, 2004, Ollendorff Minerva Center for Vision and Image Sciences, Technion, Haifa, Israel, October 21, 2004.

A. Hutt, J. Polzehl, Spatial adaptive signal detection in fMRT, Human Brain Mapping Conference, New York, USA, June 17  22, 2003.

J. Polzehl, Adaptive smoothing procedures for image processing, Workshop on Nonlinear Analysis of Multidimensional Signals, February 25  28, 2003, Teistungenburg, February 25, 2003.

J. Polzehl, Image processing using Adaptive Weights Smoothing, Uppsala University, Department of Mathematics, Sweden, May 7, 2003.

J. Polzehl, Local likelihood modeling by Adaptive Weights Smoothing, Joint Statistical Meetings, August 3  7, 2003, San Francisco, USA, August 6, 2003.

J. Polzehl, Local modeling by structural adaptation, The Art of Semiparametrics, October 19  21, 2003, Berlin, October 20, 2003.

J. Polzehl, Structural adaptive smoothing methods and applications in imaging, Magnetic Resonance Seminar, PhysikalischTechnische Bundesanstalt, March 13, 2003.

J. Polzehl, Structural adaptation I: Pointwise adaptive smoothing and imaging, University of Tromso, Department of Mathematics, Norway, April 11, 2002.

J. Polzehl, Structural adaptation I: Varying coefficient regression modeling by adaptive weights smoothing, Workshop on Nonparametric Smoothing in Complex Statistical Models, April 27  May 4, 2002, Ascona, Switzerland, April 30, 2002.

J. Polzehl, Structural adaptation methods in imaging, Joint Statistical Meetings 2002, August 11  15, 2002, New York, USA, August 12, 2002.

J. Polzehl, Structural adaptive smoothing and its applications in imaging and time series, Uppsala University, Department of Mathematics, Sweden, May 2, 2002.

J. Polzehl, Structural adaptive estimation, Bayer AG, Leverkusen, November 29, 2001.

J. Polzehl, Adaptive weights smoothing with applications in imaging, Universität Essen, Fachbereich Mathematik, Sfb 475, November 6, 2000.

J. Polzehl, Adaptive weights smoothing with applications to image denoising and signal detection, Université Catholique de LouvainlaNeuve, Institut de Statistique, Belgium, September 29, 2000.

J. Polzehl, Functional and dynamic Magnet Resonance Imaging using adaptive weights smoothing, Workshop "`Mathematical Methods in Brain Mapping"', Université de Montréal, Centre de Recherches Mathématiques, Canada, December 11, 2000.

J. Polzehl, Spatially adaptive procedures for signal detection in fMRI, Tagung "`Controlling Complexity for Strong Stochastic Dependencies"', September 10  16, 2000, Mathematisches Forschungsinstitut Oberwolfach, September 11, 2000.

J. Polzehl, Spatially adaptive smoothing techniques for signal detection in functional and dynamic Magnet Resonance Imaging, Human Brain Mapping 2000, San Antonio, Texas, USA, June 12  16, 2000.

J. Polzehl, Spatially adaptive smoothing techniques for signal detection in functional and dynamic Magnet Resonance Imaging, MEDICA 2000, Düsseldorf, November 22  25, 2000.
External Preprints

A. Kroshnin, D. Dvinskikh, P. Dvurechensky, N. Tupitsa, C. Uribe, On the complexity of approximating Wasserstein barycenter, Preprint no. arXiv:1901.08686, Cornell University Library, arXiv.org, 2019.

F. Stonyakin, A. Gasnikov, A. Tyurin, D. Pasechnyuk, A. Agafonov, P. Dvurechensky, D. Dvinskikh, A. Kroshnin, V. Piskunova, Inexact Model: A framework for optimization and variational inequalities, Preprint no. arXiv:1902.00990, Cornell University Library, arXiv.org, 2019.

F. Stonyakin, D. Dvinskikh, P. Dvurechensky, A. Kroshnin, O. Kuznetsova, A. Agafonov, A. Gasnikov, A. Tyurin, C.A. Uribe, D. Pasechnyuk, S. Artamonov, Gradient methods for problems with inexact model of the objective, Preprint no. arXiv:1902.09001, Cornell University Library, arXiv.org, 2019.

D. Dvinskikh, E. Gorbunov, A. Gasnikov, P. Dvurechensky, C.A. Uribe, On dual approach for distributed stochastic convex optimization over networks, Preprint no. arXiv:1903.09844, Cornell University Library, arXiv.org, 2019.
Abstract
We introduce dual stochastic gradient oracle methods for distributed stochastic convex optimization problems over networks. We estimate the complexity of the proposed method in terms of probability of large deviations. This analysis is based on a new technique that allows to bound the distance between the iteration sequence and the solution point. By the proper choice of batch size, we can guarantee that this distance equals (up to a constant) to the distance between the starting point and the solution.