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

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Item Type:Article
Title:Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study
Creators Name:Schuppert, C. and Rospleszcz, S. and Hirsch, J.G. and Hoinkiss, D.C. and Köhn, A. and von Krüchten, R. and Russe, M.F. and Keil, T. and Krist, L. and Schmidt, B. and Michels, K.B. and Schipf, S. and Brenner, H. and Kröncke, T.J. and Pischon, T. and Niendorf, T. and Schulz-Menger, J. and Forsting, M. and Völzke, H. and Hosten, N. and Bülow, R. and Zaitsev, M. and Kauczor, H.U. and Bamberg, F. and Günther, M. and Schlett, C.L.
Abstract: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.
Keywords:Cohort Studies, Longitudinal Studies, Magnetic Resonance Imaging, Prospective Studies, ROC Curve
Source:Scientific Reports
ISSN:2045-2322
Publisher:Nature Publishing Group
Volume:13
Number:1
Page Range:22745
Date:20 December 2023
Official Publication:https://doi.org/10.1038/s41598-023-49569-1
PubMed:View item in PubMed

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