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Schuppert, C.* ; Rospleszcz, S. ; Hirsch, J.G.* ; Hoinkiss, D.C.* ; Köhn, A.* ; von Krüchten, R.* ; Russe, M.F.* ; Keil, T.* ; Krist, L.* ; Schmidt, B.* ; Michels, K.B.* ; Schipf, S.* ; Brenner, H.* ; Kröncke, T.J.* ; Pischon, T.* ; Niendorf, T.* ; Schulz-Menger, J.* ; Forsting, M.* ; Völzke, H.* ; Hosten, N.* ; Bülow, R.* ; Zaitsev, M.A.* ; Kauczor, H.U.* ; Bamberg, F.* ; Günther, M.* ; Schlett, C.L.*

Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study.

Sci. Rep. 13:22745 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73-100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, 'structured noise maximum' was the strongest predictor for the technologists' choice, followed by 'N/2 ghosting average'. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists' choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Design
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Zeitschrift Scientific Reports
Quellenangaben Band: 13, Heft: 1, Seiten: , Artikelnummer: 22745 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-504000-010
Förderungen Helmholtz Association
Federal Ministry of Education and Research (BMBF)
Universittsklinikum Freiburg (8975)
DOI 10.1038/s41598-023-49569-1
WOS ID 001131702800003
Scopus ID 85180253339
PubMed ID 38123791
Erfassungsdatum 2024-01-09
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