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