eprintid: 1489639
rev_number: 33
eprint_status: archive
userid: 608
dir: disk0/01/48/96/39
datestamp: 2016-05-04 15:42:06
lastmod: 2021-09-19 23:31:32
status_changed: 2017-06-08 13:14:00
type: article
metadata_visibility: show
creators_name: Melbourne, A
creators_name: Toussaint, N
creators_name: Owen, D
creators_name: Simpson, I
creators_name: Anthopoulos, T
creators_name: De Vita, E
creators_name: Atkinson, D
creators_name: Ourselin, S
title: NiftyFit: a Software Package for Multi-parametric Model-Fitting of 4D Magnetic Resonance Imaging Data
ispublished: pub
divisions: UCL
divisions: B02
divisions: C10
divisions: D17
divisions: FI6
divisions: B04
divisions: C05
divisions: F42
keywords: Cerebral blood flow, Diffusion, MRI, Relaxometry, g-ratio
note: Copyright © The Author(s) 2016. This article is published with open access at Springerlink.com.
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.
date: 2016-07
date_type: published
official_url: http://doi.org/10.1007/s12021-016-9297-6
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Journal Article
verified: verified_manual
elements_id: 1119061
doi: 10.1007/s12021-016-9297-6
pii: 10.1007/s12021-016-9297-6
lyricists_name: Atkinson, David
lyricists_name: Melbourne, Andrew
lyricists_name: Ourselin, Sebastien
lyricists_name: Owen, David
lyricists_id: DATKI13
lyricists_id: AMMEL98
lyricists_id: SOURS59
lyricists_id: DOWEN23
actors_name: Melbourne, Andrew
actors_id: AMMEL98
actors_role: owner
full_text_status: public
publication: Neuroinformatics
volume: 14
number: 3
pagerange: 319-337
issn: 1559-0089
citation:        Melbourne, A;    Toussaint, N;    Owen, D;    Simpson, I;    Anthopoulos, T;    De Vita, E;    Atkinson, D;           Melbourne, A;  Toussaint, N;  Owen, D;  Simpson, I;  Anthopoulos, T;  De Vita, E;  Atkinson, D;  Ourselin, S;   - view fewer <#>    (2016)    NiftyFit: a Software Package for Multi-parametric Model-Fitting of 4D Magnetic Resonance Imaging Data.                   Neuroinformatics , 14  (3)   pp. 319-337.    10.1007/s12021-016-9297-6 <https://doi.org/10.1007/s12021-016-9297-6>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1489639/1/Melbourne_art%253A10.1007%252Fs12021-016-9297-6.pdf