37 citations · 50 across the 5 of their papers we have counts for
5 papers
Predicting before Reconstruction: A generative prior framework for MRI acceleration
Juhyung Park, Rokgi Hong, Roh-Eul Yoo +4
Recent advancements in artificial intelligence have created transformative capabilities in image synthesis and generation, enabling diverse research fields to innovate at revolutio…
Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations
Juhyung Park, Dongwon Park, Sooyeon Ji +3
Deep learning-based denoising methods have shown powerful results for improving the signal-to-noise ratio of magnetic resonance (MR) images, mostly by leveraging supervised learnin…
Deep reinforcement learning-designed radiofrequency waveform in MRI
Dongmyung Shin, Younghoon Kim, Chungseok Oh +4
Carefully engineered radiofrequency (RF) pulses play a key role in a number of systems such as mobile phone, radar, and magnetic resonance imaging. The design of an RF waveform, ho…
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…
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…