eprintid: 10074390
rev_number: 33
eprint_status: archive
userid: 608
dir: disk0/10/07/43/90
datestamp: 2019-10-03 11:23:45
lastmod: 2021-09-19 22:56:51
status_changed: 2019-10-03 11:23:45
type: proceedings_section
metadata_visibility: show
creators_name: Hill, I
creators_name: Palombo, M
creators_name: Santin, MD
creators_name: Branzoli, F
creators_name: Philippe, A-C
creators_name: Wassermann, D
creators_name: Aigrot, M-S
creators_name: Stankoff, B
creators_name: Zhang, H
creators_name: Lehericy, S
creators_name: Petiet, A
creators_name: Alexander, D
creators_name: Ciccarelli, O
creators_name: Drobnjak, I
title: Deep neural network based framework for in-vivo axonal permeability estimation
ispublished: pub
divisions: UCL
divisions: B02
divisions: C07
divisions: D07
divisions: F87
divisions: B04
divisions: C05
divisions: F43
divisions: F48
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: This study introduces a novel framework for estimating permeability from diffusion-weighted MRI data using deep learning. Recent work introduced a random forest (RF) regressor model that outperforms approximate mathematical models (Kärger model). Motivated by recent developments in machine learning, we propose a deep neural network (NN) approach to estimate the permeability associated with the water residence time. We show in simulations and in in-vivo mouse brain data that the NN outperforms the RF method. We further show that the performance of either ML method is unaffected by the choice of training data, i.e. raw diffusion signals or signal-derived features yield the same results.
date: 2018-06-16
date_type: published
publisher: ISMRM (International Society for Magnetic Resonance in Medicine)
official_url: https://www.ismrm.org/18m/
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1658745
lyricists_name: Alexander, Daniel
lyricists_name: Ciccarelli, Olga
lyricists_name: Drobnjak, Ivana
lyricists_name: Hill, Ioana
lyricists_name: Palombo, Marco
lyricists_name: Zhang, Hui
lyricists_id: DALEX06
lyricists_id: OCICC52
lyricists_id: IDROB84
lyricists_id: IDOPR43
lyricists_id: MPALO05
lyricists_id: HZHAN50
actors_name: Palombo, Marco
actors_id: MPALO05
actors_role: owner
full_text_status: public
series: ISMRM (International Society for Magnetic Resonance in Medicine)
volume: 2018
place_of_pub: Concord, CA, USA
event_title: Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018, Paris, France
institution: ISMRM 26th Annual Meeting & Exhibition
book_title: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018
editors_name: Miller, KL
editors_name: Port, JD
citation:        Hill, I;    Palombo, M;    Santin, MD;    Branzoli, F;    Philippe, A-C;    Wassermann, D;    Aigrot, M-S;                             ... Drobnjak, I; + view all <#>        Hill, I;  Palombo, M;  Santin, MD;  Branzoli, F;  Philippe, A-C;  Wassermann, D;  Aigrot, M-S;  Stankoff, B;  Zhang, H;  Lehericy, S;  Petiet, A;  Alexander, D;  Ciccarelli, O;  Drobnjak, I;   - view fewer <#>    (2018)    Deep neural network based framework for in-vivo axonal permeability estimation.                     In: Miller, KL and Port, JD, (eds.) Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018.    ISMRM (International Society for Magnetic Resonance in Medicine): Concord, CA, USA.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10074390/1/ISMRM_abstract_IoanaHill.pdf