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Allows diffusion of imaging research in a clinical oncology setting and provides tools for end-to-end diffusion image analysis as well as interoperation with clinical imaging systems. SlicerDMRI is a suite of open-source software tools for diffusion magnetic resonance imaging (dMRI) research. The software is built upon and integrated with 3D Slicer, an NIH-supported open-source platform for medical image computing. SlicerDMRI is used for both neuroscience research and cancer imaging research.
Dipy / Diffusion Imaging in Python
Allows to study diffusion Magnetic Resonance Imaging (MRI) data. Dipy is a program allowing users to share their code and experiments. One of its objectives is to provide transparent implementations for all the different steps of the dMRI analysis with a uniform programming interface. It implements two interfaces for probabilistic Markov fiber tracking: (1) it allows the user to provide the distribution evaluated on a discrete set of possible tracking directions, and (2) it accommodates tracking methods where the fiber orientation distribution function (fODF) cannot be easily computed.
Amira 3D Software for Life Sciences
Allows users to visualize, manipulate, and understand data from imaging modalities such as computed tomography, microscopy or Magnetic resonance imaging (MRI). Amira 3D Software for Life Sciences provides features to import and process 2D and 3D images data, visualization techniques and tools for visual analysis. Users can also create and share presentations. The base product can be customized by adding functional extensions to fit special needs in different application areas.
A “profilometry” framework for the multimetric analysis of white matter tracts. Mrpipe is a statistical framework that combine a multivariate analysis of covariance (MANCOVA) and a linear discriminant analysis (LDA) both accounting for age and gender, with multiple comparison corrections. This methodology can not only combine tract profile analyses in a single statistical model but can also take into account covariates such as age and gender which are of particular importance in most clinical investigations. This project is still a skeleton, the tool is not ready for use.
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