A Sparse Nonnegative Demixing Algorithm with Intrinsic Regularization for Multiplexed Fluorescence T
Fluorescence molecular tomography (FMT) is an optical technique that uses near-infrared light to perform quantitative, three-dimensional imaging of fluorophores in whole animals noninvasively. It is becoming an important tool in preclinical imaging of small animals and has been employed to image tumors and assess
response to anti-cancer therapeutics. However, the inability to perform high-throughput imaging of multiple
fluorescent targets (“multiplexing”) in bulk tissue remains a limitation. Recent work in our group suggests that joint measurement of spectral and temporal fluorophore data can enable robust identification (“demixing”) and localization of at least four concurrent fluorophores. Here we present a novel demixing strategy for this data, which incorporates ideas from sparse subspace clustering and compressed sensing. We will review the basic principles of FMT, present our demixing algorithm, and quantify its performance.
Advisor: Mark Niedre