My primary research interest is to develop novel imaging methods to deepen our understanding of pathophysiology and improve clinical diagnosis and patient management. My research is focused on MR imaging and combined PET/MR imaging with expertise in MRI physics, MR sequence design and programming, image reconstruction, image and data analysis, and deep learning.

I have extensive experience in MR oxygen metabolic imaging, perfusion (Dynamic Susceptibility Contrast and Arterial Spin Labeling) imaging, susceptibility-weighted imaging (SWI), diffusion MR imaging, quantitative T1 and T2 measurements, and attenuation and motion correction for simultaneous PET/MR measurements. Our group has developed novel MR physiological imaging techniques and applied them to study cerebral small vessel disease and sickle cell disease. Moreover, we have developed deep learning-based MR-derived pseudo-CT for PET/MR attenuation correction and pediatric cranial bone imaging to reduce ionizing radiation. We have developed deep learning-based methods to reconstruct highly under-sampled MR k-space data to accelerate acquisition and perform free-breathing motion-compensated dynamic contrast-enhanced (DCE) MRI in the liver.