Bacteria detection with thin wetting film lensless imaging

Allier, Cédric; Hiernard, G.; Poher, V.; Dinten, Jean‐Marc

doi:10.1364/boe.1.000762

Cited by 41 publications

(39 citation statements)

References 10 publications

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“…This original sample holder (illustrated in Figure 1B) thus implements the lensfree on-chip technique reported in Allier et al 14 . Briefly and as illustrated in Figure 1A, the image formed in transmission onto the sensor results from the interference between the light coming directly from the illumination source (here a laser beam), and the light scattered by the bacterial cell(s).…”

Section: Lensfree Imaging Sample Holdermentioning

confidence: 98%

A novel method for single bacteria identification by Raman spectroscopy

et al. 2014

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In this paper we present results on single bacteria rapid identification obtained with a low-cost and compact Raman spectrometer. At present, we demonstrate that a 1 minute procedure, including the localization of single bacterium, is sufficient to acquire comprehensive Raman spectrum in the range of 600 to 3300 cm -1 . Localization and detection of single bacteria is performed by means of lensfree imaging over a large field of view of 24 mm 2 . An excitation source of 532 nm and 30 mW illuminates single bacteria to collect Raman signal into a Tornado Spectral Systems prototype spectrometer (HTVS technology). The acquisition time to record a single bacterium spectrum is as low as 10 s owing to the high light throughput of this spectrometer. The spectra processing features different steps for cosmic spikes removal, background subtraction, and gain normalization to correct the residual inducted fluorescence and substrate fluctuations. This allows obtaining a fine chemical fingerprint analysis. We have recorded a total of 1200 spectra over 7 bacterial species (E. coli, Bacillus species, S. epidermis, M. luteus, S. marcescens). The analysis of this database results in a high classification score of almost 90 %. Hence we can conclude that our setup enables automatic recognition of bacteria species among 7 different species. The speed and the sensitivity (<30 minutes for localization and spectra collection of 30 single bacteria) of our Raman spectrometer pave the way for high-throughput and non-destructive real-time bacteria identification assays. This compact and low-cost technology can benefit biomedical, clinical diagnostic and environmental applications.

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Section: Lensfree Imaging Sample Holdermentioning

confidence: 98%

A novel method for single bacteria identification by Raman spectroscopy

et al. 2014

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“…This technique is used in many areas including the study of fluid flows [3,4] and biomedical imaging [5,6]. Its main advantages are its robustness, simplicity and low cost implementation.…”

Section: Introductionmentioning

confidence: 99%

Self-calibration for lensless color microscopy

et al. 2017

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Lensless color microscopy (also called in-line digital color holography) is a recent quantitative 3D imaging method used in several areas including biomedical imaging and microfluidics. By targetting cost effective and compact designs, the wavelength of the low-end sources used is only imprecisely known, in particular because of their temperature and power supply voltage dependence. This imprecision is the source of biases during the reconstruction step. An additional source of error is the crosstalk phenomenon, i.e., the mixture in color sensors of signals originating from different color channels. We propose to use a parametric inverse problem approach to achieve the self-calibration of a digital color holographic setup. This process provides an estimation of the central wavelengths and of the crosstalk. We show that taking the crosstalk phenomenon into account in the reconstruction step improves its accuracy.

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“…The role of the collection optics is advantageously replaced by data processing approaches aimed at simulating light back-propagation from the sensor plane to the plane where the objects to be investigated are located [5][6][7], thus enabling 3D imaging from the recording of one 2D hologram. Its cost-effectiveness associated with the democratization of high resolution, and high definition imaging sensors made it possible to develop on-chip wide field holographic microscopes [8] suited for the detection of bacteria and viruses [9,10], cytometry [11], or the characterization of protein aggregates [12]. In this case, the resolution of the lensless microscope is driven by the pixel pitch of the chosen sensor, but can be enhanced using pixel super-resolution strategies [13].…”

Section: Introductionmentioning

confidence: 99%

Pixel super-resolution in digital holography by regularized reconstruction

et al. 2016

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In-line digital holography (DH) and lensless microscopy are 3D imaging techniques used to reconstruct the volume of micro-objects in many fields. However, their performances are limited by the pixel size of the sensor. Recently, various pixel super-resolution algorithms for digital holography have been proposed. A hologram with improved resolution was produced from a stack of laterally shifted holograms, resulting in better resolved reconstruction than a single low-resolution hologram. Algorithms for superresolved reconstructions based on inverse problems approaches have already been shown to improve the 3D reconstruction of opaque spheres. Maximum a posteriori (MAP) approaches have also been shown capable of reconstructing the object field more accurately and more efficiently and to extend the usual field-of-view. Here we propose an inverse problem formulation for DH pixel super-resolution and an algorithm that alternates registration and reconstruction steps. The method is described in detail and used to reconstruct synthetic and experimental holograms of sparse 2D objects. We show that our approach improves both the shift estimation and reconstruction quality. Moreover, the reconstructed field-of-view can be expanded by up to a factor 3, thus making it possible to multiply the analyzed area 9 fold.

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Bacteria detection with thin wetting film lensless imaging

Cited by 41 publications

References 10 publications

A novel method for single bacteria identification by Raman spectroscopy

A novel method for single bacteria identification by Raman spectroscopy

Self-calibration for lensless color microscopy

Pixel super-resolution in digital holography by regularized reconstruction

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