Automated detection of motion artifacts in brain MR images using deep learning

Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning (DL) model to detect rigid motion in T1‐wei...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	NMR in biomedicine 2025-01, Vol.38 (1), p.e5276-n/a
Hauptverfasser:	Manso Jimeno, Marina, Ravi, Keerthi Sravan, Fung, Maggie, Oyekunle, Dotun, Ogbole, Godwin, Vaughan, John Thomas, Geethanath, Sairam
Format:	Artikel
Sprache:	eng
Schlagworte:	artifact detection Artificial neural networks automated quality assessment Automation Brain Brain - diagnostic imaging Classification Data acquisition Datasets Deep Learning explainable artificial intelligence Failure modes Humans Image acquisition Image Processing, Computer-Assisted - methods Image quality Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging Motion Motion perception Neural networks Neuroimaging Quality assessment Quality control Two dimensional analysis
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!