Your browser doesn't support javascript.
loading
Smart forecasting of artifacts in contrast-enhanced breast MRI before contrast agent administration.
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.
  • Liebert A; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. andrzej.liebert@uk-erlangen.de.
  • Das BK; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Kapsner LA; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Eberle J; Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.
  • Skwierawska D; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Folle L; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Schreiter H; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Laun FB; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Ohlmeyer S; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Uder M; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Wenkel E; Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Bickelhaupt S; Medizinische Fakultät, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Eur Radiol ; 34(7): 4752-4763, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38099964
ABSTRACT

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 is necessary to investigate the clinical value of such smart personalized imaging approaches.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Imagen por Resonancia Magnética / Artefactos / Medios de Contraste Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Imagen por Resonancia Magnética / Artefactos / Medios de Contraste Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Año: 2024 Tipo del documento: Article