8 papers
Generalizable 7T T1-map Synthesis from 1.5T and 3T T1 MRI with an Efficient Transformer Model
Zach Eidex, Mojtaba Safari, Tonghe Wang +6
Purpose: Ultra-high-field 7T MRI offers improved resolution and contrast over standard clinical field strengths (1.5T, 3T). However, 7T scanners are costly, scarce, and introduce a…
An Efficient 3D Latent Diffusion Model for T1-contrast Enhanced MRI Generation
Zach Eidex, Mojtaba Safari, Jie Ding +7
Objective: Gadolinium-based contrast agents (GBCAs) are commonly employed with T1w MRI to enhance lesion visualization but are restricted in patients at risk of nephrogenic systemi…
Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation
Shansong Wang, Zhecheng Jin, Mingzhe Hu +7
CLIP models pretrained on natural images with billion-scale image-text pairs have demonstrated impressive capabilities in zero-shot classification, cross-modal retrieval, and open-…
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
Mojtaba Safari, Shansong Wang, Zach Eidex +4
Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reco…
Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging
Shansong Wang, Mojtaba Safari, Qiang Li +5
Vision foundation models (VFMs) are pre-trained on extensive image datasets to learn general representations for diverse types of data. These models can subsequently be fine-tuned…
A Physics-Informed Deep Learning Model for MRI Brain Motion Correction
Mojtaba Safari, Shansong Wang, Zach Eidex +4
Background: MRI is crucial for brain imaging but is highly susceptible to motion artifacts due to long acquisition times. This study introduces PI-MoCoNet, a physics-informed motio…