3G network traffic sources measurement and analysis

In this paper, we show statistical analyses of several types of traffic sources in a 3G network, namely voice, video and data sources. For each traffic source type, measurements were collected in order to, on the one hand, gain better understanding of the statistical characteristics of the sources and, on the other hand, enable forecasting traffic behaviour in the network. The latter can be used to estimate service times and quality of service parameters. The probability density function, mean, variance, mean square deviation, skewness and kurtosis of the interarrival times are estimated by Wolfram Mathematica and Crystal Ball statistical tools. Based on evaluation of packet interarrival times, we show how the gamma distribution can be used in network simulations and in evaluation of available capacity in opportunistic systems. As a result, from our analyses, shape and scale parameters of gamma distribution are generated. Data can be applied also in dynamic network configuration in order to avoid potential network congestions or overflows. Copyright © 2013 John Wiley & Sons, Ltd.

Characterization of microcirculation in multiple sclerosis lesions by dynamic texture parameter analysis (DTPA)

OBJECTIVE Texture analysis is an alternative method to quantitatively assess MR-images. In this study, we introduce dynamic texture parameter analysis (DTPA), a novel technique to investigate the temporal evolution of texture parameters using dynamic susceptibility contrast enhanced (DSCE) imaging. Here, we aim to introduce the method and its application on enhancing lesions (EL), non-enhancing lesions (NEL) and normal appearing white matter (NAWM) in multiple sclerosis (MS). METHODS We investigated 18 patients with MS and clinical isolated syndrome (CIS), according to the 2010 McDonald's criteria using DSCE imaging at different field strengths (1.5 and 3 Tesla). Tissues of interest (TOIs) were defined within 27 EL, 29 NEL and 37 NAWM areas after normalization and eight histogram-based texture parameter maps (TPMs) were computed. TPMs quantify the heterogeneity of the TOI. For every TOI, the average, variance, skewness, kurtosis and variance-of-the-variance statistical parameters were calculated. These TOI parameters were further analyzed using one-way ANOVA followed by multiple Wilcoxon sum rank testing corrected for multiple comparisons. RESULTS Tissue- and time-dependent differences were observed in the dynamics of computed texture parameters. Sixteen parameters discriminated between EL, NEL and NAWM (pAVG = 0.0005). Significant differences in the DTPA texture maps were found during inflow (52 parameters), outflow (40 parameters) and reperfusion (62 parameters). The strongest discriminators among the TPMs were observed in the variance-related parameters, while skewness and kurtosis TPMs were in general less sensitive to detect differences between the tissues. CONCLUSION DTPA of DSCE image time series revealed characteristic time responses for ELs, NELs and NAWM. This may be further used for a refined quantitative grading of MS lesions during their evolution from acute to chronic state. DTPA discriminates lesions beyond features of enhancement or T2-hypersignal, on a numeric scale allowing for a more subtle grading of MS-lesions.

Real-Time In Vivo Characterization of Primary Liver Tumors With Diffuse Optical Spectroscopy During Percutaneous Needle Interventions: Feasibility Study in Woodchucks

OBJECTIVE This study presents the first in vivo real-time optical tissue characterization during image-guided percutaneous intervention using near-infrared diffuse optical spectroscopy sensing at the tip of a needle. The goal of this study was to indicate transition boundaries from healthy tissue to tumors, namely, hepatic carcinoma, based on the real-time feedback derived from the optical measurements. MATERIALS AND METHODS Five woodchucks with hepatic carcinoma were used for this study. The woodchucks were imaged with contrast-enhanced cone beam computed tomography with a flat panel detector C-arm system to visualize the carcinoma in the liver. In each animal, 3 insertions were performed, starting from the skin surface toward the hepatic carcinoma under image guidance. In 2 woodchucks, each end point of the insertion was confirmed with pathologic examination of a biopsy sample. While advancing the needle in the animals under image guidance such as fluoroscopy overlaid with cone beam computed tomography slice and ultrasound, optical spectra were acquired at the distal end of the needles. Optical tissue characterization was determined by translating the acquired optical spectra into clinical parameters such as blood, water, lipid, and bile fractions; tissue oxygenation levels; and scattering amplitude related to tissue density. The Kruskal-Wallis test was used to study the difference in the derived clinical parameters from the measurements performed within the healthy tissue and the hepatic carcinoma. Kurtoses were calculated to assess the dispersion of these parameters within the healthy and carcinoma tissues. RESULTS Blood and lipid volume fractions as well as tissue oxygenation and reduced scattering amplitude showed to be significantly different between the healthy part of the liver and the hepatic carcinoma (P < 0.05) being higher in normal liver tissue. A decrease in blood and lipid volume fractions and tissue oxygenation as well as an increase in scattering amplitude were observed when the tip of the needle crossed the margin from the healthy liver tissue to the carcinoma. The kurtosis for each derived clinical parameter was high in the hepatic tumor as compared with that in the healthy liver indicating intracarcinoma variability. CONCLUSIONS Tissue blood content, oxygenation level, lipid content, and tissue density all showed significant differences when the needle tip was guided from the healthy tissue to the carcinoma and can therefore be used to identify tissue boundaries during percutaneous image-guided interventions.

Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.