Predicting dynamic, motion‐related changes in B 0 field in the brain at a 7T MRI using a subject‐specific fine‐trained U‐net

• Published in: Magnetic resonance in medicine Vol. 91; no. 5; pp. 2044 - 2056
• Main Authors: Motyka, Stanislav, Weiser, Paul, Bachrata, Beata, Hingerl, Lukas, Strasser, Bernhard, Hangel, Gilbert, Niess, Eva, Niess, Fabian, Zaitsev, Maxim, Robinson, Simon Daniel, Langs, Georg, Trattnig, Siegfried, Bogner, Wolfgang
• Format: Journal Article
• Language: English
• Published: United States 01.05.2024
• Subjects: Brain - diagnostic imaging; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Motion; Movement; Retrospective Studies; artificial neural network; deep learning; magnetic resonance imaging; U-net; patient movement; motion correction; B0 inhomogeneities
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.29980

Subject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B ), which is a prerequisite for high quality data. Thus, characterization of changes to B , for example induced by patient movement, is impo...