Smart forecasting of artifacts in contrast-enhanced breast MRI before contrast agent administration

• Published in: European radiology 2024-07, Vol.34 (7), p.4752-4763
• Main Authors: Liebert, Andrzej, Das, Badhan K., Kapsner, Lorenz A., Eberle, Jessica, Skwierawska, Dominika, Folle, Lukas, Schreiter, Hannes, Laun, Frederik B., Ohlmeyer, Sabine, Uder, Michael, Wenkel, Evelyn, Bickelhaupt, Sebastian
• Format: Article
• Language: English
• Subjects: Algorithms; Breast; Contrast agents; Contrast media; Customization; Diagnostic Radiology; Forecasting; Gadolinium; Image acquisition; Imaging; Injection; Internal Medicine; Interventional Radiology; Intravenous administration; Magnetic resonance imaging; Medicine; Medicine & Public Health; Neural networks; Neuroradiology; Performance assessment; Radiology; Test sets; Ultrasound; Workflow
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-10469-7

Objectives To evaluate whether artifacts on contrast-enhanced (CE) breast MRI maximum intensity projections (MIPs) might already be forecast before gadolinium-based contrast agent (GBCA) administration during an ongoing examination by analyzing the unenhanced T1-weighted images acquired before the GBCA injection. Materials and methods This IRB-approved retrospective analysis consisted of n  = 2884 breast CE MRI examinations after intravenous administration of GBCA, acquired with n  = 4 different MRI devices at different field strengths (1.5 T/3 T) during clinical routine. CE-derived subtraction MIPs were used to conduct a multi-class multi-reader evaluation of the presence and severity of artifacts with three independent readers. An ensemble classifier (EC) of five DenseNet models was used to predict artifacts for the post-contrast subtraction MIPs, giving as the input source only the pre-contrast T1-weighted sequence. Thus, the acquisition directly preceded the GBCA injection. The area under ROC (AuROC) and diagnostics accuracy scores were used to assess the performance of the neural network in an independent holdout test set ( n  = 285). Results After majority voting, potentially significant artifacts were detected in 53.6% ( n  = 1521) of all breast MRI examinations (age 49.6 ± 12.6 years). In the holdout test set (mean age 49.7 ± 11.8 years), at a specificity level of 89%, the EC could forecast around one-third of artifacts (sensitivity 31%) before GBCA administration, with an AuROC = 0.66. Conclusion This study demonstrates the capability of a neural network to forecast the occurrence of artifacts on CE subtraction data before the GBCA administration. If confirmed in larger studies, this might enable a workflow-blended approach to prevent breast MRI artifacts by implementing in-scan personalized predictive algorithms. Clinical relevance statement Some artifacts in contrast-enhanced breast MRI maximum intensity projections might be predictable before gadolinium-based contrast agent injection using a neural network. Key Points • Potentially significant artifacts can be observed in a relevant proportion of breast MRI subtraction sequences after gadolinium-based contrast agent administration (GBCA). • Forecasting the occurrence of such artifacts in subtraction maximum intensity projections before GBCA administration for individual patients was feasible at 89% specificity, which allowed correctly predicting one in three future artifacts. • Further research