Evaluation of three MRI-based anatomical priors for quantitative PET brain imaging

In emission tomography, image reconstruction and therefore also tracer development and diagnosis may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject, as it helps to correct for the partial volume effect. One way to implement this, is to use the anatomical image for defining the a priori distribution in a maximum-a-posteriori (MAP) reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate the quantitative accuracy reached by three different anatomical priors when reconstructing positron emission tomography (PET) brain images, using volumetric magnetic resonance imaging (MRI) to provide the anatomical information. The priors are: 1) a prior especially developed for FDG PET brain imaging, which relies on a segmentation of the MR-image (Baete , 2004); 2) the joint entropy-prior (Nuyts, 2007); 3) a prior that encourages smoothness within a position dependent neighborhood, computed from the MR-image. The latter prior was recently proposed by our group in (Vunckx and Nuyts, 2010), and was based on the prior presented by Bowsher (2004). The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. All three priors produced a compromise between noise and bias that was clearly better than that obtained with postsmoothed maximum likelihood expectation maximization (MLEM) or MAP with a relative difference prior. The performance of the joint entropy prior was slightly worse than that of the other two priors. The performance of the segmentation-based prior is quite sensitive to the accuracy of the segmentation. In contrast to the joint entropy-prior, the Bowsher-prior is easily tuned and does not suffer from convergence problems.     

A patient-mounted robotic platform for CT-scan guided procedures

In this paper, we present a novel robotic assistant dedicated to medical interventions under computed tomography scan guidance. This compact and lightweight patient-mounted robot is designed so as to fulfill the requirements of most interventional radiology procedures. It is built from an original 5 DOF parallel structure with a semispherical workspace, particularly well suited to CT-scan interventional procedures. The specifications, the design, and the choice of compatible technological solutions are detailed. A preclinical evaluation is presented, with the registration of the robot in the CT-scan.     

Mesure du Débit d’Absorption Spécifique (das) des téléphones mobiles — contribution du projetcomobio à la normalisation internationale

Human exposure to electromagnetic fields emitted by mobile phones is characterised by the Specific Absorption Rate (sar). This value corresponds to therf power absorbed by unitary mass of the equivalent tissue. It represents the basic restriction used to define limit of human exposure torf electromagnetic fields. Academic and industrial labs have contributed through thecomobio project to define international standards for mobile phones certification.     

Overview of the MPEG Reconfigurable Video Coding Framework

Video coding technology in the last 20 years has evolved producing a variety of different and complex algorithms and coding standards. So far the specification of such standards, and of the algorithms that build them, has been done case by case providing monolithic textual and reference software specifications in different forms and programming languages. However, very little attention has been given to provide a specification formalism that explicitly presents common components between standards, and the incremental modifications of such monolithic standards. The MPEG Reconfigurable Video Coding (RVC) framework is a new ISO standard currently under its final stage of standardization, aiming at providing video codec specifications at the level of library components instead of monolithic algorithms. The new concept is to be able to specify a decoder of an existing standard or a completely new configuration that may better satisfy application-specific constraints by selecting standard components from a library of standard coding algorithms. The possibility of dynamic configuration and reconfiguration of codecs also requires new methodologies and new tools for describing the new bitstream syntaxes and the parsers of such new codecs. The RVC framework is based on the usage of a new actor/ dataflow oriented language called CAL for the specification of the standard library and instantiation of the RVC decoder model. This language has been specifically designed for modeling complex signal processing systems. CAL dataflow models expose the intrinsic concurrency of the algorithms by employing the notions of actor programming and dataflow. The paper gives an overview of the concepts and technologies building the standard RVC framework and the non standard tools supporting the RVC model from the instantiation and simulation of the CAL model to software and/or hardware code synthesis.     

Towards a new tool for the evaluation of the quality of ultrasound compressed images

This paper presents a new tool for the evaluation of ultrasound image compression. The goal is to measure the image quality as easily as with a statistical criterion, and with the same reliability as the one provided by the medical assessment. An initial experiment is proposed to medical experts and represents our reference value for the comparison of evaluation criteria. Twenty-one statistical criteria are selected from the literature. A cumulative absolute similarity measure is defined as a distance between the criterion to evaluate and the reference value. A first fusion method based on a linear combination of criteria is proposed to improve the results obtained by each of them separately. The second proposed approach combines different statistical criteria and uses the medical assessment in a training phase with a support vector machine. Some experimental results are given and show the benefit of fusion.     

A 90-nm CMOS Low-Power GSM/EDGE Multimedia-Enhanced Baseband Processor With 380-MHz ARM926 Core and Mixed-Signal Extensions

To meet the widely varying speed and power requirements of multifunctional mobile devices, an appropriate combination of technology features, circuit-level low-power techniques, and system architecture is implemented in a GSM/Edge baseband processor with multimedia and mixed-signal extensions. Power reduction techniques and performance requirements are derived from an analysis of relevant use cases and applications. The 44 mm² baseband processor is fabricated in a 90-nm low-power CMOS technology with triple-well option and dual-gate oxide core devices. The ARM926 core achieves a maximum clock frequency of 380 MHz at 1.4-V supply due to the usage of thin oxide (1.6 nm) devices. Power dissipation can be adapted to the performance requirements by means of combined voltage and frequency scaling to reduce active power consumption in medium-performance mode by 68%. To reduce leakage currents during standby mode, large SRAM blocks, nFET sleep transistors, and circuit components with relaxed performance requirements are implemented using devices with 2.2-nm gate oxide thickness     

40 Gbit/s RZ wavelength converter in a monolithically integrated chip with a tunable laser

An all-optical wavelength-switch module with 40 Gbit/s capacity is integrated monolithically in InP. The device combines an SOA-based wavelength converter and a fast-tunable 8-channel multi-frequency laser. Error-free operation is demonstrated.     

Assemblies of Functional Peptides and Their Applications in Building Blocks for Biosensors

This feature article highlights our recent applications of functional peptide nanotubes, self‐assembled from short peptides with recognition elements, as building blocks to develop sensors. Peptide nanotubes with high aspect ratios are excellent building blocks for a directed assembly into device configurations, and their combined structures with nanometric diameters and micrometric lengths enables to bridge the “nanoworld” and the “microworld”. When the peptide‐nanotube‐based biosensors, which incorporate molecular recognition units, apply alternating current probes to detect impedance signals, the peptide nanotubes behave as excellent building blocks of the transducer for the detection of target analyes such as pathogens, cells, and heavey metal ions with high specificity. In some sensor configurations, the electric signal can be amplified by coupling them with ion‐specific mineralization via molecular recognition of peptides. In general the detection limit of peptide nanotube chips sensors is very low and the dynamic range of detection can be widened by improved device designs.     

Machine learning to design full-reference image quality assessment algorithm

A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect human observers. The proposed method namely Machine Learning-based Image Quality Measure (MLIQM) first classifies the quality using multi-Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). To evaluate the quality of images, a feature vector containing visual attributes describing images content is constructed. Then, a classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared to the one obtained applying classical Full-Reference Image Quality Assessment (FR-IQA) algorithms to judge the efficiency of the proposed method.     

A New View of Microcrystalline Silicon: The Role of Plasma Processing in Achieving a Dense and Stable Absorber Material for Photovoltaic Applications

To further lower production costs and increase conversion efficiency of thin‐film silicon solar modules, challenges are the deposition of high‐quality microcrystalline silicon (μc‐Si:H) at an increased rate and on textured substrates that guarantee efficient light trapping. A qualitative model that explains how plasma processes act on the properties of μc‐Si:H and on the related solar cell performance is presented, evidencing the growth of two different material phases. The first phase, which gives signature for bulk defect density, can be obtained at high quality over a wide range of plasma process parameters and dominates cell performance on flat substrates. The second phase, which consists of nanoporous 2D regions, typically appears when the material is grown on substrates with inappropriate roughness, and alters or even dominates the electrical performance of the device. The formation of this second material phase is shown to be highly sensitive to deposition conditions and substrate geometry, especially at high deposition rates. This porous material phase is more prone to the incorporation of contaminants present in the plasma during film deposition and is reported to lead to solar cells with instabilities with respect to humidity exposure and post‐deposition oxidation. It is demonstrated how defective zones influence can be mitigated by the choice of suitable plasma processes and silicon sub‐oxide doped layers, for reaching high efficiency stable thin film silicon solar cells.