117 citations · 179 across the 22 of their papers we have counts for
24 papers
Clinically Interpretable Survival Risk Stratification in Head and Neck Cancer Using Bayesian Networks and Markov Blankets
Keyur D. Shah, Ibrahim Chamseddine, Xiaohan Yuan +8
Purpose: To identify a clinically interpretable subset of survival-relevant features in HN cancer using Bayesian Network (BN) and evaluate its prognostic and causal utility. Method…
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…
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…
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…
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…
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 (…
Adaptive Proton Therapy Using CBCT-Guided Digital Twins
Chih-Wei Chang, Zhen Tian, Richard L. J. Qiu +8
This study aims to develop a digital twin (DT) framework to enhance adaptive proton stereotactic body radiation therapy (SBRT) for prostate cancer. Prostate SBRT has emerged as a l…
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…
Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography
Xuxin Chen, Yuheng Li, Mingzhe Hu +5
Although fusion of information from multiple views of mammograms plays an important role to increase accuracy of breast cancer detection, developing multi-view mammograms-based com…
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.…
Assessing Bilateral Neurovascular Bundles Function with Pulsed Wave Doppler Ultrasound: Implications for Reducing Erectile Dysfunction Following Prostate Radiotherapy
Jing Wang, Xiaofeng Yang, Boran Zhou +5
This study aims to evaluate the functional status of bilateral neurovascular bundles (NVBs) using pulsed wave Doppler ultrasound in patients undergoing prostate radiotherapy (RT).…
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…
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…
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks
Lukas Fesser, Luca D'Amico-Wong, Richard Qiu
Physics-informed Neural Networks (PINNs) have recently gained popularity due to their effective approximation of partial differential equations (PDEs) using deep neural networks (D…
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…
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI
Yuheng Li, Jacob Wynne, Jing Wang +8
Biparametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection using convolutional neural networks (CNNs). Recently, transfor…
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…
Deep Learning in MRI-guided Radiation Therapy: A Systematic Review
Zach Eidex, Yifu Ding, Jing Wang +5
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematic…
CBCT-Based Synthetic CT Image Generation Using Conditional Denoising Diffusion Probabilistic Model
Junbo Peng, Richard L. J. Qiu, Jacob F Wynne +8
Background: Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, makin…
Generative Adversarial Network for Image Synthesis
Yang Lei, Richard L. J. Qiu, Tonghe Wang +3
This chapter reviews recent developments of generative adversarial networks (GAN)-based methods for medical and biomedical image synthesis tasks. These methods are classified into…
Deep Learning in Multi-organ Segmentation
Yang Lei, Yabo Fu, Tonghe Wang +4
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. These met…