Research

Novel Imaging Techniques for Interventional Applications

Non-Circular Orbit Design for Interventional Cone-Beam CT

Completeness maps for generalized CT orbit design

Phantom images comparing circular and non-circular orbit cases

We develop novel acquisition and reconstruction techniques for interventional cone-beam CT, including non-circular orbits for large field-of-view imaging and metal artifact reduction.

Selected Presentations

Non-circular CT orbit design for elimination of metal artifacts - SPIE Medical Imaging 2020

Spectral Digital Subtraction Angiography

Spectral DSA diffusion posterior sampling results

Digital subtraction angiography is sensitive to motion between mask and contrast frames. We develop spectral-temporal processing methods that use energy-resolved measurements to improve vessel visibility, material separation, and robustness to motion in interventional angiography.

Selected Presentations

Three-material decomposition using a dual-layer flat-panel detector in the presence of soft tissue motion - SPIE Medical Imaging 2023

Spectral Imaging

Detector modeling and system design

We model spectral imaging systems to improve the performance of quantitative spectral imaging tasks. Predictive models for bias, variance, and task-based performance indices are developed to improve spectral detector and system designs.

Detector modeling for photon-counting and multi-layer detectors
Material decomposition algorithms and image-quality prediction

Selected Presentations

Theoretical prediction of bias in model-based material decomposition - SPIE Medical Imaging 2026

Deep Learning Image Formation

Assessment of Deep Learning Reconstruction

Display strategies for communicating variability in deep learning CT reconstruction

Deep learning reconstruction and post-processing methods are nonlinear and data dependent. We develop analysis methods to evaluate how learned algorithms change image content and diagnostic performance, including how uncertainty and variability can be communicated to radiologists.

Selected Presentations

Communicating variability in deep learning CT reconstruction to radiologists - Deep Reconstruction Workshop 2025

Image Synthesis with Radiomics Supervision

We develop domain-guided generative models for image synthesis. We developed a generative adversarial network with radiomics supervision to encourage synthesized and training samples to have similar radiomic feature distributions. We later extended this concept to radiomics-conditioned diffusion posterior sampling, treating radiomic features as consistency constraints to control the synthesized lesions.

Radiomics-conditioned image synthesis
Domain knowledge incorporated into generative imaging models

Selected Presentations

A generative model of lung CT conditioned on radiomics features - SPIE Medical Imaging 2026
Generative adversarial networks and radiomics supervision for lung lesion synthesis - SPIE Medical Imaging 2021

Physics-Guided Radiomics Standardization

Radiomics Feature Standardization

GLCM recovery workflow from SPIE 2021 radiomics presentation

Classification performance after radiomics standardization

Scanner-induced variability is a major impediment to the clinical translation of radiomics studies. We use prior knowledge of spatial resolution and noise to predict and mitigate scanner-dependent feature variability for robust, reproducible, and generalizable downstream quantitative analysis.

Standardization of histogram- and texture-based radiomics features
Prediction and correction of scanner-dependent feature shifts from blur and noise
Evaluation of feature recovery in classification tasks

Selected Presentations

End-to-end modeling for predicting and estimating radiomics: application to GLCM in CT - SPIE Medical Imaging 2021

Image Quality & Task-Driven Imaging

Image Chain Modeling and System Design

We model the imaging chain from data acquisition through reconstruction and image interpretation. The goal is to use task-based detectability indices to optimize system design and improve imaging performance.

Selected Presentations

Joint optimization of fluence field modulation and regularization for multi-task objectives - SPIE Medical Imaging 2018
Task-driven acquisition and reconstruction in cone-beam CT - SPIE Medical Imaging 2015

Image Analysis

Automatic Working-Angle Prediction for Neuro-Interventional Procedures

Automatic view angle prediction workflow

We develop image-analysis and quantitative feature-extraction methods for procedural planning and outcome evaluation. In neuro-interventional applications, this includes automatic working-angle prediction for intracranial aneurysm treatment using anatomical models derived from 3D angiographic imaging.

Segmentation and quantitative feature extraction from 3D angiography
Automatic neck-view and barrel-view prediction for aneurysm treatment

Selected Presentations

Automatic viewing angles prediction for endovascular treatment in intracranial aneurysm - SPIE Medical Imaging 2026

Novel Imaging Techniques for Interventional Applications

Non-Circular Orbit Design for Interventional Cone-Beam CT

Spectral Digital Subtraction Angiography

Spectral Imaging

Detector modeling and system design

Deep Learning Image Formation

Assessment of Deep Learning Reconstruction

Image Synthesis with Radiomics Supervision

Physics-Guided Radiomics Standardization

Radiomics Feature Standardization

Image Quality & Task-Driven Imaging

Image Chain Modeling and System Design

Image Analysis

Automatic Working-Angle Prediction for Neuro-Interventional Procedures

Collaborators