001     178888
005     20240229143558.0
024 7 _ |a 10.1097/RLI.0000000000000861
|2 doi
024 7 _ |a pmid:35184102
|2 pmid
024 7 _ |a 0020-9996
|2 ISSN
024 7 _ |a 1536-0210
|2 ISSN
024 7 _ |a altmetric:129702050
|2 altmetric
037 _ _ |a DKFZ-2022-00323
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Schuppert, Christopher
|b 0
245 _ _ |a Whole-Body Magnetic Resonance Imaging in the Large Population-Based German National Cohort Study: Predictive Capability of Automated Image Quality Assessment for Protocol Repetitions.
260 _ _ |a [s.l.]
|c 2022
|b Ovid
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1655974354_11773
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2022 Jul 1;57(7):478-487
520 _ _ |a Reproducible image quality is of high relevance for large cohort studies and can be challenging for magnetic resonance imaging (MRI). Automated image quality assessment may contribute to conducting radiologic studies effectively.The aims of this study were to assess protocol repetition frequency in population-based whole-body MRI along with its effect on examination time and to examine the applicability of automated image quality assessment for predicting decision-making regarding repeated acquisitions.All participants enrolled in the prospective, multicenter German National Cohort (NAKO) study who underwent whole-body MRI at 1 of 5 sites from 2014 to 2016 were included in this analysis (n = 11,347). A standardized examination program of 12 protocols was used. Acquisitions were carried out by certified radiologic technologists, who were authorized to repeat protocols based on their visual perception of image quality. Eleven image quality parameters were derived fully automatically from the acquired images, and their discrimination ability regarding baseline acquisitions and repetitions was tested.At least 1 protocol was repeated in 12% (n = 1359) of participants, and more than 1 protocol in 1.6% (n = 181). The repetition frequency differed across protocols (P < 0.001), imaging sites (P < 0.001), and over the study period (P < 0.001). The mean total scan time was 62.6 minutes in participants without and 67.4 minutes in participants with protocol repetitions (mean difference, 4.8 minutes; 95% confidence interval, 4.5-5.2 minutes). Ten of the automatically derived image quality parameters were individually retrospectively predictive for the repetition of particular protocols; for instance, 'signal-to-noise ratio' alone provided an area under the curve of 0.65 (P < 0.001) for repetition of the Cardio Cine SSFP SAX protocol. Combinations generally improved prediction ability, as exemplified by 'image sharpness' plus 'foreground ratio' yielding an area under the curve of 0.89 (P < 0.001) for repetition of the Neuro T1w 3D MPRAGE protocol, versus 0.85 (P < 0.001) and 0.68 (P < 0.001) as individual parameters.Magnetic resonance imaging protocol repetitions were necessary in approximately 12% of scans even in the highly standardized setting of a large cohort study. Automated image quality assessment shows predictive value for the technologists' decision to perform protocol repetitions and has the potential to improve imaging efficiency.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
700 1 _ |a von Krüchten, Ricarda
|b 1
700 1 _ |a Hirsch, Jochen G
|b 2
700 1 _ |a Rospleszcz, Susanne
|b 3
700 1 _ |a Hoinkiss, Daniel C
|b 4
700 1 _ |a Selder, Sonja
|b 5
700 1 _ |a Köhn, Alexander
|b 6
700 1 _ |a von Stackelberg, Oyunbileg
|b 7
700 1 _ |a Peters, Annette
|b 8
700 1 _ |a Völzke, Henry
|b 9
700 1 _ |a Kröncke, Thomas
|b 10
700 1 _ |a Niendorf, Thoralf
|b 11
700 1 _ |a Forsting, Michael
|b 12
700 1 _ |a Hosten, Norbert
|b 13
700 1 _ |a Hendel, Thomas
|b 14
700 1 _ |a Pischon, Tobias
|b 15
700 1 _ |a Jöckel, Karl-Heinz
|b 16
700 1 _ |a Kaaks, Rudolf
|0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a
|b 17
|u dkfz
700 1 _ |a Bamberg, Fabian
|b 18
700 1 _ |a Kauczor, Hans-Ulrich
|b 19
700 1 _ |a Günther, Matthias
|b 20
700 1 _ |a Schlett, Christopher L
|b 21
700 1 _ |a Investigators, German National Cohort MRI Study
|b 22
|e Collaboration Author
773 _ _ |a 10.1097/RLI.0000000000000861
|g Vol. Publish Ahead of Print
|0 PERI:(DE-600)2041543-6
|n 7
|p 478-487
|t Investigative radiology
|v 57
|y 2022
|x 0020-9996
909 C O |p VDB
|o oai:inrepo02.dkfz.de:178888
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 17
|6 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2022
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-01-27
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-27
915 _ _ |a Allianz-Lizenz
|0 StatID:(DE-HGF)0410
|2 StatID
|d 2022-11-29
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2022-11-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2022-11-29
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b INVEST RADIOL : 2021
|d 2022-11-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-29
915 _ _ |a IF >= 10
|0 StatID:(DE-HGF)9910
|2 StatID
|b INVEST RADIOL : 2021
|d 2022-11-29
920 1 _ |0 I:(DE-He78)C020-20160331
|k C020
|l C020 Epidemiologie von Krebs
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C020-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21