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Quantitative BioImaging Lab (QBIL)

QBIL Director, Dr. Baowei Fei

QBIL Director, Dr. Baowei Fei

Advances in molecular medicine offer the potential to move beyond traditional cytotoxic anticancer treatments and to develop safer and more effective targeted therapies based on the molecular characteristics of a patient’s tumor. Significant translational research efforts are needed to realize these emerging opportunities. There are urgent needs to develop Quantitative Imaging methods and clinical decision software tools. Such quantitative imaging may require the use of multiple imaging modalities. The development of anatomical, functional, and molecular imaging methods requires proper recognition and addressing the complexities associated with the expression of suspected biomarkers. A full understanding of the response patterns for the potential surrogate biomarkers, e.g. those used to monitor angiogenesis, hypoxia, and necrosis, may often require the use of modeling and/or multiparametric analysis of the image data in order to examine quantitative correlations with other clinical metadata and clinical outcomes. These requirements generally hold for the measurements of responses to drugs or radiation therapy and for image-guided interventions.

Research at the Quantitative BioImaging Lab (QBIL) concentrates on the development and application of Quantitative Imaging technologies. Specifically, we are interested in synthesizing the information obtained from multiple imaging modalities and sources in order to study disease mechanisms and/or to aid in making clinical decisions. Our research goals are to

  1. provide efficient methods and procedures for mapping the properties of tissue in space and time,
  2. integrate multiple information streams acquired from different imaging technologies into a single coherent picture, and
  3. validate and interpret in vivo imaging data for biologic, physiologic, and pathologic interpretation.

The research will combine multimodality imaging and multidimensional data to exploit our current knowledge of the genetic and molecular bases of various diseases and therefore to have substantial positive implications for disease prevention, detection, diagnosis, and therapy.