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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

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

Signal prediction in echo dimension of multi-echo gradient echo using multi-layer seq2seq model

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