64-section multidetector CT of the upper abdomen: optimization of a saline chaser injection protocol for improved vascular and parenchymal contrast enhancement

To prospectively investigate the effect of varying the injection flow rates of a saline chaser on vascular and parenchymal contrast enhancement during abdominal MDCT.

Effect of beam hardening on arterial enhancement in thoracoabdominal CT angiography with increasing patient size: an in vitro and in vivo study

To assess the effect of beam hardening on arterial enhancement in thoracoabdominal computed tomographic (CT) angiography in various body sizes in a phantom and in a clinical study.

Mobile phone technologies and advanced data analysis towards the enhancement of diabetes self-management

Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patient's self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.

Dégradation des forêts et stock de carbone dans la biomasse épigée de la forêt dense humide de Manompana - Nord Est de Madagascar

L’estimation du stock de carbone contenu dans les forêts peut être effectuée de plusieurs manières. Les méthodes les plus connues sont destructives et nécessitent l’abattage d’un grand nombre représentatif d’arbres. Cette représentativité est difficilement atteinte dans les forêts tropicales, présentant une diversité d’espèces exceptionnelles, comme à Madagascar. Afin d’évaluer le niveau de dégradation des forêts, une étude d'images par télédétection est effectuée au moyen de l’analyse du signal radiométrique, combinée à un inventaire non destructif de biomasse. L’étude de la dynamique du paysage proposé est alors basée sur une correction atmosphérique d’une image SPOT 5, de l’année 2009, et sur une classification semi supervisée de l’occupation des sols, combinant une classification préliminaire non supervisée, un échantillonnage aléatoire des classes et une classification supervisée avec un maximum de vraisemblance. La validation est effectuée à l’aide de points indépendants relevés lors des inventaires de biomasse avec des valeurs du stock de carbone bien précises. La classification non supervisée a permis de ressortir deux classes de forêt dénommées « peu dégradée » et « dégradée ». La première désigne l’état climax (le stock de carbone a atteint une valeur qui varie peu) alors que la seconde est caractérisée par un taux de carbone plus faible que le niveau climax, mais qui peut être atteint sans perturbation. Cette première classification permet alors de répartir les placettes d’inventaire dans chaque classe. La méthode d’inventaire recueille à la fois des données dendrométriques classiques (espèce, densité, hauteur totale, hauteur fût, diamètre) et des échantillons représentatifs de branches et de feuilles sur un arbre. Ces différents paramètres avec la densité de bois permettent d’établir une équation allométrique de laquelle est estimée la biomasse totale d’un arbre et conséquemment de la formation forestière. Par la suite, la classification supervisée a été effectuée à partir d’échantillons aléatoires donnant la valeur de séparabilité des classes, de la classification finale. De plus, les valeurs de stocks de carbone à l’hectare, estimées de chaque placette, ont permis de valider cette classification et d’avoir une évaluation de la précision. La connaissance de ce niveau de dégradation issue de données satellitaires à haute résolution spatiale, combinées à des données d’inventaire, ouvre le champ du suivi interannuel du stock de carbone et subséquemment de la modélisation de la situation future du stock de carbone dans différents types de forêts.

Speech Enhancement With Inventory Style Speech Resynthesis

We present a new method for the enhancement of speech. The method is designed for scenarios in which targeted speaker enrollment as well as system training within the typical noise environment are feasible. The proposed procedure is fundamentally different from most conventional and state-of-the-art denoising approaches. Instead of filtering a distorted signal we are resynthesizing a new “clean” signal based on its likely characteristics. These characteristics are estimated from the distorted signal. A successful implementation of the proposed method is presented. Experiments were performed in a scenario with roughly one hour of clean speech training data. Our results show that the proposed method compares very favorably to other state-of-the-art systems in both objective and subjective speech quality assessments. Potential applications for the proposed method include jet cockpit communication systems and offline methods for the restoration of audio recordings.

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.

Gadolinium diethylenetriaminepentaacetate enhancement kinetics in the menisci of asymptomatic subjects: a first step towards a dedicated dGEMRIC (delayed gadolinium-enhanced MRI of cartilage)-like protocol for biochemical imaging of the menisci

It was our aim to investigate the gadolinium diethylenetriaminepentaacetate (Gd-DTPA(2-) ) enhancement kinetics in the menisci of the knee joint over a prolonged period of time. Six asymptomatic volunteers (four men and two women; mean age, 25 ± 2.4 years) were enrolled. Sagittal, T(1) -weighted, spin-echo MR sequences of the right knee joint were obtained at 3 T. Imaging was performed before (baseline), 1 h after and in half-hour intervals up to 9 h after the intravenous administration of 0.2 mmol/kg of Gd-DTPA(2-) . To measure the rates of contrast enhancement relative to the baseline, regions of interest that covered the anterior and posterior horns of the medial and lateral meniscus were defined on each of two adjacent sections, and enhancement curves were constructed. An enhancement peak between 2.5 and 4.5 h after Gd-DTPA(2-) administration was observed, and analysis of variance also revealed no significant difference (p=0.94), in terms of enhancement, within this time interval. Pair-wise, post hoc testing also revealed no significant differences between 2.5 and 3, 3 and 3.5, 3.5 and 4, and 4 and 4.5 h post Gd-DTPA(2-) application. Our preliminary data therefore suggest that the time window suitable for a dGEMRIC (delayed gadolinium-enhanced MRI of cartilage)-like T(1) mapping of the menisci is relatively short, and lies between 2.5 and 4.5 h after Gd-DTPA(2-) injection.

Deforestation and forest degradation monitoring and assessment of biomass and carbon stock of lowland rainforest in the Analanjirofo region, Madagascar

Madagascar is currently developing a policy and strategies to enhance the sustainable management of its natural resources, encouraged by United Nations Framework Convention on Climate Change (UNFCCC) and REDD. To set up a sustainable financing scheme methodologies have to be provided that estimate, prevent and mitigate leakage, develop national and regional baselines, and estimate carbon benefits. With this research study this challenge was tried to be addressed by analysing a lowland rainforest in the Analanjirofo region in the district of Soanierana Ivongo, North East of Madagascar. For two distinguished forest degradation stages: “low degraded forest” and “degraded forest” aboveground biomass and carbon stock was assessed. The corresponding rates of carbon within those two classes were calculated and linked to a multi-temporal set of SPOT satellite data acquired in 1991, 2004 and 2009. Deforestation and particularly degradation and the related carbon stock developments were analysed. With the assessed data for the 3 years 1991, 2004 and 2009 it was possible to model a baseline and to develop a forest prediction for 2020 for Analanjirofo region in the district of Soanierana Ivongo. These results, developed applying robust methods, may provide important spatial information regarding the priorities in planning and implementation of future REDD+ activities in the area.