Co-clinical imaging research
Multi-disciplinary effort to facilitate the promise of precision medicine by optimizing the use imaging and realistic animal models of cancer (e.g., PDX, GEMM) to better inform of therapeutic outcome in clinical trials. The overall goal of the project is to develop and implement advanced quantitative imaging (QI)/radiomic pipelines to predict response to therapy in TNBC, and integrate to QI/radiomic image features with multi-scale analytic data (-OMICS, path) via machine learning algorithms to enhance prediction. Please visit https://c2ir2.wustl.edu/ for more details.
Quantitative & Computational Imaging
We are developing technology to capture the heterogeneity of diseased tissue via radiomic analyses and computational imaging methods in both preclinical and clinical research. These methods are primarily investigated in the context of co-clinical imaging research.
Obesity and Type-2-Diabetes (T2D) are a result of systemic disturbances in metabolism and inflammation. In light of the highly interconnected and coordinated nature of substrate metabolism in health, and its failure in disease, the goal of the project is to elucidate imaging biomakers for the progression of obesity/diabetes using preclinical animal models of diabetes with a translational endpoint. We are investigating the interplay between metabolism and oxidative stress/inflammation. Reactive oxygen species (ROS) are important mediators in the pathogenesis of a wide range of diseases such as cancer, neurocognitive and neurodenerative disorders, and diabetes mellitus. Of particular interest, is application of imaging ROS/inflammation at the interface with obesity/diabetes and neurological diseases.
Ex-vivo artificial tissue bioreactor
An artificial tissue bioreactor is a versatile system designed to simulate the 3-dimensional (3D) structure and microenvironment of tissues in vivo. In vivo, the responses of individual cells are regulated by spatiotemporal cues that reside in the local microenvironment such as the extracellular matrix (ECM), neighboring cells, soluble factors and physical forces, all presented in a 3D context. When cells are isolated from their in-vivo environment, they are usually placed in a monolayer environment with limited cell-cell contact and bathed in a static medium, thus spatiotemporal cues are lost resulting in a multitude of cells displaying phenotypic instability. We are currently developing a mobile artificial tissue bioreactor to be integrated with imaging instruments to facilitate research, discovery, and validation of therapeutic and imaging biomarkers.
Preclinical Imaging XNAT-enabled Informatics (PIXI)
In collaboration with Dr. Daniel Marcus of the Neuroinformatics Research Group (NRG), we are developing a preclinical imaging informatics platform to manage the workflow of preclinical imaging and to develop computational imaging pipelines in the cloud.