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Published in: Neuroinformatics 3/2016

Open Access 01-07-2016 | Software Original Article

NiftyFit: a Software Package for Multi-parametric Model-Fitting of 4D Magnetic Resonance Imaging Data

Authors: Andrew Melbourne, Nicolas Toussaint, David Owen, Ivor Simpson, Thanasis Anthopoulos, Enrico De Vita, David Atkinson, Sebastien Ourselin

Published in: Neuroinformatics | Issue 3/2016

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Abstract

Multi-modal, multi-parametric Magnetic Resonance (MR) Imaging is becoming an increasingly sophisticated tool for neuroimaging. The relationships between parameters estimated from different individual MR modalities have the potential to transform our understanding of brain function, structure, development and disease. This article describes a new software package for such multi-contrast Magnetic Resonance Imaging that provides a unified model-fitting framework. We describe model-fitting functionality for Arterial Spin Labeled MRI, T1 Relaxometry, T2 relaxometry and Diffusion Weighted imaging, providing command line documentation to generate the figures in the manuscript. Software and data (using the nifti file format) used in this article are simultaneously provided for download. We also present some extended applications of the joint model fitting framework applied to diffusion weighted imaging and T2 relaxometry, in order to both improve parameter estimation in these models and generate new parameters that link different MR modalities. NiftyFit is intended as a clear and open-source educational release so that the user may adapt and develop their own functionality as they require.
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Metadata
Title
NiftyFit: a Software Package for Multi-parametric Model-Fitting of 4D Magnetic Resonance Imaging Data
Authors
Andrew Melbourne
Nicolas Toussaint
David Owen
Ivor Simpson
Thanasis Anthopoulos
Enrico De Vita
David Atkinson
Sebastien Ourselin
Publication date
01-07-2016
Publisher
Springer US
Published in
Neuroinformatics / Issue 3/2016
Print ISSN: 1539-2791
Electronic ISSN: 1559-0089
DOI
https://doi.org/10.1007/s12021-016-9297-6

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