Jamal Charara

Magneto-optical waveguides made of cobalt ferrite nanoparticles embedded in silica/zirconia organic-inorganic matrix

This paper describes a way to develop magneto-optical waveguides via sol-gel process. They are made of cobalt ferrite nanoparticles embedded in a silica/zirconia matrix. Thin films are coated on glass substrate using the dip-coating technique. Annealing and UV treatment are applied to finalize sample preparation. Therefore, planar waveguides combining magneto-optical properties with a low refractive index (≈1,5) are obtained. M-lines and free space ellipsometry measurements show a specific Faraday rotation of 250°/cm and a modal birefringence of 1×10−4 at 820 nm. Thus, the mode conversion efficiency can reach a maximum value around 56%.

The influence of the number of endoclips and of mesh incorporation on the strength of an experimental hernia patch repair

The strength conferred to a mesh by fixing it with laparoscopic staples and the effects of tissue incorporation have never been quantified.Eighteen dogs were divided into three groups sacrificed at 2 days (5 dogs), 2 weeks (6 dogs), and 2 months (7 dogs). One 3.5- by 5-cm piece of abdominal wall was removed from each side through a median laparotomy, leaving the skin intact. A polypropylene mesh (5 by 7 cm) was fixed over one defect with four Endopath EMS staples (Ethicon Endo-surgery) and over the other with 16 EMS staples. At sacrifice, bursting strength (BS) was measured with an Instron tester and specimens were studied histologically. One-way analysis of variance and the Newmann-Keuls multiple-comparison test were used.BS tests showed that for each period studied, the strength of the repair performed with 16 staples was significantly higher than that obtained when four staples were applied. They also showed that tensile strength increased significantly in both groups as time elapsed. Light microscopy supported the conclusion that the initial strength of the repair was related to the number of clips and was significantly increased by cellular infiltration at 2 weeks and significantly more by collagen deposition at 2 months. At 2 months, BS was significantly higher in the 16-staples group, suggesting that initial fixation still plays a significant role.

Development of a parallel plate flow chamber for studying cell behavior under pulsatile flow.

&NA; The design of a new parallel plate perfusion chamber for cell behavior studies involving pulsatile flowrates is presented. It was based on fluid mechanical considerations to ensure a region of regular and uniform shear stress at the wall. A numeric solution of the flow was performed to study the effect of pulsating flow on the entrance length. Dye injection investigations in the chamber showed laminar and uniform flow in the culture region under steady state conditions. ASAIO Journal 1995;41:876‐883.

Segmentation of dynamic PET images with kinetic spectral clustering

Segmentation is often required for the analysis of dynamic positron emission tomography (PET) images. However, noise and low spatial resolution make it a difficult task and several supervised and unsupervised methods have been proposed in the literature to perform the segmentation based on semi-automatic clustering of the time activity curves of voxels. In this paper we propose a new method based on spectral clustering that does not require any prior information on the shape of clusters in the space in which they are identified. In our approach, the p-dimensional data, where p is the number of time frames, is first mapped into a high dimensional space and then clustering is performed in a low-dimensional space of the Laplacian matrix. An estimation of the bounds for the scale parameter involved in the spectral clustering is derived. The method is assessed using dynamic brain PET images simulated with GATE and results on real images are presented. We demonstrate the usefulness of the method and its superior performance over three other clustering methods from the literature. The proposed approach appears as a promising pre-processing tool before parametric map calculation or ROI-based quantification tasks.

Quantitative characterization of blood rheological behavior in transient flow with a model including a structure parameter

Transient rheological behavior of blood which involves non newtonian viscosity, elasticity and thixotropy can be modelized with a Maxwell rheological state equation which depends on a structure parameter having dimension of a shear rate. Identification of the model parameters leads to use an exponential apparent shear rate step and to use recursive filters for taking into account the impulse response of the viscometer servo-control device. Typical results for a normal blood sample are given.

Unsupervised Spectral Clustering for Segmentation of Dynamic PET Images

Segmentation of dynamic PET images is needed to extract the time activity curves (TAC) of regions of interest (ROI). These TAC can be used in compartmental models for in vivo quantification of the radiotracer target. While unsupervised clustering methods have been proposed to segment PET sequences, they are often sensitive to initial conditions or favour convex shaped clusters. Kinetic spectral clustering (KSC) of dynamic PET images was recently proposed to handle arbitrary shaped clusters in the space in which they are identified. While improved results were obtained with KSC compared to three state of art methods, its use for clinical applications is still hindered by the manual setting of several parameters. In this paper, we develop an extension of KSC to automatically estimate the parameters involved in the method and to make it deterministic. First, a global search procedure is used to locate the optimal cluster centroids from the projected data. Then an unsupervised clustering criterion is tailored and used in a global optimization scheme to automatically estimate the scale parameter and the weighting factors involved in the proposed Automatic and Deterministic Kinetic Spectral Clustering (AD-KSC). We validate the method using GATE Monte Carlo simulations of dynamic numerical phantoms and present results on real dynamic images. The deterministic results obtained with AD-KSC agree well with those obtained with optimal manual parameterization of KSC, and improve the ROI identification compared to three other clustering methods. The proposed approach could have significant impact for quantification of dynamic PET images in molecular imaging studies.

Contrast improvement in sub- and ultraharmonic ultrasound contrast imaging by combining several hammerstein models.

Sub- and ultraharmonic (SUH) ultrasound contrast imaging is an alternative modality to the second harmonic imaging, since, in specific conditions it could produce high quality echographic images. This modality enables the contrast enhancement of echographic images by using SUH present in the contrast agent response but absent from the nonperfused tissue. For a better access to the components generated by the ultrasound contrast agents, nonlinear techniques based on Hammerstein model are preferred. As the major limitation of Hammerstein model is its capacity of modeling harmonic components only, in this work we propose two methods allowing to model SUH. These new methods use several Hammerstein models to identify contrast agent signals having SUH components and to separate these components from harmonic components. The application of the proposed methods for modeling simulated contrast agent signals shows their efficiency in modeling these signals and in separating SUH components. The achieved gain with respect to the standard Hammerstein model was 26.8 dB and 22.8 dB for the two proposed methods, respectively.

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity

This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series.

Reducing sojourn points from recurrence plots to improve transition detection

The analysis of biomedical signals demonstrating complexity through recurrence plots is challenging. Quantification of recurrences is often biased by sojourn points that hide dynamic transitions. To overcome this problem, time series have previously been embedded at high dimensions. However, no one has quantified the elimination of sojourn points and rate of detection, nor the enhancement of transition detection has been investigated. This paper reports our on-going efforts to improve the detection of dynamic transitions from logistic maps and fetal hearts by reducing sojourn points. Three signal-based recurrence plots were developed, i.e. embedded with specific settings, derivative-based and m-time pattern. Determinism, cross-determinism and percentage of reduced sojourn points were computed to detect transitions. For logistic maps, an increase of 50% and 34.3% in sensitivity of detection over alternatives was achieved by m-time pattern and embedded recurrence plots with specific settings, respectively, and with a 100% specificity. For fetal heart rates, embedded recurrence plots with specific settings provided the best performance, followed by derivative-based recurrence plot, then unembedded recurrence plot using the determinism parameter. The relative errors between healthy and distressed fetuses were 153%, 95% and 91%. More than 50% of sojourn points were eliminated, allowing better detection of heart transitions triggered by gaseous exchange factors. This could be significant in improving the diagnosis of fetal state.

A general framework for modeling sub- and ultraharmonics of ultrasound contrast agent signals with MISO volterra series.

Sub- and ultraharmonics generation by ultrasound contrast agents makes possible sub- and ultraharmonics imaging to enhance the contrast of ultrasound images and overcome the limitations of harmonic imaging. In order to separate different frequency components of ultrasound contrast agents signals, nonlinear models like single-input single-output (SISO) Volterra model are used. One important limitation of this model is its incapacity to model sub- and ultraharmonic components. Many attempts are made to model sub- and ultraharmonics using Volterra model. It led to the design of mutiple-input singe-output (MISO) Volterra model instead of SISO Volterra model. The key idea of MISO modeling was to decompose the input signal of the nonlinear system into periodic subsignals at the subharmonic frequency. In this paper, sub- and ultraharmonics modeling with MISO Volterra model is presented in a general framework that details and explains the required conditions to optimally model sub- and ultraharmonics. A new decomposition of the input signal in periodic orthogonal basis functions is presented. Results of application of different MISO Volterra methods to model simulated ultrasound contrast agents signals show its efficiency in sub- and ultraharmonics imaging.