Understanding the dynamic interplay between tissue microenvironments and metabolic substrates is critical in the rapidly developing field of metabolic imaging. In this study, we will show a new method to metabolic MRI that uses Dynamic Glucose-Enhanced (DGE) MRI by Chemical Exchange Saturation Transfer (CEST) technology. Our study provides a one-hour temporal window into the complicated dynamics of glucose transport inside the brain's various regions and disease states.
Our work initially focuses on the use of DGE MRI to characterize the tumor microenvironment in mouse models. We were able to identify distinct glucose use patterns inside tumor tissues by using machine learning approaches such as Self-Organized Mapping (SOM). The slope of the glucose signal is proposed as a viable diagnostic for distinguishing tumors from normal tissues. Theoretical models provide more information on the molecular elements of glucose metabolism and its connection with tumor microenvironments, emphasizing the metabolic variability found inside tumors.
We are also investigating the utility of DGE MRI in understanding the course of Huntington's disease (HD) in mice. Our preliminary data show that there are substantial differences in glucose uptake that correlate with disease development. Pathological and biochemical analyses are used to support the approach and discover the underlying illness causes. Finally, although this area is actively being researched, our work is oriented toward investigating the functional links between different brain regions.
This work intends to transform diagnostic imaging from a static image to a dynamic panorama, providing essential insights into disease causes, diagnosis, and treatment methods by combining dynamic metabolic data.