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dc.contributor.authorBrand, Jonathan F.
dc.contributor.authorFurenlid, Lars R.
dc.contributor.authorAltbach, Maria I.
dc.contributor.authorGalons, Jean-Philippe
dc.contributor.authorBhattacharyya, Achyut
dc.contributor.authorSharma, Puneet
dc.contributor.authorBhattacharyya, Tulshi
dc.contributor.authorBilgin, Ali
dc.contributor.authorMartin, Diego R.
dc.date.accessioned2017-02-02T00:56:48Z
dc.date.available2017-02-02T00:56:48Z
dc.date.issued2016-07-21
dc.identifier.citationTask-based optimization of flip angle for fibrosis detection in T1-weighted MRI of liver 2016, 3 (3):035502 Journal of Medical Imagingen
dc.identifier.issn2329-4302
dc.identifier.pmid27446971
dc.identifier.doi10.1117/1.JMI.3.3.035502
dc.identifier.urihttp://hdl.handle.net/10150/622346
dc.description.abstractChronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. The current reference standard for diagnosing HF is biopsy followed by pathologist examination; however, this is limited by sampling error and carries a risk of complications. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically in the order of 1 to 5 mm, which approximates the resolution limit of in vivo gadolinium-enhanced magnetic resonance imaging in the delayed phase. We use MRI of formalin-fixed human ex vivo liver samples as phantoms that mimic the textural contrast of in vivo Gd-MRI. We have developed a local texture analysis that is applied to phantom images, and the results are used to train model observers to detect HF. The performance of the observer is assessed with the area-under-the-receiver-operator-characteristic curve (AUROC) as the figure-of-merit. To optimize the MRI pulse sequence, phantoms were scanned with multiple times at a range of flip angles. The flip angle that was associated with the highest AUROC was chosen as optimal for the task of detecting HF. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
dc.description.sponsorshipNIBIB NIH HHS [T32 EB000809]en
dc.language.isoenen
dc.publisherSPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERSen
dc.relation.urlhttp://medicalimaging.spiedigitallibrary.org/article.aspx?doi=10.1117/1.JMI.3.3.035502en
dc.rights© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.en
dc.subjecthotelling observeren
dc.subjectmagnetic resonance imagingen
dc.subjectMRIen
dc.subjecthepatic fibrosisen
dc.subjectliveren
dc.subjectoptimizationen
dc.subjecttexture analysisen
dc.titleTask-based optimization of flip angle for fibrosis detection in T1-weighted MRI of liveren
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Coll Opt Scien
dc.contributor.departmentUniv Arizona, Coll Med, Dept Med Imagingen
dc.contributor.departmentUniv Arizona, Coll Med, Dept Patholen
dc.identifier.journalJournal of Medical Imagingen
dc.description.noteSPIE grants to authors of papers published in an SPIE Journal or Proceedings the right to post an author-prepared version or an official version (preferred version) of the published paper on an internal or external server controlled exclusively by the author/employer, provided that (a) such posting is noncommercial in nature and the paper is made available to users without charge; (b) an appropriate copyright notice and full citation appear with the paper, and (c) a link to SPIE's official online version of the abstract is provided using the DOI (Document Object Identifier) link.en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionUniversity of Arizona, College of Optical Sciences, 1630 East University Boulevard, Tucson, Arizona 85719, United States
dc.contributor.institutionUniversity of Arizona, College of Optical Sciences, 1630 East University Boulevard, Tucson, Arizona 85719, United StatesbUniversity of Arizona, College of Medicine, Department of Medical Imaging, P.O. Box 245067 Tucson, Arizona 85724-5067, United States
dc.contributor.institutionUniversity of Arizona, College of Medicine, Department of Medical Imaging, P.O. Box 245067 Tucson, Arizona 85724-5067, United States
dc.contributor.institutionUniversity of Arizona, College of Medicine, Department of Medical Imaging, P.O. Box 245067 Tucson, Arizona 85724-5067, United States
dc.contributor.institutionUniversity of Arizona, College of Medicine, Department of Pathology, 1501 North Campbell Avenue, Tucson, Arizona 85724, United States
dc.contributor.institutionUniversity of Arizona, College of Medicine, Department of Medical Imaging, P.O. Box 245067 Tucson, Arizona 85724-5067, United States
dc.contributor.institutionUniversity of Arizona, College of Medicine, Department of Pathology, 1501 North Campbell Avenue, Tucson, Arizona 85724, United States
dc.contributor.institutionUniversity of Arizona, College of Medicine, Department of Medical Imaging, P.O. Box 245067 Tucson, Arizona 85724-5067, United States
dc.contributor.institutionUniversity of Arizona, College of Medicine, Department of Medical Imaging, P.O. Box 245067 Tucson, Arizona 85724-5067, United States
refterms.dateFOA2018-09-11T17:20:04Z
html.description.abstractChronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. The current reference standard for diagnosing HF is biopsy followed by pathologist examination; however, this is limited by sampling error and carries a risk of complications. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically in the order of 1 to 5 mm, which approximates the resolution limit of in vivo gadolinium-enhanced magnetic resonance imaging in the delayed phase. We use MRI of formalin-fixed human ex vivo liver samples as phantoms that mimic the textural contrast of in vivo Gd-MRI. We have developed a local texture analysis that is applied to phantom images, and the results are used to train model observers to detect HF. The performance of the observer is assessed with the area-under-the-receiver-operator-characteristic curve (AUROC) as the figure-of-merit. To optimize the MRI pulse sequence, phantoms were scanned with multiple times at a range of flip angles. The flip angle that was associated with the highest AUROC was chosen as optimal for the task of detecting HF. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.


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