A sparse nonnegative demixing algorithm with intrinsic regularization for multiplexed fluorescence tomography

Fluorescence molecular tomography is becoming an important tool in preclinical biomedical imaging of small animals. However, the inability to perform high-throughput imaging of multiple fluorescent targets 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. It uses a suitable “library” of fluorophore signatures to compute a nonnegative least-squares estimate of each fluorophore signal in the sample. The algorithm does not require a regularization parameter, even when the library is rank-deficient. In simulations, we simultaneously demixed four fluorophores with closely overlapping spectral and temporal profiles in a 25 mm diameter cross-sectional area with an RMS error of less than 3% per fluorophore.