Dr. Joerg Polzehl - personal homepage

I am senior researcher in the research group Stochastic Algorithms and Nonparametric Statistics of the Weierstrass Institut for Applied Analysis and Stochastics. My current research interests are in statistical modeling, adaptive smoothing and dimension reduction methods with main applications in image processing and neuroscience.

I am author and co-author of several software packages for adaptive smoothing, dimension reduction, image processing and neuroscience applications within the framework of the R Environment for Statistical Computing:

aws - Adaptive Weights Smoothing
adimpro - Adaptive Smoothing of Digital Images (with K. Tabelow)
dti - dMRI Analysis (with K. Tabelow)
qMRI - Analysis of quantitative MRI Experiments (with K. Tabelow)
fmri - Analysis of fMRI Experiments (with K. Tabelow)
EDR - Effective dimension reduction in multi-index models

and the

ACID-Toolbox for Artifact Correction in dMRI for SPM (with S. Mohammadi, K. Tabelow, L. Ruthotto)

Most parts of my research on adaptive smoothing and modeling of MRI experiments are described in our book

J. Polzehl, K. Tabelow, Magnetic Resonance Brain Imaging: Modeling and Data Analysis using R, Series: Use R!, Springer International Publishing, Cham, 2019, 231 pages, DOI 10.1007/978-3-030-29184-6

R code of the examples and results generated from RMarkdown using knitr are provided on our

Book website

The 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 Multi-Parameter Mapping. The book concludes with extended appendices providing details of the non-parametric 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.