Dr Muge Karaman- Non-Gaussian Diffusion MRI as a Probe to Tumor Tissue Microstructures

Upcoming webinar: Friday 10th July 6:00 am Brisbane. Register online

Non-Gaussian Diffusion MRI as a Probe to Tumor Tissue Microstructures​

At a microscopic level, tumor tissues exhibit a high degree of structural heterogeneity and complexity, which reflect the underlying biology. Encoding the diffusion displacement of water molecules, diffusion-weighted MRI (DWI) has the potential to provide quantitative information related to the microstructural changes due to the physiologic and/or pathologic transformations in tissue. Unlike diffusion in a homogeneous medium, the diffusion displacements of water molecules do not follow Gaussian distribution in a heterogenous environment, particularly in tumors. Recognizing this complexity, several non-Gaussian diffusion models have been suggested to probe the underlying tissue microstructures and environment. Among these, the continuous-time random walk model, its predecessor fractional-order calculus model, and the fractional motion model were developed to examine the spatiotemporal and/or stochastic characteristics of biological tissue through non-Gaussian diffusion dynamics. This talk will present theory, implementation, and validation of these novel non-Gaussian DWI models; and demonstrate their clinical applications in diagnosis and treatment assessment of different types of cancer, such as brain tumor, gastric cancer, and breast cancer.

Dr Muge Karaman received her first degree in Applied Mathematics from Istanbul Technical University in Istanbul, Turkey. She then pursued a Master’s degree in Computational Sciences and Engineering, where she worked on developing computational optimization models for disaster preparedness and response logistics; hoping to alleviate the impact of the expected powerful earthquakes in the region.

She found herself in the MRI field in 2009. She received her PhD in Computational Sciences from Marquette University in Milwaukee, Wisconsin, where she had a lot of time to learn a lot about functional MRI (fMRI). Her research focused on developing complex-valued statistical models for fMRI and functional connectivity MRI in contrast to conventional techniques that use magnitude-only MRI data. As a reflection of her background in unconventional methods and models, she developed interest in anomalous diffusion after she joined the University of Illinois at Chicago (UIC) in 2014.

She is now a Research Assistant Professor at the Department of Bioengineering and the Center for MR Research of UIC. The overall goal of her research is developing and validating quantitative diffusion-weighted MRI techniques for the characterization of complex biological tissue in vivo and integrating these techniques into computational models to address current and future medical challenges. She is particularly interested in investigating their feasibility in probing the underlying tissue characteristics in different disease conditions and organs, including adult and pediatric brain tumors, gastric cancer, and breast cancer. She is confident these advanced MRI techniques will facilitate critical clinical decisions in cancer diagnosis, early prediction of response to therapy, monitoring disease recurrence; and ultimately will help save lives.