1 citations · 1 across the 6 of their papers we have counts for
7 papers
Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation
Alzahra Altalib, Chunhui Li, Christopher Hamill Taylor +2
During standard radiotherapy planning, repeated CT acquisitions are often required for patient registration, verification, and adaptive planning, resulting in increased cumulative…
FOD-Diff: 3D Multi-Channel Patch Diffusion Model for Fiber Orientation Distribution
Hao Tang, Hanyu Liu, Alessandro Perelli +2
Diffusion MRI (dMRI) is a critical non-invasive technique to estimate fiber orientation distribution (FOD) for characterizing white matter integrity. Estimating FOD from single-she…
Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review
Alzahra Altalib, Chunhui Li, Alessandro Perelli
Objective: Cone-beam computed tomography (CBCT) provides a low-dose imaging alternative to conventional CT, but suffers from noise, scatter, and artifacts that degrade image qualit…
Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
Alzahra Altalib, Chunhui Li, Alessandro Perelli
Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy. It offers real time anatomical visualization with low acquisition cost and…
Synthetic CT image generation from CBCT: A Systematic Review
Alzahra Altalib, Scott McGregor, Chunhui Li +1
The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology. This systema…
Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis
Juanhua Zhang, Ruodan Yan, Alessandro Perelli +2
Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs. Deep learning approaches have been used to enhance dMRI and predict diffusion biomarkers th…
End-to-End Model-based Deep Learning for Dual-Energy Computed Tomography Material Decomposition
Jiandong Wang, Alessandro Perelli
Dual energy X-ray Computed Tomography (DECT) enables to automatically decompose materials in clinical images without the manual segmentation using the dependency of the X-ray linea…