The packages are developed at the Weierstrass Institute for Applied Analysis and Stochastics within the projects "A3 - Image and signal processing in medicine and biosciences" (2005-2010) and "F10 - Image and signal processing in the biomedical sciences" (2010-2014) of the DFG Research Center MATHEON. The packages implement new methodology for adaptive data processing in imaging and neurosciences.

# WIAS software collection for Imaging

## Package "adimpro" for R

This packages provides functions for structure adaptive smoothing of digital images. This includes I/O functions for several image formats (including RAW), which relies on ImageMagick, image analysis and processing tools.

**Authors:** Karsten Tabelow (Maintainer), Joerg Polzehl

**Reference, including documentation:**

J. Polzehl, K. Tabelow. Adaptive smoothing of digital images: The R package adimpro., Journal of Statistical Software **19(1)**, (2007).

**Download:** freely available from CRAN server under GPL.

## The AWS for AMIRA (TM) plugin

The AWS for AMIRA (TM) plugin implements a structural adaptive smoothing procedure for two- and three- dimensional images in the visualization software AMIRA (TM). It is available in the Zuse Institute Berlin's version of the software for research purposes (http://amira.zib.de/).

# WIAS software collection for Neuroscience

## Package "fmri" for R

The R-package "fmri" provides functions for analyzing single run fmri data with structure adaptive smoothing procedure. This includes I/O function for ANALYZE, AFNI, or DICOM files, linear modelling with hemodynamic response functions, signal detection using Random Field Theory. Additionally, the structural adaptive segmentation method from Polzehl et al. 2010 is implemented.

**Authors:** Karsten Tabelow (Maintainer), Joerg Polzehl with contributions by Devy Hoffmann

**References:**

K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny. Analyzing
fMRI experiments with structural adaptive smoothing procedures., NeuroImage **33(1)**, pp. 55-62 (2006).

J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation for statistical parametric mapping, NeuroImage, **52(2)** pp. 515--523 (2010) .

**Documentation:**

J. Polzehl, K. Tabelow. Analyzing fMRI experiments with the fmri package in R. Version 1.0 - A users guide. WIAS-Technical Report No. 10 (2006)

J. Polzehl, K. Tabelow. *fmri: A package for analyzing fmri data*, RNews **7(2)** 13-17 (2007).

K. Tabelow, J. Polzehl. Statistical parametric maps for functional MRI experiments in R: The package fmri., Journal of Statistical Software **44(11)**, (2011).

**Download:** freely available from CRAN or NITRC server under GPL.

## Package "dti" for R

The package contains tools for the analysis of diffusion-weighted magnetic resonance imaging data (dMRI). It can be used to read dMRI data, to estimate the diffusion tensor, for the adaptive smoothing of dMRI data, the estimation of orientation density functions or its square root, the estimation of tensor mixture models, the estimation of the diffusion kurtosis model, fiber tracking, and for the two- and three-dimensional visualization of the results.

**Authors:** Karsten Tabelow (Maintainer), Joerg Polzehl with contributions by Felix Anker

**Reference:**

K. Tabelow, J. Polzehl, V. Spokoiny, and H.U. Voss.
Diffusion Tensor Imaging: Structural adaptive smoothing, NeuroImage **39(4)**, pp. 1763-1773 (2008).

S. Becker, K. Tabelow, H.U. Voss, A. Anwander, R.M. Heidemann, J. Polzehl, Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS), Med. Image Anal., **16(1)** pp. 200--211 (2012)

K. Tabelow, H.U. Voss, J. Polzehl, Modeling the orientation distribution function by mixtures of angular central Gaussian distributions, J. Neurosci. Meth., **203** pp. 200--211 (2012).

S. Becker, K. Tabelow, S. Mohammadi, N. Weiskopf, J. Polzehl, Adaptive smoothing of multi-shell diffusion-weighted magnetic resonance data by msPOAS, NeuroImage **95**, pp. 90--105 (2014).

K. Tabelow, H.U. Voss, and J. Polzehl, Local estimation of the noise level in MRI using structural adaptation, Medical Image Analysis **20**, pp. 76--86 (2015)

**Documentation:**

J. Polzehl, K. Tabelow. Structural adaptive smoothing in diffusion tensor imaging: The R package dti, Journal of Statistical Software **31(9)**, (2011).

J. Polzehl, K. Tabelow, Beyond the Gaussian model in diffussion-weighted imaging: The package dti, Journal of Statistical Software **44(12)** (2011).

**Download:** freely available from CRAN or NITRC server under GPL.

## ACID-Toolbox for SPM

The ACID-Toolbox contains a number of tools for Artifact Correction in dMRI. It contains an implementation of msPOAS in Matlab. It can be used withing the neuroimaging software SPM.

**Reference:**

K. Tabelow, S. Mohammadi, N. Weiskopf, and J. Polzehl (2015), POAS4SPM --- A toolbox for SPM to denoise diffusion MRI data, Neuroinformatics **13** pp. 19-29 (2015).

## aws4SPM

This is a toolbox for SPM that implements structural adaptive smoothing for fMRI in SPM. Its main page can be found here. The package is currently under reconstruction as it uses some ugly hacks to enter SPM's internal mechanism.

# Note

The packages come with absolutely NO WARRANTY! It is not intended for any purpose! It is especially not intended for any clinical use, but for evaluation purpose only.

Karsten Tabelow Last modified: Mon Feb 09 14:11:00 CET 2015