Associate Professor, Department of Radiology
Diffusion-weighted imaging (DWI) is a remarkable MRI tool that provides sensitivity to the measurements of tissue microstructure and water mobility on a micron scale and embeds this information as contrast in macroscopic images of the human body. In this approach, water molecules become reporters of their host-tissue microenvironment, as native or pathological processes either restrict or drive their local motion.
The applications of this technique are as varied as the behavior of water within biological tissue and, with the proper acquisition and analysis framework, can include diagnostic and prognostic biomarkers of tissue function for many disorders.
My research group works at the translational interface between the technical development and the clinical application of DWI. We develop new imaging and analysis tools and apply them in clinical populations to determine their optimum benefit.
In the area of breast cancer, conventional DWI is an established tool for detecting aggressive cellularity through its restriction of apparent water diffusion.
Our implementation of the intravoxel incoherent motion (IVIM) approach, however, provides sensitivity not only to cellularity but also to the often concomitant growth of neovasculature (angiogenesis) that supports tumors’ hyperactive growth.
We are also exploring the connection between these IVIM biomarkers and histological microstructural metrics, systemic anomalies such as interstitial fluid pressure, hormonal prognostic factors, and imaging biomarkers from other modalities (such as positron emission tomography (PET)) in order to maximize their potential in both diagnosis and prediction of treatment response.
Skeletal muscle is another system in which microstructure heavily affects macroscopic function. We apply diffusion tensor imaging (DTI)—a technique sensitive to tissue directionality through anisotropic water restriction—to skeletal muscle pathologies such as chronic exertional compartment syndrome and dermatomyositis, in order to improve the detection of these debilitating disorders and better understand the underlying biophysical mechanisms.
However, because the kinetics of muscle activation are often key to diagnosis, we have developed a revolutionary new approach to muscle DTI, a multiple echo diffusion tensor acquisition technique (MEDITATE), wherein, through the use of multiple echoes, it takes a far lower number of scans to capture the necessary variations in diffusion sensitization. Speeding up these measurements may then allow DTI microstructural metrics to be captured dynamically, during and after muscle exertion. Using MRI-compatible exercise modules (ergometers), we now utilize this technique to capture the response and recovery of DTI metrics in muscle in healthy and pathological muscle.
Finally, water transport in renal tissue is a complex mixture of blood flow, tubular flow, and random motion in tissue that act in concert to enable kidney function. We apply advanced diffusion MRI tools (DTI, IVIM), both alone and in joint analysis, to parse out microstructure and processes of perfusion and tubular flow as they affect healthy kidney function and its decline with chronic disease or acute surgical shock. Such quantitative and noninvasive data may prove invaluable in the management of renal disease and—as in the breast—renal cancer.
PhD from Northwestern University
Fellowship, Northwestern University Radiology, Postdoctoral Fellowship
Fellowship, Schlumberger-Doll Research, Postdoctoral Fellowship
Journal of magnetic resonance imaging. 2020 Jul; 52(1):70-90
European radiology. 2020 Mar; 30(3):1436-1450
MAGMA (European Society for Magnetic Resonance in Medicine & Biology). 2020 Feb; 33(1):131-140
MAGMA (European Society for Magnetic Resonance in Medicine & Biology). 2020 Feb; 33(1):177-195
MAGMA (European Society for Magnetic Resonance in Medicine & Biology). 2020 Feb; 33(1):197-198
MAGMA (European Society for Magnetic Resonance in Medicine & Biology). 2020 Jan 28; Y:
Neuroimage. 2019 Sep 30; 116228
European journal of radiology. 2019 Jan; 110:163-168