Congratulations! Our abstract "State-of-the-Art Diagnostic Performance of an Interpretable Vision-Language Model for BI-RADS Assessment in Breast Ultrasound (B-RAD): Multinational Validation and Reader Study" has been accepted to the Annual Meeting of the Radiological Society of North America 2026 (RSNA 2026): Sunghyun Jung and Han Jang

Congratulations! Our abstract "Agentic AI for Longitudinal Assessment of Brain Metastases: Reproducible Reporting and Human-in-the-Loop Safety" has been accepted to the Annual Meeting of the Radiological Society of North America 2026 (RSNA 2026): Songsoo Kim, MD, PhD and Junhyeok Lee, MS

Congratulations! Our abstract "Abdominal CT and Brain MRI Phenotypes Across the Glycemic Spectrum: Graded Body–Brain Signatures of Prediabetes and Diabetes" has been accepted to the Annual Meeting of the Radiological Society of North America 2026 (RSNA 2026): Songsoo Kim, MD, PhD

Congratulations! Our abstract "Comprehensive Assessment of Associations Between CT-Derived Fat Distribution, Muscle Quality, Muscle Quantity, and Brain Health in a Health-Screening Cohort" has been accepted to the Annual Meeting of the Radiological Society of North America 2026 (RSNA 2026): Prof. Wonjung Kim, MD, PhD

Congratulations! Our abstract "Longitudinal Volumetric MRI for Earlier Identification of Progression in Brain Metastases" has been accepted to the Annual Meeting of the Radiological Society of North America 2026 (RSNA 2026): Young Hun Jeon, MD and Junhyeok Lee, MS

Congratulations! Our paper "Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation" has been accepted to International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026):  Junhyeok Lee, MS  

Congratulations! Our paper "Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification" has been accepted to International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026):  Junhyeok Lee, MS 

Congratulations! Selected to serve on the Editorial Board of Radiology: In Training as an Associate Editor(July 1,2026 to June 30, 2027): Songsoo Kim, MD, PhD

Congratulations! Our paper "Text-Deficient Multimodal Stroke Segmentation with Lesion-Grounded Self-Retrieval-Augmented Generation" has been provisionally accepted (Top 9%) to International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026): Heeseong Eum, MS 

Congratulations! Our paper "Deep Learning for Survival Prediction in Glioblastoma: Time-dependent Model Interpretability Using MRI, Clinical, and Molecular Data "  has been accepted to Radiology: Artificial Intelligence (IF=13.2):  Junhyeok Lee, MS 

Congratulations! Our paper "MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models" has been accepted to Findings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026, Poster Presentation): Han Jang

Congratulations! Outstanding Research Paper Award, Seoul National University Hospital Biomedical Research Institute: Junhyeok Lee, MS 

Congratulations! Our abstract "Molecular versus Histologic Glioblastoma: Tumor Heterogeneity from Multiparametric Physiologic MRI "  has been accepted to Annual Meeting of European Congress of Radiology 2026 (ECR 2026) (Oral presentation): Minseo Choi, MD 

Congratulations! Best Trainee Scientific Awards (Gold Prize), The 13th International Congress on Magnetic Resonance Imaging & 30th Annual Scientific Meeting of KSMRM (ICMRI2025): Joon Jang

Congratulations! Best Trainee Scientific Awards (Silver Prize), The 13th International Congress on Magnetic Resonance Imaging & 30th Annual Scientific Meeting of KSMRM (ICMRI2025): Han Jang

Congratulations! Our research has been selected for the 2025 Research Grant Program of the Korean Society of Magnetic Resonance in Medicine (KSMRM). 

Congratulations! Our paper "GlioSurv: Interpretable Transformer for Multimodal, Individualized Survival Prediction in Diffuse Glioma"  has been accepted to npj digital medicine (IF=15.1): Junhyeok Lee, MS

We are pleased to announce that Prof. Kyu Sung Choi has been invited to deliver a keynote lecture at the LIDM (Learning with Longitudinal Medical Images and Data) Workshop at MICCAI 2025. 

Congratulations! Our paper "ST-SRPerf: Continuous Spatiotemporal Representation for Perfusion MRI Super-resolution through Neural ODE and Implicit Neural Representation "  has been accepted to Computers in Biology and Medicine (IF=6.3; JCR top 5%): Junhyeok Lee, MS

Congratulations! Our abstract "Exploring evidence for vertical recurrence of glioblastoma using dynamic contrast-enhanced MRI" has been accepted to Annual Meeting of Radiological Society of North America 2025 (RSNA 2025): Prof. Minchul Kim, MD, PhD (Collaborator) 

Congratulations! Our paper "Lesion-Aware Post-Training of Latent Diffusion Models for Synthesizing Diffusion MRI from CT Perfusion " has been provisionally accepted (Top 9%) to International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025): Junhyeok Lee, MS 

Congratulations! New Researcher Academic Award, Korean Society of Neuroradiology (KSNR2025): Prof. Kyu Sung Choi

Congratulations! Our paper "Association of Deep Learning-Based Chest CT-Derived Respiratory Parameters with Disease Progression in Amyotrophic Lateral Sclerosis"  has been accepted to Radiology (IF=12.1): Prof. Kyu Sung Choi 

Congratulations! Our paper "Unsupervised Deep Learning for Model-Free Blood‒Brain Barrier Leakage Detection with Dynamic Contrast-Enhanced MRI in Diffuse Gliomas"  has been accepted to Radiology: Artificial Intelligence (IF=8.1): Joon Jang, MS 

Driven by physician-scientists and physician-investigators, the lab focuses on:

1. Disease-wisely:

• Brain Glymphatics Analysis via perfusion MRI for neurodegeneration, demyelinating disease, and Glioblastoma.

• Tumor Microenvironment Analysis using multiparametric MRI in Glioblastoma.

• Longitudinal Volumetric Change Analysis in Degenerative, Demyelinating Diseases, and Autoimmune Encephalitis.

• Prediction and Generative models in Stroke using Brain CT

2. Methodologically, the lab specializes in:

• Learning Disease Spatiotemporal Dynamics using Perfusion MRI.

• Synthesizing and Validating Medical Imaging and Radiologic Reports with Generative Models.