Fluorescence imaging locates fluorescent markers that specifically bind to targets; like tumors, markers are injected to a patient, optimally excited with near-infrared light, and located thanks to backward-emitted fluorescence analysis. To investigate thick and diffusive media, as the fluorescence signal decreases exponentially with the light travel distance, the autofluorescence of biological tissues comes to be a limiting factor. To remove autofluorescence and isolate specific fluorescence, a spectroscopic approach, based on nonnegative matrix factorization (NMF), is explored. To improve results on spatially sparse markers detection, we suggest a new constrained NMF algorithm that takes sparsity constraints into account. A comparative study between both algorithms is proposed on simulated and in vivo data.
['Anne-Sophie Montcuquet', 'Lionel Hervé', 'Fabrice Navarro', 'Jean-Marc Dinten', 'Jérôme I. Mars']
In Vivo Fluorescence Spectra Unmixing and Autofluorescence Removal by Sparse Nonnegative Matrix Factorization