Rapid automated liver quantitative susceptibility mapping.

Feasibility Studies Humans Image Interpretation, Computer-Assisted / methods Imaging, Three-Dimensional / methods Iron / analysis Iron Overload / diagnostic imaging Liver / diagnostic imaging Magnetic Resonance Imaging / methods Prospective Studies Reproducibility of Results
in-phase echoes liver iron overload quantitative susceptibility mapping water/fat separation

Journal

Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850

Informations de publication

Date de publication:
09 2019
Historique:
received: 15 05 2018
revised: 09 12 2018
accepted: 11 12 2018
pubmed: 15 1 2019
medline: 22 10 2020
entrez: 15 1 2019
Statut: ppublish

Résumé

Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC. To develop a rapid, robust, and automated liver QSM for clinical practice. Prospective. 13 healthy subjects and 22 patients. 1.5 T and 3 T/3D multiecho gradient-recalled echo (GRE) sequence. Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and region of interest (ROI) measurements. Reproducibility of QSM, R Liver QSM using the IP method was found to be ~5.5 times faster than SPURS (P < 0.05) in initializing T Use of IP echo-based initialization enables robust water/fat separation and field estimation for automated, rapid, and reproducible liver QSM for clinical applications. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:725-732.

Sections du résumé

BACKGROUND
Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC.
PURPOSE
To develop a rapid, robust, and automated liver QSM for clinical practice.
STUDY TYPE
Prospective.
POPULATION
13 healthy subjects and 22 patients.
FIELD STRENGTH/SEQUENCES
1.5 T and 3 T/3D multiecho gradient-recalled echo (GRE) sequence.
ASSESSMENT
Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T
STATISTICAL TESTS
IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and region of interest (ROI) measurements. Reproducibility of QSM, R
RESULTS
Liver QSM using the IP method was found to be ~5.5 times faster than SPURS (P < 0.05) in initializing T
DATA CONCLUSION
Use of IP echo-based initialization enables robust water/fat separation and field estimation for automated, rapid, and reproducible liver QSM for clinical applications.
LEVEL OF EVIDENCE
1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:725-732.

Identifiants

DOI: 10.1002/jmri.26632 PMID: 30637892 PMC: PMC6929208
pubmed: 30637892
doi: 10.1002/jmri.26632
pmc: PMC6929208
mid: NIHMS1063503
doi:

Substances chimiques

Iron E1UOL152H7

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

725-732

Subventions

Organisme : NINDS NIH HHS
ID : R01 NS095562
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS090464
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK116126
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA181566
Pays : United States

Informations de copyright

© 2019 International Society for Magnetic Resonance in Medicine.

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Auteurs

Ramin Jafari (R)

Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

Sujit Sheth (S)

Department of Pediatrics, Weill Medical College of Cornell University, New York, New York, USA.

Pascal Spincemaille (P)

Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

Thanh D Nguyen (TD)

Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

Martin R Prince (MR)

Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.
ORCID: 0000-0002-9883-0584

Yan Wen (Y)

Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

Yihao Guo (Y)

Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.
School of Biomedical Engineering, Southern Medical University, Guangzhou, China.

Kofi Deh (K)

Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

Zhe Liu (Z)

Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

Daniel Margolis (D)

Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

Gary M Brittenham (GM)

Department of Pediatrics, Columbia University, New York, New York, USA.

Andrea S Kierans (AS)

Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

Yi Wang (Y)

Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.

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Classifications MeSH

questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études de faisabilité › Rapid automated liver quantitative...
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questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Traitement d'image par ordinateur › Imagerie tridimensionnelle › Rapid automated liver quantitative...
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