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000286380 1001_ $$aSchuppert, Christopher$$b0
000286380 245__ $$aAutomated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study.
000286380 260__ $$a[London]$$bMacmillan Publishers Limited, part of Springer Nature$$c2023
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000286380 520__ $$aIn 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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000286380 7001_ $$aRospleszcz, Susanne$$b1
000286380 7001_ $$aHirsch, Jochen G$$b2
000286380 7001_ $$aHoinkiss, Daniel C$$b3
000286380 7001_ $$aKöhn, Alexander$$b4
000286380 7001_ $$avon Krüchten, Ricarda$$b5
000286380 7001_ $$aRusse, Maximilian F$$b6
000286380 7001_ $$aKeil, Thomas$$b7
000286380 7001_ $$aKrist, Lilian$$b8
000286380 7001_ $$aSchmidt, Börge$$b9
000286380 7001_ $$aMichels, Karin B$$b10
000286380 7001_ $$aSchipf, Sabine$$b11
000286380 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b12$$udkfz
000286380 7001_ $$aKröncke, Thomas J$$b13
000286380 7001_ $$aPischon, Tobias$$b14
000286380 7001_ $$aNiendorf, Thoralf$$b15
000286380 7001_ $$aSchulz-Menger, Jeanette$$b16
000286380 7001_ $$aForsting, Michael$$b17
000286380 7001_ $$aVölzke, Henry$$b18
000286380 7001_ $$aHosten, Norbert$$b19
000286380 7001_ $$aBülow, Robin$$b20
000286380 7001_ $$aZaitsev, Maxim$$b21
000286380 7001_ $$aKauczor, Hans-Ulrich$$b22
000286380 7001_ $$aBamberg, Fabian$$b23
000286380 7001_ $$aGünther, Matthias$$b24
000286380 7001_ $$aSchlett, Christopher L$$b25
000286380 773__ $$0PERI:(DE-600)2615211-3$$a10.1038/s41598-023-49569-1$$gVol. 13, no. 1, p. 22745$$n1$$p22745$$tScientific reports$$v13$$x2045-2322$$y2023
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