Current Research and Scholarly Interests
(1) Imaging of radionuclides with single-cell resolution: Currently, radionuclide tracer imaging is the most sensitive assay for probing subtle biochemical processes in living subjects. Molecular imaging with PET and SPECT has become crucial both for basic science research and for patient management. However, little is known about how those radionuclide tracers interact with cell at the single cell level. I am currently developing a new imaging tool call the radioluminescence microscope that can image these tracers in a standard microscopy environment. This new tool allows researchers at Stanford to visualize how radionuclide tracers distribute in a living cell population.
(2) X-ray molecular imaging: Molecular imaging offers the ability to probe subtle biological signals that are characteristic of disease onset and progression. It can also monitor the response of a disease to treatment before any anatomical changes occur. My research explores two emerging imaging techniques that can probe multiple disease biomarkers in a non-invasive fashion. In both imaging techniques, a contrast agent is introduced that can produce a distinguishable signal when irradiated with X-ray. This feature makes it possible to obtain molecular information during a CT examination. The two imaging techniques differ in the following: In X-ray luminescence imaging, the contrast agent is a radioluminescent nanoparticle that produces near-infrared light under X-ray irradiation. In X-ray fluorescence imaging, the contrast agent is a high-atomic-number element that emits a characteristic X-ray signal under irradiation.
(3) High-performance medical computing: Efficient computing now requires using multi- and many-core processors--which embed multiple computing elements in a single chip. New medical imaging algorithms must be designed that are aware of the parallel computing capabilities of new computer hardware. In my work, I develop medical imaging algorithms adapted to these new parallel architectures. Clinically, those algorithms can shorten the time required to process data by as much as tenfold, removing a critical bottleneck in the clinical workflow. One of the most promising platform for medical computing is the graphics processing unit: originally a gadget sought by serious computer gamers, it is now used as an inexpensive supercomputer on-a-chip by researchers in all fields.
