5 papers
A Target-Free Harmonization Method for MRI
Minjun Kim, Dong Ju Mun, Hwihun Jeong +4
In MRI, variations in scan parameters, sequence, or hardware can lead to discrepancies in image appearance, even for the same subject. These inconsistencies, known as domain shifts…
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…
MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learning
Hwihun Jeong, Se Young Chun, Jongho Lee
Deep learning-based Magnetic Resonance (MR) reconstruction methods have focused on generating high-quality images but often overlook the impact on downstream tasks (e.g., segmentat…
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…
Efficient and robust 3D blind harmonization for large domain gaps
Hwihun Jeong, Hayeon Lee, Se Young Chun +1
Blind harmonization has emerged as a promising technique for MR image harmonization to achieve scale-invariant representations, requiring only target domain data (i.e., no source d…