Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/20680
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dc.contributor.authorFazlollahi, Amir-
dc.contributor.authorAyton, Scott-
dc.contributor.authorBourgeat, Pierrick-
dc.contributor.authorDiouf, Ibrahima-
dc.contributor.authorRaniga, Parnesh-
dc.contributor.authorFripp, Jurgen-
dc.contributor.authorDoecke, James-
dc.contributor.authorAmes, David-
dc.contributor.authorMasters, Colin L-
dc.contributor.authorRowe, Christopher C-
dc.date2018-09-26-
dc.date.accessioned2019-04-26T00:28:54Z-
dc.date.available2019-04-26T00:28:54Z-
dc.date.issued2018-09-
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/20680-
dc.description.abstractThe quantitative use of medical images often requires an intensity scaling with respect to the signal from a well-characterized anatomical region of interest. The choice of such a region often varies between studies which can substantially influence the quantification, resulting in study bias hampering objective findings which are detrimental to open science. This study outlines a list of criteria and a statistical ranking approach for identifying normalization region of interest. The proposed criteria include (i) associations between reference region and demographics such as age, (ii) diagnostic group differences in the reference region, (iii) correlation between reference and primary areas of interest, (iv) local variance in the reference region, and (v) longitudinal reproducibility of the target regions when normalized. The proposed approach has been used to establish an optimal normalization region of interest for the analysis of Quantitative Susceptibility Mapping (QSM) of Magnetic Resonance Imaging (MRI). This was achieved by using cross-sectional data from 119 subjects with normal cognition, mild cognitive impairment, and Alzheimer’s disease as well as and 19 healthy elderly individuals with longitudinal data. For the QSM application, we found that normalizing by the white matter regions not only satisfies the criteria but it also provides the best separation between clinical groups for deep brain nuclei target regions.en_US
dc.subjectQuantificationen_US
dc.subjectReference regionen_US
dc.subjectNormalizationen_US
dc.subjectQSMen_US
dc.titleA Framework to Objectively Identify Reference Regions for Normalizing Quantitative Imagingen_US
dc.typeConferenceen_US
dc.identifier.affiliationCSIRO Health and Biosecurity, Brisbane, Australiaen_US
dc.identifier.affiliationAustin Health, Heidelberg, Victoria, Australiaen
dc.identifier.affiliationCooperative Research Centre for Mental Health, Parkville, Australiaen_US
dc.identifier.affiliationFlorey Institute of Neuroscience and Mental Health, Parkville, Australiaen_US
dc.identifier.affiliationThe University of Melbourne, Parkville, Australiaen_US
dc.identifier.affiliationAustin Health, Heidelberg, Victoria, Australiaen_US
dc.description.conferencenameMICCAI: International Conference on Medical Image Computing and Computer-Assisted Interventionen_US
dc.description.conferencelocationGranada, Spainen_US
dc.identifier.doi10.1007/978-3-030-00928-1_8en_US
dc.type.contentTexten_US
dc.identifier.orcid0000-0003-3910-2453en_US
dc.description.conferencenumber21en_US
dc.type.austinConference Presentationen_US
local.name.researcherMasters, Colin L
item.cerifentitytypePublications-
item.openairetypeConference-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
crisitem.author.deptThe Florey Institute of Neuroscience and Mental Health-
crisitem.author.deptMolecular Imaging and Therapy-
Appears in Collections:Journal articles
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