Abstract-Reconstruction algorithms for fluorescence tomography have to address two crucial issues: 1) the ill-posedness of the reconstruction problem, 2) the large scale of numerical problems arising from imaging of 3-D samples. Our contribution is the design and implementation of a reconstruction algorithm that incorporates general regularization . The originality of this work lies in the application of general constraints to fluorescence tomography, combined with an efficient matrix-free strategy that enables the algorithm to deal with large reconstruction problems at reduced memory and computational costs. In the experimental part, we specialize the application of the algorithm to the case of sparsity promoting constraints . We validate the adequacy of regularization for the investigation of phenomena that are well described by a sparse model, using data acquired during phantom experiments.

We report on rapid identification of single bacteria using a low-cost, compact, Raman spectroscope. We demonstrate that a 60-s procedure is sufficient to acquire a comprehensive Raman spectrum in the range of 600 to 3300 cm⁻¹. This time includes localization of small bacteria aggregates, alignment on a single individual, and spontaneous Raman scattering signal collection. Fast localization of small bacteria aggregates, typically composed of less than a dozen individuals, is achieved by lensfree imaging over a large field of view of 24 mm². The lensfree image also allows precise alignment of a single bacteria with the probing beam without the need for a standard microscope. Raman scattered light from a 34-mW continuous laser at 532 nm was fed to a customized spectrometer (prototype Tornado Spectral Systems). Owing to the high light throughput of this spectrometer, integration times as low as 10 s were found acceptable. We have recorded a total of 1200 spectra over seven bacterial species. Using this database and an optimized preprocessing, classification rates of ~90% were obtained. The speed and sensitivity of our Raman spectrometer pave the way for high-throughput and nondestructive real-time bacteria identification assays. This compact and low-cost technology can benefit biomedical, clinical diagnostic, and environmental applications.