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20232026
most citedCBCT-Based Synthetic CT Image Generation Using Conditional Denoising Diffusion Probabilistic Model

117 citations · 178 across the 28 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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-…

cs.CV2025

Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models

Yuan Gao, Shaoyan Pan, Mingzhe Hu +6

Cone-beam CT (CBCT) is widely used in clinical radiotherapy for image-guided treatment, improving setup accuracy, adaptive planning, and motion management. However, slow gantry rot…

cs.CV2025

A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learning

Mingzhe Hu, Yuan Gao, Yuheng Li +10

Purpose: Accurate segmentation of clinical target volumes (CTV) and organs-at-risk is crucial for optimizing gynecologic brachytherapy (GYN-BT) treatment planning. However, anatomi…