From the 1 of 20 linked papers with an AI index.
20 papers
Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reduction
Mojtaba Safari, Shansong Wang, Zach Eidex +4
Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep l…
MRI super-resolution in ten sampling steps using a diffusion bridge model
Mojtaba Safari, Hang Yu, Zach Eidex +10
Objective. MRI provides excellent soft-tissue contrast, but long acquisition times can cause patient discomfort and lead to motion artifacts, forcing a trade-off between spatial re…
One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy
Shaoyan Pan, Kirk Jon Luca, Yuan Gao +8
The paper presents a foundation‑model based system that automatically creates daily adaptive radiotherapy plans from cone‑beam CT images in under two minutes, handling tasks such a…
BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
Yizhou Wu, Shansong Wang, Yuheng Li +5
Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data. Here we sh…
Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
Yuheng Li, Yuan Gao, Haoyu Dong +5
Computed tomography (CT) is a central to three-dimensional medical imaging, yet CT-based artificial intelligence remains fragmented across task-specific models for segmentation, cl…
MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering
Suyang Xi, Songtao Hu, Yuxiang Lai +4
Medical vision--language models (VLMs) have shown strong potential for medical visual question answering (VQA), yet their reasoning remains largely text-centric: images are encoded…