Laser source 690 nm Spectrometer Translation stage: anesthezia B&W camera NλNλ NxNx NyNy CCD camera NxNx Anne-Sophie MONTCUQUET 1,2, Lionel HERVE 1, Fabrice NAVARRO 1, Jean-Marc DINTEN 1, Jérôme I. MARS 2 1 LETI / LISA – CEA, Minatec, 17 rue des Martyrs, Grenoble Cedex 9, France. 2 DIS, GIPSA-Lab, 961 Rue de la Houille Blanche, BP 46, Saint Martin d'Hères, France. 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 diagnosis 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. Fluorescent imaging Image CEA Therapeutic window Challenge We want to unmix several fluorescence spectra: spectroscopic approach We do not have much information about the fluorescence spectra :. blind source separation Algorithm Optimization Cost function Fluorescent probes location NON-NEGATIVE MATRIX FACTORIZATION UNDER SPARSITY CONSTRAINTS TO UNMIX IN VIVO SPECTRALLY RESOLVED ACQUISITIONS The use of red light limits the biological tissues absorption Injected fluorescent markers bind specifically to a given molecule Experimental set-up. An original regularized NMF algorithm with sparsity constraints is used.. Experiments were performed in vivo to assess the capacity of NMF to unmix overlapping specific fluorescence and autofluorescence spectra.. Spectrally resolved acquisitions combined to NMF processing successfully separate different fluorescent markers or filter different fluorescence contributions of interest from measurements impaired by autofluorescence.. Sparsity constraints lead to best unmixing solutions (simulation experiments have been run to validate) *Andor technologies * * Non-negative Matrix Factorization 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) Results on in vivo measurements We present in this poster a feasibility experiment on a mouse: a capillary tube filled with fluorescent markers is placed subcutaneous to simulate marked targets. By using an regularized NMF algorithm with sparsity constraints, we improve results on spatially sparse markers detection: we successfully unmix three fluorescent sources and eradicate the unwanted autofluorescence signal. For each position N y of the translation stage, an N x x N λ acquisition is measured. Finally, a scanning of the whole object is obtained N y steps HbO 2 Hb H2OH2O In vivo imaging Sparsity term Sparsity We look for spatially sparse specific fluorescence signals (for marked tumors)  we impose a sparsity value on specific fluorescence weights (on column Ap of matrix A): (2) Sparsity= 0.3 Sparsity= 0.5 Sparsity= 0.8 Sparsity= 0.9 Example: Marker unmixing result Weight matrix A Autofluorescence residual specific marker no residual Alexa 750 Mixed dataPost NMF: unmixed data autofluorescence Alexa 750ICG-LNP EXPERIMENT Alexa 750 ICG- LNP incision Capillary tube Conclusion (1)Lee, D. & Seung, H. « Algorithms for Non-negative Matrix Factorization », Advances in neural information processing systems, 2001, 13, (2)Hoyer, P. « Non-negative matrix factorization with sparseness constraints », The Journal of Machine Learning Research, MIT Press Cambridge, MA, USA, 2004, 5, ICG- LNP Autofluorescence Alexa 750 ICG-LNP -- initializations S Wavelength (nm) Algorithm: NMF 1. Initialize and with respectively non-negative constants and non-negative spectra models 2. Update S: 3. Update A: 4. Each column Ap of A referring to weights of specific markers, for a wanted sparsity value φ for coefficients of column Ap, is changed into Ãp: With: 5. Restart steps 2 to 4 until stopping criterion is obtained (for example when for a chosen ε). RESULTS Alexa 750 ICG-LNP