666 citations · 918 across the 43 of their papers we have counts for
46 papers
A radiographic world model for clinical reasoning and evidence generation
Suyang Xi, Songtao Hu, Shansong Wang +8
Medical imaging artificial intelligence (AI) is commonly developed as separate mappings from radiographs to diagnostic outputs or from clinical descriptions to generated images, al…
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…
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…
Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories
Ran Tong, Tong Wang, Lanruo Wang +1
Medium-horizon Alzheimer's disease progression prediction is difficult because future clinical scores can remain tied to baseline severity, while biomarker histories are irregular…
CBCT-Based Synthetic CT Generation Using Conditional Flow Matching Model
Junbo Peng, Huiqiao Xie, Tonghe Wang +2
Daily or weekly cone-beam computed tomography (CBCT) is employed in image-guided radiotherapy (IGRT) for precise patient alignment. However, its clinical utility in quantitative ta…
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…
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…
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…
Dual-Energy Cone-Beam CT Using Two Orthogonal Projection Views: A Phantom Study
Junbo Peng, Tonghe Wang, Shaoyan Pan +1
This study proposes a novel imaging and reconstruction framework for dual-energy cone-beam CT (DECBCT) using only two orthogonal X-ray projections at different energy levels (2V-DE…
Patient-Specific CBCT Synthesis for Real-time Tumor Tracking in Surface-guided Radiotherapy
Shaoyan Pan, Vanessa Su, Junbo Peng +6
We present a new imaging system to support real-time tumor tracking for surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire real-time surface top…
A Review of Artificial Intelligence in Brachytherapy
Jingchu Chen, Richard Qiu, Tonghe Wang +2
Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine l…
Optimization-Based Image Reconstruction Regularized with Inter-Spectral Structural Similarity for Limited-Angle Dual-Energy Cone-Beam CT
Junbo Peng, Tonghe Wang, Huiqiao Xie +6
Background: Limited-angle (LA) dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without h…
Deep Learning Based Apparent Diffusion Coefficient Map Generation from Multi-parametric MR Images for Patients with Diffuse Gliomas
Zach Eidex, Mojtaba Safari, Jacob Wynne +6
Purpose: Apparent diffusion coefficient (ADC) maps derived from diffusion weighted (DWI) MRI provides functional measurements about the water molecules in tissues. However, DWI is…
Unsupervised Bayesian Generation of Synthetic CT from CBCT Using Patient-Specific Score-Based Prior
Junbo Peng, Yuan Gao, Chih-Wei Chang +7
Background: Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (…
Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration
Yang Lei, Luke A. Matkovic, Justin Roper +6
This paper aims to create a deep learning framework that can estimate the deformation vector field (DVF) for directly registering abdominal MRI-CT images. The proposed method assum…
Dual-Energy Cone-Beam CT Using Two Complementary Limited-Angle Scans with A Projection-Consistent Diffusion Model
Junbo Peng, Chih-Wei Chang, Richard L. J. Qiu +5
Background: Dual-energy imaging on cone-beam CT (CBCT) scanners has great potential in different clinical applications, including image-guided surgery and adaptive proton therapy.…
High-resolution 3T to 7T MRI Synthesis with a Hybrid CNN-Transformer Model
Zach Eidex, Jing Wang, Mojtaba Safari +6
7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC…
Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss
Junbo Peng, Chih-Wei Chang, Huiqiao Xie +6
Background: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise…
One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT
Qian Wang, Huiqiao Xie, Tonghe Wang +7
Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification…
Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model
Shaoyan Pan, Elham Abouei, Junbo Peng +8
Objective: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is betwee…
Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning
Yang Lei, Yifu Ding, Richard L. J. Qiu +6
Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniq…
Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model
Shaoyan Pan, Elham Abouei, Jacob Wynne +10
Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone…
Data-Driven Volumetric Image Generation from Surface Structures using a Patient-Specific Deep Leaning Model
Shaoyan Pan, Chih-Wei Chang, Marian Axente +5
The advent of computed tomography significantly improves patient health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escala…
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis
Shaoyan Pan, Chih-Wei Chang, Junbo Peng +7
This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition each…