13 citations · 41 across the 19 of their papers we have counts for
21 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…
Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model
Zach Eidex, Yu-nong Lin, Mojtaba Safari +4
Background: Accurate glioma subregion delineation is important for radiotherapy planning and longitudinal monitoring, but manual contour correction is time-consuming. Models such a…
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…
Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning
Mojtaba Safari, Shansong Wang, Vanessa L Wildman +10
Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan, yet existing deep…
Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model
Qiang Li, Mojtaba Safari, Shansong Wang +4
Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denois…
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…
Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction
Mojtaba Safari, Zach Eidex, Richard L. J. Qiu +3
Background: To systematically review and perform a meta-analysis of artificial intelligence (AI)-driven methods for detecting and correcting magnetic resonance imaging (MRI) motion…
DINOv3 with Test-Time Training for Medical Image Registration
Shansong Wang, Mojtaba Safari, Mingzhe Hu +4
Prior medical image registration approaches, particularly learning-based methods, often require large amounts of training data, which constrains clinical adoption. To overcome this…
Benchmarking GPT-5 for Zero-Shot Multimodal Medical Reasoning in Radiology and Radiation Oncology
Mingzhe Hu, Zach Eidex, Shansong Wang +3
Radiology, radiation oncology, and medical physics require decision-making that integrates medical images, textual reports, and quantitative data under high-stakes conditions. With…
Is ChatGPT-5 Ready for Mammogram VQA?
Qiang Li, Shansong Wang, Mingzhe Hu +3
Mammogram visual question answering (VQA) integrates image interpretation with clinical reasoning and has potential to support breast cancer screening. We systematically evaluated…
Performance of GPT-5 in Brain Tumor MRI Reasoning
Mojtaba Safari, Shansong Wang, Mingzhe Hu +3
Accurate differentiation of brain tumor types on magnetic resonance imaging (MRI) is critical for guiding treatment planning in neuro-oncology. Recent advances in large language mo…
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-…
Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI
Mojtaba Safari, Shansong Wang, Qiang Li +5
Objective. Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts,…
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…
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration
Mojtaba Safari, Zach Eidex, Chih-Wei Chang +2
Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition…
T1-contrast Enhanced MRI Generation from Multi-parametric MRI for Glioma Patients with Latent Tumor Conditioning
Zach Eidex, Mojtaba Safari, Richard L. J. Qiu +4
Objective: Gadolinium-based contrast agents (GBCAs) are commonly used in MRI scans of patients with gliomas to enhance brain tumor characterization using T1-weighted (T1W) MRI. How…
Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction
Mojtaba Safari, Zach Eidex, Shaoyan Pan +2
Purpose: To propose a self-supervised deep learning-based compressed sensing MRI (DL-based CS-MRI) method named "Adaptive Self-Supervised Consistency Guided Diffusion Model (ASSCGD…
Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review
Mojtaba Safari, Zach Eidex, Chih-Wei Chang +2
Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-invasive and highly detailed look into the human body. However, the long acquisition times of M…