30 citations · 39 across the 6 of their papers we have counts for
4 papers · 1 filter
Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction
Jaejin Cho, Borjan Gagoski, Taehyung Kim +4
Purpose: To propose a wave-encoded model-based deep learning (wave-MoDL) strategy for highly accelerated 3D imaging and joint multi-contrast image reconstruction, and further exten…
Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning
Daniel Polak, Itthi Chatnuntawech, Jaeyeon Yoon +6
We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-t…
Highly Accelerated Multishot EPI through Synergistic Machine Learning and Joint Reconstruction
Berkin Bilgic, Itthi Chatnuntawech, Mary Kate Manhard +7
Purpose: To introduce a combined machine learning (ML) and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imagin…
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
Jaeyeon Yoon, Enhao Gong, Itthi Chatnuntawech +10
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction. In this work, an MRI reconstruction algorithm, which is referred to as qua…