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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Vydáno v:	Medical image analysis Ročník 95; s. 103173
Hlavní autoři:	Zhang, Ming, Feng, Ruimin, Li, Zhenghao, Feng, Jie, Wu, Qing, Zhang, Zhiyong, Ma, Chengxin, Wu, Jinsong, Yan, Fuhua, Liu, Chunlei, Zhang, Yuyao, Wei, Hongjiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.07.2024
Témata:	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
On-line přístup:	Získat plný text
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