Paper Info
Title
NiftyFit: a Software Package for Multi-parametric Model-Fitting of 4D Magnetic Resonance Imaging Data.
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.
Year
DOI
Venue
2016
10.1007/s12021-016-9297-6
Neuroinformatics
Keywords
Field
DocType
Cerebral blood flow,Diffusion,MRI,Relaxometry,g-ratio
File format,Computer vision,Data mining,Diffusion MRI,Parametric model,Computer science,Software,Artificial intelligence,Neuroimaging,Estimation theory,Relaxometry,Magnetic resonance imaging
Journal
Volume
Issue
ISSN
14
3
1559-0089
Citations 
PageRank 
References 
4
0.83
14
Authors
8
Name
Order
Citations
PageRank
Andrew Melbourne113618.05
Nicolas Toussaint2579.19
David Owen372.08
Ivor J. A. Simpson41569.37
Thanasis Anthopoulos540.83
Enrico De Vita6193.68
David Atkinson7676.89
Sébastien Ourselin82499237.61