As the number of cancers is steadily increasing, doctors are in need of automatic tools with better and faster analysis methods to help them with the diagnosis. One way to tackle this challenge is to propose label-free methods capable to analyze a large number of samples. Recent development in photonics components could enable to use infrared light to detect abnormal tissues and Mid-IR imaging can provide an unequivocal information about the biochemical composition of human cells. The combination of a set of Quantum Cascade Lasers (QCLs) and lensfree imaging with uncooled bolometer matrix will allow the biochemical mapping over a wide field of view. This experimental setup coupled to machine learning algorithms (Random Forest, Neural Networks, K-means) can help to classify the biological cells in a fast and reproducible way. Images from the frozen section tissue of nude mice bearing human orthotropic oral cavity tumors from the CAL33 cell line have been acquired and analyzed. Using amide and DNA absorption bands, we achieved up to 94% of successful predictions of cancer cells with a population of 325 pixels corresponding to muscle tissues and 325 pixels corresponding to cancer tissues. This work may lead to the development of an imaging device, that could be used for cancer diagnosis at hospital.
Microbial identification is a critical process aiming at identifying the species contained in a biological sample, with applications in healthcare, industry or even national security. Traditionally, this process relies either on MALDI-TOF mass spectroscopy, on biochemical tests and on the observation of the morphology of colonies after growth on a Petri dish. Here is presented an innovative method for label-free optical identification of pathogens, based on the multispectral infrared imaging of colonies. This lensless imaging technique enables a high-throughput analysis and wide-field analysis of agar plates. It could yield very high correct identification rates as it relies on an optical fingerprint gathering both spectroscopic and morphologic features. The setup consists of a Quantum Cascade Lasers light source and an imager, a square 2.72 by 2.72 mm uncooled bolometer array. Microorganisms to be analyzed are streaked on a porous growth support compatible with infrared imaging, laid on top of an agar plate for incubation. When imaging is performed, growth support is put in close contact with the imaging sensor and illuminated at different wavelengths. After acquisition, an image descriptor based on spectral and morphological features is extracted for each microbial colony. Supervised classification is finally performed with a Support Vector Machine algorithm and tested with tenfold cross-validation. A first database collecting 1012 multispectral images of colonies belonging to five different species has already been acquired with this system, resulting in a correct identification rate of 92%. For these experiments, multispectral images are acquired at nine different wavelengths, between 5.6 and 8 μm. Considering the optimization possibilities of the image descriptors currently used and the ongoing development of the uncooled bolometers technology, these very first results are promising and could be dramatically improved with further experiments. Thereby, mid-infrared multispectral lensless imaging has the potential to become a fast and precise Petri dish analysis technology.
Spontaneous Raman scattering is a reliable technique for fast identification of single bacterial cells, when spectra are acquired in laboratory conditions where bacteria growth and state are controlled. We have developed a multi-modal system combining Raman spectroscopy and darkfield imaging, aiming at analysing environmental samples, typically in the field context of biological pathogens detection. Such samples are heterogeneous, both in terms of phenotype content and environmental matrix, even after a preliminary purification step. In this paper, we report a study on the identification of Bacillus Thuriengensis (BT) mimicing pathogen bacteria, embedded in a real-world matrix: a sample of surface water enriched with environmental bacterial species. The purpose is to evaluate both the detection limit of aging BT over time and the false alarm rate, in the conditions of our experiment.
Flow cytometry is the main technology used in hematology analyzers. However, this technology requires bulky and complex hardware systems. Lens-free imaging is an emerging microscopy technique based on a simple and compact inline holography setup. This technique enables to image a large field-of-view (≈30mm²) leading to statistical counting (>10 000 cells) in a single-shot acquisition consistent with performances required in hematology. We report high accuracy platelet count in 54 platelet-rich plasma samples. This accuracy can be achieved through a wise choice of the illumination spectral properties and an optimized algorithmic chain dedicated to small pure phase objects.
Recent developments in energy-discriminating Photon-Counting Detector (PCD) enable new horizons for spectral CT. With PCDs, new reconstruction methods take advantage of the spectral information measured through energy measurement bins. However PCDs have serious spectral distortion issues due to charge-sharing, fluorescence escape, pileup effect… Spectral CT with PCDs can be decomposed into two problems: a noisy geometric inversion problem (as in standard CT) and an additional PCD spectral degradation problem. The aim of the present study is to introduce a reconstruction method which solves both problems simultaneously: a “one-step” approach. An explicit linear detector model is used and characterized by a Detector Response Matrix. The algorithm reconstructs two basis material maps from energy-window transmission data. The results prove that the simultaneous inversion of both problems is well performed for simulation data. For comparison, we also perform a standard “two-step” approach: an advanced polynomial decomposition of measured sinograms combined with a filtered-back projection reconstruction. The results demonstrate the potential uses of this method for medical imaging or for non-destructive testing in industry.
Recent advances in the domain of energy-resolved semiconductor detectors stimulate research in X-ray computed
tomography (CT). However, the imperfections of these detectors induce errors that should be considered for further
applications. Charge sharing and pile-up effects due to high photon fluxes can degrade image quality or yield wrong
material identification. Basis component decomposition provides separate images of principal components, based on the
energy related information acquired in each energy bin. The object is typically either decomposed in photoelectric and
Compton physical effects or in basis materials functions.
This work presents a simulation study taking into account the properties of an energy-resolved CdTe detector with
flexible energy thresholds in the context of materials decomposition CT. We consider the effects of a first order pile-up
model with triangular pulses of a non-paralyzable detector and a realistic response matrix. We address the problem of
quantifying mineral content in bone based on a polynomial approach for material decomposition in the case of two and
three energy bins. The basis component line integrals are parameterized directly in the projection domain and a
conventional filtered back-projection reconstruction is performed to obtain the material component images. We use
figures of merit such as noise and bias to select the optimal thresholds and quantify the mineral content in bone. The
results obtained with an energy resolved detector for two and three energy bins are compared with the ones obtained for
the dual-kVp technique using an integrating-mode detector with filters and voltages optimized for bone densitometry.
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