A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation

•Subject-specific and unsupervised deep learning for QSM reconstruction.•Integration of implicit continuous signal representation and explicit regularizations.•Phase compensation strategy for an accurate physical model.•Improved accuracy and quality compared with established methods. Quantitative su...

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Veröffentlicht in:	Medical image analysis Jg. 95; S. 103173
Hauptverfasser:	Zhang, Ming, Feng, Ruimin, Li, Zhenghao, Feng, Jie, Wu, Qing, Zhang, Zhiyong, Ma, Chengxin, Wu, Jinsong, Yan, Fuhua, Liu, Chunlei, Zhang, Yuyao, Wei, Hongjiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.07.2024
Schlagworte:	Implicit neural representation Phase compensation Quantitative susceptibility mapping Unsupervised learning Quantitative susceptibility mapping Implicit neural representation Unsupervised learning Phase compensation
ISSN:	1361-8415, 1361-8423, 1361-8423
Online-Zugang:	Volltext
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