xQSM: quantitative susceptibility mapping with octave convolutional and noise‐regularized neural networks

Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill‐posed dipole inversion process. In this study, a new deep learning method for QSM reconstruct...

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Veröffentlicht in:	NMR in biomedicine 2021-03, Vol.34 (3), p.e4461-n/a
Hauptverfasser:	Gao, Yang, Zhu, Xuanyu, Moffat, Bradford A., Glarin, Rebecca, Wilman, Alan H., Pike, G. Bruce, Crozier, Stuart, Liu, Feng, Sun, Hongfu
Format:	Artikel
Sprache:	eng
Schlagworte:	Biological products Datasets Deep learning dipole inversion Dipoles Error analysis Globus pallidus Image contrast Image processing Image reconstruction In vivo methods and tests Inversion Linearity Magnetic resonance imaging Mapping Neural networks Noise noise regularization octave convolution QSM Regularization Substantia grisea Susceptibility Teaching methods xQSM
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