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WIAS R-packages for imaging

The packages are developed at the Weierstrass Institute for Applied Analysis and Stochastics within the Project "A3 - Image and signal processing in medicine and biosciences" of the DFG Research Center MATHEON.

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.

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

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

Download: CRAN, which may contain a newer version, or fmri_1.4-0.tar.gz, or fmri_1.4-0.zip

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.

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

Download: CRAN, which may contain a newer version, or adimpro_0.7.3.tar.gz, or adimpro_0.7.3.zip

Package "dti" for R

This package provides a structure adaptive smoothing procedure for Diffusion Tensor Imaging (DTI).

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

Documentation: J. Polzehl, K. Tabelow. Structural adaptive smoothing in diffusion tensor imaging: The R package dti, WIAS-Preprint No. 1382 (2008).

Download: CRAN, which may contain a newer version, or dti_0.9-0.tar.gz, or dti_0.9-0.zip

Package "PET" for R

This package implements different analytic/direct and iterative reconstruction methods of Peter Toft. It also offer the possibility to simulate PET data.

Download: CRAN, which may contain a newer version, or PET_0.4.6.tar.gz, or PET_0.4.6.zip

Note

The packeges 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.

General

The development of a bulk of medical imaging facilities has been a major advance in medicine and bioscience in the last one or two decades. It enables an in vivo examination of the human body by physicians and researchers with a plethora of applications in medical diagnostics. However, all imaging modalities suffer from significant noise, which may render subsequent analysis difficult. Interesting structures and signals may be weak, superposed by some irrelevant structure, and can hardly be detected. The situation is impaired by the fact that image reconstruction and signal detection is often a complicated multi-step procedure where the quality of the results is sensitive to the noise level at early steps and strongly depends on a correct modeling of the specific physics of the image acquisition process. Diagnostically important measures are often only derived from the acquired images. In several applications, the high dimensionality of the data requires a suitable dimension reduction technique.

Due to its simplicity and the existence of fast implementations the standard method for noise reduction in medical imaging is the application of non-adaptive filters like the Gaussian filter. However, this inherently comes with a significant blurring of important structures and a loss of spatial resolution and hence details. In applications like Diffusion Tensor Imaging where the structures of interest are small and highly anisotropic noise reduction is therefore still not part of the standard image analysis procedure giving away accuracy of the measurement.

A new general class of structural adaptive methods based on local parametric models have been recently developed. The approach called Adaptive Weights Smoothing (AWS) uses spatially adaptive smoothing to remove the noise without loosing the structural information. An important feature lies in its direct applicability to data in any dimension. The same idea has been used to develop new structural adaptive dimension reduction methods.

In many cases the analysis of medical imaging data in 3D or 4D via visual inspection by an expert is almost impossible. Data given as non-stationary and aperiodic sequences of images rather requires a spatiotemporal analysis. Thus, the development of new automatic methods and algorithms which enable automatic (pre)diagnostics, detection of structures, edge and pattern detection and recognition etc. is urgently required. Such methods should be able to reduce noise while preserving important structure like edges and homogeneous regions. These may occur at all scales especially at the limits of the increased spatial and temporal resolution.

Many imaging modalities may benifit from the development of advanced analyzing methods. Typical examples range range from functional Magnetic Resonance Imaging (fMRI), Diffusion Weighted Imaging (DWI), Positron-Emission Tomography (PET) to microbiological images produced by confocal microscopy and many others.

Literature

1. K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny. Analyzing fMRI experiments with structural adaptive smoothing procedures, NeuroImage 33(1), 55-62 (2006).
2. J. Polzehl, K. Tabelow. fmri: A package for analyzing fmri data, RNews 7(2), 13-17 (2007).
3. H.U. Voss, K. Tabelow, J. Polzehl, O. Tchernichovski, K. Maul, D. Salgado-Commissariat, D. Ballon, and 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 (PNAS) 104(25), 10667-10672 (2007).
4. K. Tabelow, J. Polzehl, A. M. Ulug, J. P. Dyke, R. Watts, L. A. Heier, and H. U. Voss. Accurate Localization of Brain Activity in Presurgical fMRI by Structure Adaptive Smoohting, IEEE Trans. Med. Imaging 27(4), 531-537 (2008).
5. D. Hoffmann, K. Tabelow. Structural adaptive smoothing for single-subject analysis in SPM: the aws4SPM-toolbox, WIAS Technical Report No. 11 (2008)
6. K. Tabelow, V. Piech, J. Polzehl, and H. U. Voss. High-resolution fMRI: Overcoming the signal-to-noise problem, Journal of Neuroscience Methods 178, 357-365 (2009).
7. J. Polzehl, K. Tabelow. Structural adaptive smoothing in diffusion tensor imaging: The R package dti., Journal of Statistical Software 31, (2009).
8. J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation for statistical parametric mapping, Preprint no. 1484, WIAS, Berlin, 2010.

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Karsten Tabelow

Last modified: Tue Feb 24 10:54:12 CET 2010