2 citations · 3 across the 9 of their papers we have counts for
9 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…
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…
One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy
Shaoyan Pan, Kirk Jon Luca, Yuan Gao +8
In this work, we introduce the One-for-All Adaptive Radiotherapy Planning Agent, a unified foundation-model-based system that performs complete, treatment-specific online adaptive…
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…
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…
Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning
Mohammadamin Moradi, Runyu Jiang, Yingzi Liu +6
High-dose-rate (HDR) brachytherapy plays a critical role in the treatment of locally advanced cervical cancer but remains highly dependent on manual treatment planning expertise. T…
Patient-Specific Deep Reinforcement Learning for Automatic Replanning in Head-and-Neck Cancer Proton Therapy
Malvern Madondo, Yuan Shao, Yingzi Liu +3
Anatomical changes during intensity-modulated proton therapy (IMPT) for head-and-neck cancer (HNC) can shift Bragg peaks, risking tumor underdosing and organ-at-risk overdosing. Tr…
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…
Stereotactic Arrhythmia Radioablation for Refractory Ventricular Tachycardia: A Narrative Review and Exploratory Pooled Analysis of Clinical Outcomes and Toxicity
Keyur D. Shah, Chih-Wei Chang, Sibo Tian +6
Purpose: Stereotactic arrhythmia radioablation (STAR) is a non-invasive salvage therapy for refractory ventricular tachycardia (VT), especially in patients ineligible for catheter…