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We introduce an innovative wristband wireless device based on a dual wavelength NDIR optical measurement and an optimized thermo-fluidic channel to improve the extraction of the carbon dioxide gas from the blood within the heated skin region. We describe a signal processing model combining an innovative linear quadratic model of the optical measurement and a fluidic model. The evaluation is achieved using a cardiopulmonary exercise test (CPET). We compare carbon dioxide tension measurement at the forearm level using our device, with an electrochemical measurement at the forearm level, and an optical measurement of the end-tidal exhaled breath. These curves demonstrate a significant reduction of the variability of carbon dioxide pressure measurement with respect to the pressure dynamic range during the test.

In medical applications, quantitative analysis of breath may open new prospects for diagnosis or for patient monitoring. To detect acetone, a breath biomarker for diabetes, we use a single metal-oxide (MOX) gas sensor working in a dual temperature mode. We propose a linear-quadratic model to describe the mixing model mapping gas concentrations to MOX sensor responses. In this purpose, it is necessary to inverse the nonlinear problem in order to quantify the component of the gas mixture. As a proof of concept, we study a mixture of two gases, acetone and ethanol diluted in air buffer. In order to estimate the concentration of each gas, we introduce a supervised Bayesian source separation method. Based on MCMC stochastic sampling methods to estimate the mean of the posterior distribution, this Bayesian approach is robust to noise for solving this ill-posed non-linear inversion problem. We analyze the performance on a set of samples associated with a set of gas concentration covering the range suitable for exhaled breath. We use a cross-validation approach, calibrating the mixing parameters with some samples and validating the source estimation with others. Our new supervised method applied on a linear-quadratic model allows to estimate acetone and ethanol concentration with a precision of around 2 ppm.

The aim of our work is to quantify two gases (acetone and ethanol) diluted in an air buffer using only a single metal oxide (MOX) sensor. We took advantage of the low selectivity of the MOX sensor, exploiting a dual-temperature mode. Working at two temperatures of the MOX sensitive layer allowed us to obtain diversity in the measures. Two virtual sensors were created to characterize our gas mixture. We presented a linear-quadratic mixture sensing model which was closer to the experimental data. To validate this model and the experimental protocol, we inverted the system of quadratic equations to quantify a mixture of the two gases. The linear-quadratic model was compared to the bilinear model proposed in the literature. We presented an experimental evaluation on mixtures made of a few ppm of acetone and ethanol, and we obtained a precision close to the ppm. This is an important step towards medical applications, particularly in terms of diabetes, to deliver a non-invasive measure with a low-cost device., Competing Interests: The authors declare no conflict of interest.

Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algorithm and two more recent ones (BHI-PRO 1 and 2). Given the high technological variability, the quantification failed to restitute the known quantities of five out of nine proteins present in a controlled solution. There was a linear relationship between protein quantities and peak intensities for four out of nine peaks with all algorithms. The biological component of the variance was higher with BHI-PRO than with the classical algorithm (80-95% with BHI-PRO 1, 79-95% with BHI-PRO 2 vs. 56-90%); thus, BHI-PRO were more efficient in protein quantification. The technological component of the variance was higher with the classical algorithm than with BHI-PRO (6-25% vs. 2.5-9.6% with BHI-PRO 1 and 3.5-11.9% with BHI-PRO 2). The chemical component was also higher with the classical algorithm (3.6-18.7% vs. < 3.5%). Thus, BHI-PRO were better in removing noise from signal when the expected peaks are detected. Overall, either BHI-PRO algorithm may reduce the technological variance from 25 to 10% and thus improve protein quantification and biomarker validation., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Analysis of a fluid mixture using a chromatographic system is a standard technique for many biomedical applications such as in-vitro diagnostic of body fluids or air and water quality assessment. The analysis is often dedicated towards a set of molecules or biomarkers. However, due to the fluid complexity, the number of mixture components is often larger than the list of targeted molecules. In order to get an analysis as exhaustive as possible and also to take into account possible interferences, it is important to identify and to quantify all the components that are included in the chromatographic signal. Thus the signal processing aims to reconstruct a list of an unknown number of components and their relative concentrations. We address this question as a problem of sparse representation of a chromatographic signal. The innovative representation is based on a stochastic forward model describing the transport of elementary molecules in the chromatography column as a molecular random walk. We investigate three methods: two probabilistic Bayesian approaches, one parametric and one non-parametric, and a determinist approach based on a parsimonious decomposition on a dictionary basis. We examine the performances of these 3 approaches on an experimental case dedicated to the analysis of mixtures of the micro-pollutants Polycyclic Aromatic Hydrocarbons (PAH) in a methanol solution in two cases of high and low signal to noise ratio (SNR).

This work concerns 2D + t dynamic tomography. We show that a much larger class of deformations than the affine transforms can be compensated analytically within filtered back projection algorithms in 2D parallel beam and fan beam dynamic tomography. We present numerical experiments on the Shepp and Logan phantom showing that nonaffine deformations can be compensated. A generalization to 3D cone beam tomography is proposed.

There is a need to precisely measure concentration of proteins in biological substance for early diagnosis of disease or knowledge of fundamental biological processes. Many apparatus have been proposed, and now data processing methods have to be investigated. This paper focuses on data processing of proteomic experiments combining nano liquid chromatography and mass spectrometry. Experimental fluctuations of this process raise an interest for robust methods. Consequently, we propose a model of this acquisition system and a probabilistic Bayesian method to estimate the proteins' concentrations and system parameters.

This paper presents a full proteomics analysis LC-MS (Liquid Chromatography-Mass Spectrometry) chain combining bio, nano and information technologies in order to quantify targeted proteins in blood sample. The objective is to enable an early detection of pancreatic cancer. We focus on the data processing step which estimates the proteins' concentration. First, we pre-process the data in order to correct time shift between the experiments. We propose to use block matching algorithm. Second, quantification of protein is performed using chemometrics approaches and more precisely CLS, PLS, N-PLS and PARAFAC algorithms. Performances of the various methods have been compared on cytochrome c protein LC-MS analyses.

Detecting proteins in human blood holds the promise of a revolution in cancer diagnosis. Also, the ability to perform laboratory operations on small scales using miniaturized (lab-on-a-chip) devices has many benefits. Designing and fabricating such systems is extremely challenging, but physicists and engineers are beginning to construct such highly integrated and compact labs on chips with exciting functionality. This paper focuses on the presentation of the requirements of the information technology layer in such an integrated platform been developed in the LOCCANDIA project. LOCCANDIA is a Specific Targeted Research project (STREP) funded under the 6th Framework program of the EC. Its ultimate objective is to develop an innovative nano-technology based (lab-on-a-chip) platform for the medical-proeomics field. The paper presents the main engineering aspects, challenges and architecture for creating an Integrated Clinico-Proteomic Environment. The environment will be used to monitor and document the analysis and discovery chain and to allow the physician to interpret the digital spectrogram data delivered by the mass spectrometer, for diagnostic purposes.

This work is dedicated to the reduction of reconstruction artefacts due to motion occurring during the acquisition of computerized tomographic projections. This problem has to be solved when imaging moving organs such as the lungs or the heart. The proposed method belongs to the class of motion compensation algorithms, where the model of motion is included in the reconstruction formula. We address two fundamental questions. First what conditions on the deformation are required for the reconstruction of the object from projections acquired sequentially during the deformation, and second how do we reconstruct the object from those projections. Here we answer these questions in the particular case of 2D general time-dependent affine deformations, assuming the motion parameters are known. We treat the problem of admissibility conditions on the deformation in the parallel-beam and fan-beam cases. Then we propose exact reconstruction methods based on rebinning or sequential FBP formulae for each of these geometries and present reconstructed images obtained with the fan-beam algorithm on simulated data.

We give the sampling conditions of the 3D fan-beam x-ray transform (3DFBXRT). The motivation of this work lies in the fact that helical tomography with a single detector line is simply a sampling of this transform under the helical constraint. We give a precise description of the geometry of the essential support of the 3DFBXRT Fourier transform and show how to derive efficient sampling schemes. We then give efficient sampling schemes in parallel helical tomography. We present numerical experiments showing that efficient sampling on hexagonal interlaced schemes yields better reconstructions than the standard schemes in both parallel helical tomography (using QDO) and 3DFBXRT. We discuss the practical drawbacks and advantages of these efficient schemes and the possible extension to fan-beam helical CT.

Dynamic cone-beam reconstruction algorithms are required to reconstruct three-dimensional (3D) image sequences on dynamic 3D CT combining multi-row two-dimensional (2D) detectors and sub-second scanners. The speed-up of the rotating gantry allows one to improve the temporal resolution of the image sequence, but at the same time, it implies increase in the dose delivered during a given time period to keep constant the signal-to-noise ratio associated with each frame. The alternative solution proposed in this paper is to process data acquisition on several half-turns in order to reduce the dose delivered per rotation with the same signal-to-noise ratio. In order to compensate for time evolution and motion artefacts, we propose to use a dynamic particle model to describe the object evolution during the scan. In this article, we first introduce the dynamic particle model and the dynamic CT acquisition model. Then, we explain the principle of the proposed dynamic cone-beam reconstruction algorithm. Lastly, we present preliminary results on simulated data.

Some recent medical imaging applications such as functional imaging (PET and SPECT) or interventional imaging (CT fluoroscopy) involve increasing amounts of data. In order to reduce the image reconstruction time, we develop a new fast 3D reconstruction algorithm based on a divide and conquer approach. The proposed multichannel algorithm performs an indirect frequential subband decomposition of the image f to be reconstructed (f = sigma fj) through the filtering of the projections Rf. The subband images fj are reconstructed on a downsampled grid without information suppression. In order to reduce the computation time, we do not backproject the null filtered projections and we downsample the number of projections according to the Shannon conditions associated with the subband image. Our algorithm is based on filtering and backprojection operators. Using the same algorithms for these basic operators, our approach is three and a half times faster than a classical FBP algorithm for a 2D image 512 x 512 and six times faster for a 3D image 32 x 512 x 512.

This paper presents an iterative method based on the minimal residual algorithm for tomographic attenuation compensated reconstruction from attenuated cone-beam projections given the attenuation distribution. Unlike conjugate-gradient based reconstruction techniques, the proposed minimal residual based algorithm solves directly a quasisymmetric linear system, which is a preconditioned system. Thus it avoids the use of normal equations, which improves the convergence rate. Two main contributions are introduced. First, a regularization method is derived for quasisymmetric problems, based on a Tikhonov-Phillips regularization applied to the factorization of the symmetric part of the system matrix. This regularization is made spatially adaptive to avoid smoothing the region of interest. Second, our existing reconstruction algorithm for attenuation correction in parallel-beam geometry is extended to cone-beam geometry. A circular orbit is considered. Two preconditioning operators are proposed: the first one is Grangeat's inversion formula and the second one is Feldkamp's inversion formula. Experimental results obtained on simulated data are presented and the shadow zone effect on attenuated data is illustrated.