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dc.contributor.author
Willers, Corin
dc.contributor.author
Bauman, Grzegorz
dc.contributor.author
Andermatt, Simon
dc.contributor.author
Santini, Francesco
dc.contributor.author
Sandkühler, Robin
dc.contributor.author
Ramsey, Kathryn A.
dc.contributor.author
Cattin, Philippe C.
dc.contributor.author
Bieri, Oliver
dc.contributor.author
Pusterla, Orso
dc.contributor.author
Latzin, Philipp
dc.date.accessioned
2020-11-13T08:13:59Z
dc.date.available
2020-09-20T04:17:48Z
dc.date.available
2020-09-22T09:38:25Z
dc.date.available
2020-11-13T08:13:59Z
dc.date.issued
2021-02
dc.identifier.issn
0740-3194
dc.identifier.issn
1522-2594
dc.identifier.other
10.1002/mrm.28476
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/441329
dc.description.abstract
Purpose To investigate the repeatability and reproducibility of lung segmentation and their impact on the quantitative outcomes from functional pulmonary MRI. Additionally, to validate an artificial neural network (ANN) to accelerate whole-lung quantification. Method Ten healthy children and 25 children with cystic fibrosis underwent matrix pencil decomposition MRI (MP-MRI). Impaired relative fractional ventilation (R-FV) and relative perfusion (R-Q) from MP-MRI were compared using whole-lung segmentation performed by a physician at two time-points (A(t1)and A(t2)), by an MRI technician (B), and by an ANN (C). Repeatability and reproducibility were assess with Dice similarity coefficient (DSC), paired t-test and Intraclass-correlation coefficient (ICC). Results The repeatability within an observer (A(t1)vs A(t2)) resulted in a DSC of 0.94 +/- 0.01 (mean +/- SD) and an unsystematic difference of -0.01% for R-FV(P= .92) and +0.1% for R-Q(P= .21). The reproducibility between human observers (A(t1)vs B) resulted in a DSC of 0.88 +/- 0.02, and a systematic absolute difference of -0.81% (P< .001) for R(FV)and -0.38% (P= .037) for R-Q. The reproducibility between human and the ANN (A(t1)vs C) resulted in a DSC of 0.89 +/- 0.03 and a systematic absolute difference of -0.36% for R-FV(P= .017) and -0.35% for R-Q(P= .002). The ICC was >0.98 for all variables and comparisons. Conclusions Despite high overall agreement, there were systematic differences in lung segmentation between observers. This needs to be considered for longitudinal studies and could be overcome by using an ANN, which performs as good as human observers and fully automatizes MP-MRI post-processing.
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.subject
automated segmentation
en_US
dc.subject
functional lung MRI
en_US
dc.subject
inter‐reader reproducibility
en_US
dc.subject
neural networks
en_US
dc.subject
pediatrics
en_US
dc.title
The impact of segmentation on whole-lung functional MRI quantification: Repeatability and reproducibility from multiple human observers and an artificial neural network
en_US
dc.type
Journal Article
dc.date.published
2020-09-06
ethz.journal.title
Magnetic Resonance in Medicine
ethz.journal.volume
85
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
Magn Reson Med
ethz.pages.start
1079
en_US
ethz.pages.end
1092
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2020-09-20T04:17:53Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-11-13T08:14:12Z
ethz.rosetta.lastUpdated
2022-03-29T04:01:26Z
ethz.rosetta.versionExported
true
ethz.COinS
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