Dr. Guillermo Tearney is Professor of Pathology at Harvard Medical School, Mike and Sue Hazard Family MGH Research Scholar, an Affiliated Faculty member of the Harvard-MIT Division of Health Sciences and Technology (HST), Fellow of the American College of Cardiologists (FACC), Fellow of the College of American Pathologists (FCAP) and heads his own lab at the Wellman Center for Photomedicine at the Massachusetts General Hospital. Dr. Tearney received his MD magna cum laude from Harvard Medical School and received his PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. He was the guest speaker at a lecture organized by the MGH Gordon Center. Below is the presentation summary provided through the courtesy of Dr. Tearney.
Today’s gold standard for medical diagnosis is histology of excised biopsies or surgical specimens where tissue is taken out of the body, processed, sectioned, stained and looked at under a light microscope by a pathologist. There are many limitations of this technique, including the fact that it is inherently invasive, time consuming, costly, and dangerous for some organs. Furthermore, oftentimes the diseased tissue is not readily seen by visual inspection and as a result the tissue is sampled at a random location, which can be highly inaccurate.
If we could instead conduct microscopy inside the body, then we could provide tools for screening, targeting biopsies, making primary disease diagnosis, and guiding intervention on the cellular basis. This promise of has motivated the development of a new field, termed in vivo microscopy, the goal of which is to obtain microscopic images from living human patients. Two in vivo microscopy technologies, confocal microscopy and optical coherence tomography, are currently available and in clinical use. Upcoming developments, including whole organ microscopy, swallowable microscopy capsules, molecular imaging, and very high resolution microscopic devices are in the pipeline and will likely revolutionize how disease is diagnosed and how medicine is practiced in the future. Both of these patch-based techniques capture local image features through particular learning processes. They have been demonstrated to improve conventional MAP methods in reconstructing simulated and patient PET data, showing their potential for clinical applications.