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Arbitrary Missing Contrast Generation Using Multi-Contrast Generative Network with An Encoder Network

Author:Geonhui Son  Yohan Jun  Sewon Kim  Dosik Hwang  Taejoon Eo*  

Session Type:Online Gather.town Pitches  

Session Date:Wednesday, 11 May 2022  

Topic:Module 14: Image Reconstruction  

Session Name:Image Reconstruction IV  

Program Number:4308  

Room Session:1  

Institution:Yonsei University  

Deep Learning-Based Reconstruction

Author:Dosik Hwang  

Session Type:Weekend Course  

Session Date:Sunday, 08 May 2022  

Topic:

Session Name:Image Reconstruction: Theory, Methods & Practical Considerations  

Program Number:

Room Session:N11 (Breakout A)  

Institution:Yonsei University  

Deep prior for suppressing noise amplification and edge preservation in Phase-based EPT with Low-SNR image

Author:Chuanjiang Cui  Jun-Hyeong Kim  Kyu-Jin Jung  Jaeuk Yi  Dong-Hyun Kim  

Session Type:Digital Poster  

Session Date:Thursday, 12 May 2022  

Topic:Module 23: MR Contrasts  

Session Name:Electromagnetic Properties & Oximetry  

Program Number:2914  

Room Session:Exhibition Hall:S8 & S9  

Institution:Yonsei University  

Gibbs-ringing Removal through Anti-aliased Deep Priors

Author:Jaeuk Yi  Chuanjiang Cui  Kyu-Jin Jung  Dong-Hyun Kim  

Session Type:Digital Poster  

Session Date:Monday, 09 May 2022  

Topic:Module 15: Data Acquisition & Artifacts  

Session Name:Motion & Artifacts II  

Program Number:0956  

Room Session:Exhibition Hall:S8 & S9  

Institution:Yonsei University  

Interpretable Meningioma Grading and Segmentation with Multiparametric Deep Learning

Investigation of asymmetric undersampling scheme in accelerated flow imaging for improving velocity and turbulence kinetic energy estimation

Joint Generation of Multi-contrast Magnetic Resonance Images and Segmentation Map Using StyleGAN2-based Generative Network

Author:Geonhui Son  Taejoon Eo  Yohan Jun  Hyungseob Shin  Dosik Hwang  

Session Type:Oral  

Session Date:Monday, 09 May 2022  

Topic:Module 29: Processing & Analysis  

Session Name:New Techniques in Data Acquisition & Analysis  

Program Number:0102  

Room Session:N11 (Breakout A)  

Institution:Yonsei University  

MoDGAN: Unsupervised rigid motion detection and correction with generative adversarial networks

Author:Mu-Yul Park  Seul Lee  Kyu-Jin Jung  Jisu Yun  Dong-Hyun Kim  

Session Type:Digital Poster  

Session Date:Monday, 09 May 2022  

Topic:Module 15: Data Acquisition & Artifacts  

Session Name:Motion & Artifacts I  

Program Number:0863  

Room Session:Exhibition Hall:S8 & S9  

Institution:Yonsei University  

Motion Correction of Contrast-enhanced Pediatric Brain MRI with Optional Non-contrast-enhanced Synthesis within a Single Neural Network

Myelin Water Imaging using Dimensionality Reduction

Author:Jae Eun Song  Shreyas Vasanawala  Dong-Hyun Kim  

Session Type:Online Gather.town Pitches  

Session Date:Monday, 09 May 2022  

Topic:Module 17: White Matter & Nervous System  

Session Name:White Matter & Nervous System III  

Program Number:3362  

Room Session:3  

Institution:Stanford University  Yonsei University  

PedQ-NET : Unsupervised Model-Based Joint-Loss Deep learning for Pediatric QSM

Self-Assisted Priors with Cascaded Refinement Network for Reduction of Rigid Motion Artifacts in Brain MRI

Transformer-based Alzheimer’s disease analyzer for multi-institutional 3D MRI images

Author:Jinseong Jang  Dosik Hwang  

Session Type:Oral  

Session Date:Wednesday, 11 May 2022  

Topic:Module 9: Multiple Sclerosis, Alzheimer's and Dementia  

Session Name:Alzheimer's Disease & Other Dementias  

Program Number:0461  

Room Session:S11 (Breakout A)  

Institution:Yonsei University  

Triplanar Ensemble Detection Network (TPE-Det): A Single End-to-End Model for Efficient Detection of Cerebral Microbleeds in MR Images

Triplanar Ensemble Detection Network (TPE-Det): A Single End-to-End Model for Efficient Detection of Cerebral Microbleeds in MR Images

Unsupervised Deep Learning using modified Cycle Generative Adversarial Network for rigid motion correction in pediatric brain MRI

ViT-PU-Net: Volumetric Phase Unwrapping for MR images based on Vision Transformer

ViT-PU-Net: Volumetric Phase Unwrapping for MR images based on Vision Transformer