TOM 1 - Silicon Photonics and Guided-Wave Optics
TOM 2 - Computational, Adaptive and Freeform Optics
TOM 3 - Optical System Design, Tolerancing and Manufacturing
TOM 4 - Bio-Medical Optics
TOM 5 - Resonant Nanophotonics
TOM 6 - Optical Materials: crystals, thin films, organic molecules & polymers, syntheses, characterization and applications
TOM 7 - Thermal radiation and energy management
TOM 8 - Non-linear and Quantum Optics
TOM 9 - Opto-electronic Nanotechnologies and Complex Systems
TOM 10 - Frontiers in Optical Metrology
TOM 11 - Tapered optical fibers, from fundamental to applications
TOM 12 - Optofluidics
TOM 13 - Advances and Applications of Optics and Photonics
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Single-shot 3D endoscopic imaging exploiting a diffuser and neural networks
Julian Lich1, Tom Glosemeyer1, Jürgen Czarske1,2, Robert Kuschmierz1,2
1Laboratory of Measurement and Sensor Systems, TU Dresden, Germany; 2Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Germany
Lens-based endoscopes offer high lateral resolution, but suffer from rigid imaging properties, such as a fixed focal plane. We present a miniaturized 0.5 mm diameter endoscope in which the objective lens is replaced by an optical diffuser. The intensity information of the object space is scattered and passed to a camera via a coherent fibre bundle. The image is reconstructed by a neural network. The field of view and resolution depend on the object distance. 3D-single-shot imaging up to video rate can be enabled. The approach shows great potential for applications like robust 3D fluorescence imaging.
1Technische Universität Ilmenau, Germany; 2Institut für Bioprozess- und Analysenmesstechnik Heiligenstadt, Germany
Light-sheet fluorescence microscopy (LSFM) with single light-sheet illumination enables rapid 3D-imaging of living cells. In this paper we show the design, fabrication and characterization of a diffractive optical element producing several light sheets along an inclined tube for applications in flow-driven imaging. The element, which is based on a multi-focal Fresnel zone plate and a linear grating, generates in combination with a refractive cylindrical lens five thin light sheets of equal intensity.
Herein we show a prototype based on a lens-less fibre optic for fluorescence detection of labelled bio-assay which in this work it has been tested for early diagnosis of sepsis through detection of C-reactive protein (CRP) biomarker. In particular the rationalized optical design of the assay substrate allows to improve the coupling among the emitter and the optical fibre while enhancing the collection efficiency of the fluorescence signal for small numerical apertures. The prototype has been tested in a well-stablish and reproducible standard as it is the immunoglobuline IgG/Anti-IgG assay, reporting an enhancement above one order of magnitude compared to a commercial equipment. The limit-of-detection (LOD) achieved with this prototype for the CRP biomarker in matrices mimicking a real sample is in the clinically relevant range for early diagnosis of sepsis. These results demonstrate the validity of this prototype as an affordable, easy-to-use, compatible with micro-well arrays device for sepsis diagnosis ideal for hospital benches. Moreover, it can be extended to other biomarkers and fluid samples.
Emmanouil Xypakis1,2, Valeria deTuris1, Fabrizio Gala3, Giancarlo Ruocco1, Marco Leonetti1,2,4
1Center for Life Nano- and Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy; 2D-TAILS srl, 00161, Rome, Italy; 3Crestoptics S.p.A. (Italy); 4Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, 00185, Rome, Italy
We developed a physics-informed deep neural network architecture able to achieve signal to noise ratio improvement starting from low exposure noisy data. Our model is based on the nature of the photon detection process characterized by a Poisson probability distribution which we included in the training loss function. Our approach surpasses previous algorithms performance for microscopy data, moreover, the generality of the physical concepts employed here, makes it readily exportable to any imaging context.