1. Anne-Sophie MONTCUQUET 1,2, Lionel HERVE 1, Jean-Marc DINTEN 1, Jérôme I. MARS 2 1 LETI / LISA – CEA, Minatec, 17 rue des Martyrs, 38054 Grenoble Cedex 9, France. 2 GIPSA-Lab / Dept Images – Signal, 961 Rue de la Houille Blanche, BP 46, 38402 Saint Martin d'Hères, France. anne-sophie.montcuquet@cea.fr Introduction Fluorescent imaging in diffusive media is an emerging imaging modality for medical applications: injected fluorescent markers (in multiplexing, several specific markers are used) bind specifically to targeted compounds, like carcinoma. The region of interest is illuminated with near infrared light and the emitted back fluorescence is analyzed to localize the fluorescence sources. For medical diagnostic application, thick media have to be investigated: as the fluorescence signal gets exponentially weak with the light travel distance, any disturbing signal - such as biological tissues autofluorescence - may be a limiting factor. To remove these unwanted contributions, or to separate different fluorescent markers, a spectroscopic approach and a blind source separation method are explored. We present in this poster a feasibility experiment on an optical phantom in which a marked tumor is simulated. We show how an NMF unmixing preprocessing eradicates the autofluorescence signal of the phantom and allows to get more accurate 3-D reconstructions of the specific marker by Fluorescence Diffuse Optical Tomography (FDOT). Fluorescent imaging Image CEA Non-negative Matrix Factorization Therapeutic window Formal statement NMF applied to spectroscopy Given a non-negative matrix, find non-negative matrices and such that: (P stands for the number of fluorescent sources to unmix) Challenge We want to unmix several fluorescence spectra: A spectroscopic approach is chosen. We do not have much information about the fluorescence spectra : A blind source separation method is required. Conclusion Algorithm Multiplicative update rules Update of A: 1. Initialization of matrices A (constant) and S (spectra models) with A 0 and S 0 > 0 2. Minimization of the cost function F  Update, in turn, of A and S Algorithm steps Fluorescent probes location Non-negative Matrix Factorization: a blind sources separation method applied to optical fluorescence spectroscopy and multiplexing The use of red light limits the biological tissues absorption Injected fluorescent markers bind specifically to a given molecule Experimental set-up Update of S: The fluorescence signal is collected along a line of N x detectors by a spectrometer coupled with a CCD camera: a N x x N λ acquisition is measured. A translation stage, covering N y steps, is then used to get a scanning of the whole object. Feasibility experimentResults (1/2) Autofluorescence (PPIX) ICG-LNP Results (2/2) NMF decomposition gives two distinct fluorescence spectra. An original regularized NMF algorithm is used.. Experiments were performed ex vivo on optical phantoms to assess the capacity of NMF to unmix overlapping specific fluorescence and autofluorescence spectra.. The NMF algorithm is also suitable for in vivo experiments.. Spectrally resolved acquisitions combined to NMF processing successfully separate different fluorescent markers or filter different fluorescence contributions of interest from measurements impaired by autofluorescence.. NMF preprocessing improves FDOT reconstructions of specific fluorescent markers distributions by removing the disturbing fluorescence signals Intensity data Forward model: finite volume method *Andor technologies * * 690 nm