eprintid: 1392608
rev_number: 52
eprint_status: archive
userid: 608
dir: disk0/01/39/26/08
datestamp: 2013-05-07 18:39:00
lastmod: 2021-09-19 22:55:10
status_changed: 2014-01-22 15:40:53
type: proceedings_section
metadata_visibility: show
item_issues_count: 0
creators_name: Ferizi, U
creators_name: Schneider, T
creators_name: Panagiotaki, E
creators_name: Nedjati-Gilani, G
creators_name: Zhang, H
creators_name: Wheeler-Kingshott, CAM
creators_name: Alexander, DC
creators_name: IEEE, 
title: Ranking diffusion-MRI models with in-vivo human brain data
ispublished: pub
divisions: UCL
divisions: B02
divisions: C07
divisions: D07
divisions: F87
divisions: B04
divisions: C05
divisions: F48
keywords: Diffusion MRI, Brain Imaging
note: © 2013 IEEE. This is the authors' accepted manuscript of this published article.
abstract: Diffusion MRI microstructure imaging provides a unique non-invasive probe into the microstructure of biological tissue. Its analysis relies on mathematical models relating microscopic tissue features to the MR signal. This work aims to determine which compartment models of diffusion MRI are best at describing the signal from in-vivo brain white matter. Recent work shows that three compartment models, including restricted intra-axonal, glial compartments and hindered extra-cellular diffusion, explain best multi b-value data sets from fixed rat brain tissue. Here, we perform a similar experiment using in-vivo human data. We compare one, two and three compartment models, ranking them with standard model selection criteria. Results show that, as with fixed tissue, three compartment models explain the data best, although simpler models emerge for the in-vivo data. We also find that splitting the scanning into shorter sessions has little effect on the models fitting and that the results are reproducible. The full ranking assists the choice of model and imaging protocol for future microstructure imaging applications in the brain.
date: 2013
publisher: IEEE
official_url: http://dx.doi.org/10.1109/ISBI.2013.6556565
vfaculties: VFBRS
vfaculties: VENG
vfaculties: VENG
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_source: WoS-Lite
elements_id: 869068
doi: 10.1109/ISBI.2013.6556565
isbn_13: 978-1-4673-6455-3
lyricists_name: Alexander, Daniel
lyricists_name: Ferizi, Uran
lyricists_name: Nedjati-Gilani, Gemma
lyricists_name: Panagiotaki, Eleftheria
lyricists_name: Schneider, Torben
lyricists_name: Wheeler-Kingshott, Claudia
lyricists_name: Zhang, Hui
lyricists_id: DALEX06
lyricists_id: UFERI10
lyricists_id: GLMOR82
lyricists_id: EPANA29
lyricists_id: TSCHN54
lyricists_id: CWHEE14
lyricists_id: HZHAN50
full_text_status: public
pagerange: 676 - 679
event_title: 10th International Symposium on Biomedical Imaging (ISBI)
issn: 1945-7928
book_title: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI)
citation:        Ferizi, U;    Schneider, T;    Panagiotaki, E;    Nedjati-Gilani, G;    Zhang, H;    Wheeler-Kingshott, CAM;    Alexander, DC;           Ferizi, U;  Schneider, T;  Panagiotaki, E;  Nedjati-Gilani, G;  Zhang, H;  Wheeler-Kingshott, CAM;  Alexander, DC;  IEEE;   - view fewer <#>    (2013)    Ranking diffusion-MRI models with in-vivo human brain data.                     In:  2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI).  (pp. 676 - 679).  IEEE       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1392608/1/Ferizi_Alexander_IEEE.pdf