Research

Overview

The lab develops computational methods that combine quantitative MRI, inverse problems, machine learning, and clinically motivated neuroimaging analysis.

Current Directions

Accelerated MRI

Fast acquisition and reconstruction methods for quantitative imaging, with an emphasis on practical deployment, robustness, and translational impact.

Quantitative Susceptibility Mapping

Methods for susceptibility estimation, reconstruction, and downstream analysis in neurological applications, especially where quantitative tissue characterization matters.

Lesion Analysis

Automated segmentation, longitudinal tracking, and biomarker discovery for white matter lesion burden and disease progression.

AI for Neuroimaging

Machine learning models for image analysis, harmonization, prediction, and decision support in MRI-driven studies.

Collaboration

We are interested in collaborations spanning MRI methodology, clinical neuroimaging, and translational data science. This page can grow to include project summaries, representative figures, and grant-supported efforts.