5 citations · 8 across the 6 of their papers we have counts for
8 papers · 1 filter
BlindHarmony: "Blind" Harmonization for MR Images via Flow model
Hwihun Jeong, Heejoon Byun, Dong Un Kang +1
In MRI, images of the same contrast (e.g., T) from the same subject can exhibit noticeable differences when acquired using different hardware, sequences, or scan parameters. Th…
DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues
Juhung Park, Woojin Jung, Eun-Jung Choi +4
In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient schem…
A geometric approach to separate the effects of magnetic susceptibility and chemical shift/exchange in a phantom with isotropic magnetic susceptibility
Hyunsung Eun, Hwihun Jeong, Jingu Lee +2
Purpose: To separate the effects of magnetic susceptibility and chemical shift/exchange in a phantom with isotropic magnetic susceptibility. To generate a chemical shift/exchange-c…
DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE
Hongjun An, Hyeong-Geol Shin, Sooyoen Ji +5
Respiration-induced B fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at…
Overview of quantitative susceptibility mapping using deep learning -- Current status, challenges and opportunities
Woojin Jung, Steffen Bollmann, Jongho Lee
Quantitative susceptibility mapping (QSM) has gained broad interests in the field by extracting biological tissue properties, predominantly myelin, iron and calcium from magnetic r…
Exploring linearity of deep neural network trained QSM: QSMnet+
Woojin Jung, Jaeyeon Yoon, Joon Yul Choi +4
Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill conditioned dipole inversion in QSM and generated high-quality s…