Deep learning–guided weighted averaging for signal dropout compensation in DWI of the liver

Purpose To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion. Methods Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed that aims to suppress the contribution of image regions...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Magnetic resonance in medicine 2022-12, Vol.88 (6), p.2679-2693
Hauptverfasser:	Gadjimuradov, Fasil, Benkert, Thomas, Nickel, Marcel Dominik, Führes, Tobit, Saake, Marc, Maier, Andreas
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms cardiac motion artifact Compensation Deep learning Dropouts DWI Evaluation Image detection Image processing Liver liver MRI liver oncology Machine learning Noise reduction
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!