Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany.
Chair of Epidemiology, Institute of Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians University, Faculty of Medicine, Munich, Germany.
Sci Rep. 2023 Dec 20;13(1):22745. doi: 10.1038/s41598-023-49569-1.
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.
在磁共振成像（MRI）中，对图像质量不佳的感知可能会促使重复进行相应的图像采集协议。随后，从一系列采集数据中选择首选的高质量图像数据可能具有挑战性。自动化工作流程可以促进和改善这种选择。因此，我们旨在研究自动图像质量评估在预测主观首选图像采集方面的适用性。我们的分析包括了来自 11347 名参与者的数据，这些参与者进行了全身 MRI 检查，作为正在进行的前瞻性德国国家队列（NAKO）研究的一部分。受过训练的放射技师因诱导的设置错误和/或主观上不满意的图像质量而重复任何 12 个检查协议，并从产生的系列中选择首选采集。从所有采集自动衍生了多达 11 个定量图像质量参数。计算了正则化回归和标准诊断准确性估计。在 2342 个由两个或更多采集组成的系列中控制设置变化，在保留初始设置的 1396 个系列中的 1116 个系列中，技术人员更喜欢重复采集而不是初始采集（79.9%，各协议范围内：73-100%）。然后，在选择和丢弃采集之间，图像质量参数通常显示出统计学上的显著差异。在所有协议的正则化回归中，“结构噪声最大值”是技术人员选择的最强预测因子，其次是“N/2 重影平均值”。在预测技术人员的选择方面，自动衍生参数的组合提供了 0.51 到 0.74 之间的 ROC 曲线下面积。结论是，尽管协议和解剖区域之间存在相当大的性能差异，自动图像质量评估仍然可以为识别一系列 MRI 采集的主观偏好做出重大贡献，并为读者提供有效的决策支持。
