5 papers
Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization
Heejun Shin, Taehee Kim, Jongho Lee +3
Cone beam computed tomography (CBCT) is an emerging medical imaging technique to visualize the internal anatomical structures of patients. During a CBCT scan, several projection im…
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…
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR
Dongmyung Shin, Sooyeon Ji, Doohee Lee +3
A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as DeepRF_SLR, is designed to minimize the peak ampl…
Artificial neural network for myelin water imaging
Jieun Lee, Doohee Lee, Joon Yul Choi +3
Purpose: To demonstrate the application of artificial-neural-network (ANN) for real-time processing of myelin water imaging (MWI). Methods: Three neural networks, ANN-IMWF, ANN-IGM…