6 citations · 33 across the 13 of their papers we have counts for
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Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction
Juan Liu, Kevin M. Koch
Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue magnetic susceptibility and reveal pathology. Current deep-learning-based approaches to solve the QSM i…
Deep Quantitative Susceptibility Mapping for Background Field Removal and Total Field Inversion
Juan Liu, Kevin M. Koch
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to estimate local tissue susceptibility, which has been shown useful to provide novel image contrast and as biom…
MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks
Juan Liu, Kevin M. Koch
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer estimates of local tissue magnetism (magnetic susceptibility), which has been shown useful to provide n…
Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion
Juan Liu, Kevin M. Koch
Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by ill conditioning of its field-to -susceptibility transformation. State-of-art QS…
Quantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution
Juan Liu, Andrew S. Nencka, L. Tugan Muftuler +3
Purpose: Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by poor conditioning of the field to susceptibility transformation. State-…
Build-A-FLAIR: Synthetic T2-FLAIR Contrast Generation through Physics Informed Deep Learning
Andrew S. Nencka, Andrew Klein, Kevin M. Koch +7
Purpose: Magnetic resonance imaging (MRI) exams include multiple series with varying contrast and redundant information. For instance, T2-FLAIR contrast is based upon tissue T2 dec…