Basic MR sequence parameters systematically bias automated brain volume estimation.
Introduction: Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typical...
| Publicado en: | Neuroradiology Vol. 58; no. 11; pp. 1153 - 1161 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=119456277&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119456277 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Nov2016 vid: 58 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 119456277 119456277 144068368 119456277 10.1007/s00234-016-1737-3 119456277 ppf: 1153 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Basic MR sequence parameters systematically bias automated brain volume estimation. aug: au: Haller, Sven Falkovskiy, Pavel Meuli, Reto Thiran, Jean-Philippe Krueger, Gunnar Lovblad, Karl-Olof Kober, Tobias Roche, Alexis Marechal, Bénédicte affil: Department of Radiology , University Hospital (CHUV) , Lausanne Switzerland sug: subj: Gray Matter Alzheimer's Disease Brain Image Enhancement Magnetic Resonance Imaging Female Data Analysis, Statistical Data Analysis Descriptive Statistics Reliability Female ab: Introduction: Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results. Methods: The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL− 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters. Results: Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % ( p < 0.001) in cortical gray matter and 4.16 % ( p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % ( p < 0.05) in cortical gray matter. Conclusion: A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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