Oct 15, 2021 · In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL- ...
Apr 14, 2020 · The generation of QSM requires solving a challenging ill-posed field-to-source inversion problem. Inaccurate field-to-source inversion often ...
Oct 15, 2021 · In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL- ...
Jan 21, 2021 · In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL- ...
May 1, 2024 · A novel deep learning-based method that combines physical model guidance with a mini-U-net architecture in an unsupervised manner, for Quantitative ...
May 1, 2024 · We propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior).
Oct 21, 2020 · We present a model-based DL method, denoted as uQSM. Without accessing to QSM labels, uQSM is trained using the well-established physical model.
Model-Based Learning for Quantitative Susceptibility Mapping
www.semanticscholar.org › paper › Mod...
Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map.
(PDF) MoG-QSM: Model-based Generative Adversarial Deep ...
www.researchgate.net › publication › 34...
Sep 8, 2024 · Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases.
Mar 31, 2021 · This paper proposes data augmentation to improve QSM-quantification through Deep Learning networks. The aim is to improve reconstruction in ...