Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Multi-stage ytterbium fiber-amplifier seeded by a gain-switched laser diode

We demonstrated all-fiber amplification of 11 ps pulses from a gain-switched laser diode at 1064 nm. The diode was driven at a repetition rate of 40 MHz and delivered 13 µW of fiber-coupled average output power. For the low output pulse energy of 325 fJ we have designed a multi-stage core pumped pre-amplifier in order to keep the contribution of undesired amplified spontaneous emission as low as possible. By using a novel time-domain approach for determining the power spectral density ratio (PSD) of signal to noise, we identified the optimal working point for our pre-amplifier. After the pre-amplifier we reduced the 40 MHz repetition rate to 1 MHz using a fiber coupled pulse-picker. The final amplification was done with a cladding pumped Yb-doped large mode area fiber and a subsequent Yb-doped rod-type fiber. With our setup we reached a total gain of 73 dB, resulting in pulse energies of >5.6 µJ and peak powers of >0.5 MW. The average PSD-ratio of signal to noise we determined to be 18/1 at the output of the final amplification stage.

Hypervascular liver tumors: low tube voltage, high tube current multi-detector row CT for enhanced detection--phantom study

PURPOSE: To prospectively evaluate, for the depiction of simulated hypervascular liver lesions in a phantom, the effect of a low tube voltage, high tube current computed tomographic (CT) technique on image noise, contrast-to-noise ratio (CNR), lesion conspicuity, and radiation dose. MATERIALS AND METHODS: A custom liver phantom containing 16 cylindric cavities (four cavities each of 3, 5, 8, and 15 mm in diameter) filled with various iodinated solutions to simulate hypervascular liver lesions was scanned with a 64-section multi-detector row CT scanner at 140, 120, 100, and 80 kVp, with corresponding tube current-time product settings at 225, 275, 420, and 675 mAs, respectively. The CNRs for six simulated lesions filled with different iodinated solutions were calculated. A figure of merit (FOM) for each lesion was computed as the ratio of CNR2 to effective dose (ED). Three radiologists independently graded the conspicuity of 16 simulated lesions. An anthropomorphic phantom was scanned to evaluate the ED. Statistical analysis included one-way analysis of variance. RESULTS: Image noise increased by 45% with the 80-kVp protocol compared with the 140-kVp protocol (P < .001). However, the lowest ED and the highest CNR were achieved with the 80-kVp protocol. The FOM results indicated that at a constant ED, a reduction of tube voltage from 140 to 120, 100, and 80 kVp increased the CNR by factors of at least 1.6, 2.4, and 3.6, respectively (P < .001). At a constant CNR, corresponding reductions in ED were by a factor of 2.5, 5.5, and 12.7, respectively (P < .001). The highest lesion conspicuity was achieved with the 80-kVp protocol. CONCLUSION: The CNR of simulated hypervascular liver lesions can be substantially increased and the radiation dose reduced by using an 80-kVp, high tube current CT technique.

Advances of dual source, dual-energy imaging in postmortem CT

This paper focuses on the use of multi-detector row dual-energy computed tomography (DECT) in the evaluation of postmortal examinations. The use of dual energy moves postmortem CT to an entirely new dimension of diagnostic sensitivity where contrast in the image is not merely limited to X-ray attenuation differences, but may include elements of functional and tissue characterization. This additional information may be used to improve the benefit postmortem imaging can provide to supplement and simplify the conventional autopsy.

High precision range estimation techniques for multi-path wireless environments

Range estimation is the core of many positioning systems such as radar, and Wireless Local Positioning Systems (WLPS). The estimation of range is achieved by estimating Time-of-Arrival (TOA). TOA represents the signal propagation delay between a transmitter and a receiver. Thus, error in TOA estimation causes degradation in range estimation performance. In wireless environments, noise, multipath, and limited bandwidth reduce TOA estimation performance. TOA estimation algorithms that are designed for wireless environments aim to improve the TOA estimation performance by mitigating the effect of closely spaced paths in practical (positive) signal-to-noise ratio (SNR) regions. Limited bandwidth avoids the discrimination of closely spaced paths. This reduces TOA estimation performance. TOA estimation methods are evaluated as a function of SNR, bandwidth, and the number of reflections in multipath wireless environments, as well as their complexity. In this research, a TOA estimation technique based on Blind signal Separation (BSS) is proposed. This frequency domain method estimates TOA in wireless multipath environments for a given signal bandwidth. The structure of the proposed technique is presented and its complexity and performance are theoretically evaluated. It is depicted that the proposed method is not sensitive to SNR, number of reflections, and bandwidth. In general, as bandwidth increases, TOA estimation performance improves. However, spectrum is the most valuable resource in wireless systems and usually a large portion of spectrum to support high performance TOA estimation is not available. In addition, the radio frequency (RF) components of wideband systems suffer from high cost and complexity. Thus, a novel, multiband positioning structure is proposed. The proposed technique uses the available (non-contiguous) bands to support high performance TOA estimation. This system incorporates the capabilities of cognitive radio (CR) systems to sense the available spectrum (also called white spaces) and to incorporate white spaces for high-performance localization. First, contiguous bands that are divided into several non-equal, narrow sub-bands that possess the same SNR are concatenated to attain an accuracy corresponding to the equivalent full band. Two radio architectures are proposed and investigated: the signal is transmitted over available spectrum either simultaneously (parallel concatenation) or sequentially (serial concatenation). Low complexity radio designs that handle the concatenation process sequentially and in parallel are introduced. Different TOA estimation algorithms that are applicable to multiband scenarios are studied and their performance is theoretically evaluated and compared to simulations. Next, the results are extended to non-contiguous, non-equal sub-bands with the same SNR. These are more realistic assumptions in practical systems. The performance and complexity of the proposed technique is investigated as well. This study’s results show that selecting bandwidth, center frequency, and SNR levels for each sub-band can adapt positioning accuracy.

STOCHASTIC CHARGE TRANSPORT IN MULTI-ISLAND SINGLE-ELECTRON TUNNELING DEVICES

The physics of the operation of singe-electron tunneling devices (SEDs) and singe-electron tunneling transistors (SETs), especially of those with multiple nanometer-sized islands, has remained poorly understood in spite of some intensive experimental and theoretical research. This computational study examines the current-voltage (IV) characteristics of multi-island single-electron devices using a newly developed multi-island transport simulator (MITS) that is based on semi-classical tunneling theory and kinetic Monte Carlo simulation. The dependence of device characteristics on physical device parameters is explored, and the physical mechanisms that lead to the Coulomb blockade (CB) and Coulomb staircase (CS) characteristics are proposed. Simulations using MITS demonstrate that the overall IV characteristics in a device with a random distribution of islands are a result of a complex interplay among those factors that affect the tunneling rates that are fixed a priori (e.g. island sizes, island separations, temperature, gate bias, etc.), and the evolving charge state of the system, which changes as the source-drain bias (VSD) is changed. With increasing VSD, a multi-island device has to overcome multiple discrete energy barriers (up-steps) before it reaches the threshold voltage (Vth). Beyond Vth, current flow is rate-limited by slow junctions, which leads to the CS structures in the IV characteristic. Each step in the CS is characterized by a unique distribution of island charges with an associated distribution of tunneling probabilities. MITS simulation studies done on one-dimensional (1D) disordered chains show that longer chains are better suited for switching applications as Vth increases with increasing chain length. They are also able to retain CS structures at higher temperatures better than shorter chains. In sufficiently disordered 2D systems, we demonstrate that there may exist a dominant conducting path (DCP) for conduction, which makes the 2D device behave as a quasi-1D device. The existence of a DCP is sensitive to the device structure, but is robust with respect to changes in temperature, gate bias, and VSD. A side gate in 1D and 2D systems can effectively control Vth. We argue that devices with smaller island sizes and narrower junctions may be better suited for practical applications, especially at room temperature.

Multi-locus microsatellite analysis supports the hypothesis of an autochthonous focus of Echinococcus multilocularis in northern Italy

Echinococcus multilocularis is characterised by a wide geographical distribution, encompassing three continents (North America, Asia and Europe) yet very low genetic variability is documented. Recently, this parasite has been detected in red foxes (Vulpes vulpes) circulating in an Alpine region of Italy, close to Austria. This finding raised the question as to whether an autochthonous cycle exists in Italy or whether the infected foxes originated from the neighbouring regions of Austria. Studies have shown that multi-locus microsatellite analysis can identify genomic regions carrying mutations that result in a local adaptation. We used a tandem repeated multi-locus microsatellite (EmsB) to evaluate the genetic differences amongst adult worms of E. multilocularis collected in Italy, worms from neighbouring Austria and from other European and extra-European countries. Fluorescent PCR was performed on a panel of E. multilocularis samples to assess intra-specific polymorphism. The analysis revealed four closed genotypes for Italian samples of E. multilocularis which were unique compared with the other 25 genotypes from Europe and the five genotypes from Alaska. An analysis in the Alpine watershed, comparing Italian adult worms with those from neighbouring areas in Austria, showed a unique cluster for Italian samples. This result supports the hypothesis of the presence of an autochthonous cycle of E. multilocularis in Italy. EmsB can be useful for 'tracking' the source of infection of this zoonotic parasite and developing appropriate measures for preventing or reducing the risk of human alveolar echinococcosis.

Source attribution of human Campylobacter isolates by MLST and fla-typing and association of genotypes with quinolone resistance

Campylobacteriosis is the most frequent zoonosis in developed countries and various domestic animals can function as reservoir for the main pathogens Campylobacter jejuni and Campylobacter coli. In the present study we compared population structures of 730 C. jejuni and C. coli from human cases, 610 chicken, 159 dog, 360 pig and 23 cattle isolates collected between 2001 and 2012 in Switzerland. All isolates had been typed with multi locus sequence typing (MLST) and flaB-typing and their genotypic resistance to quinolones was determined. We used complementary approaches by testing for differences between isolates from different hosts with the proportion similarity as well as the fixation index and by attributing the source of the human isolates with Bayesian assignment using the software STRUCTURE. Analyses were done with MLST and flaB data in parallel and both typing methods were tested for associations of genotypes with quinolone resistance. Results obtained with MLST and flaB data corresponded remarkably well, both indicating chickens as the main source for human infection for both Campylobacter species. Based on MLST, 70.9% of the human cases were attributed to chickens, 19.3% to cattle, 8.6% to dogs and 1.2% to pigs. Furthermore we found a host independent association between sequence type (ST) and quinolone resistance. The most notable were ST-45, all isolates of which were susceptible, while for ST-464 all were resistant.

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

Dose Reduction in Standard Head CT: First Results from a New Scanner Using Iterative Reconstruction and a New Detector Type in Comparison with Two Previous Generations of Multi-slice CT

PURPOSE Computed tomography (CT) accounts for more than half of the total radiation exposure from medical procedures, which makes dose reduction in CT an effective means of reducing radiation exposure. We analysed the dose reduction that can be achieved with a new CT scanner [Somatom Edge (E)] that incorporates new developments in hardware (detector) and software (iterative reconstruction). METHODS We compared weighted volume CT dose index (CTDIvol) and dose length product (DLP) values of 25 consecutive patients studied with non-enhanced standard brain CT with the new scanner and with two previous models each, a 64-slice 64-row multi-detector CT (MDCT) scanner with 64 rows (S64) and a 16-slice 16-row MDCT scanner with 16 rows (S16). We analysed signal-to-noise and contrast-to-noise ratios in images from the three scanners and performed a quality rating by three neuroradiologists to analyse whether dose reduction techniques still yield sufficient diagnostic quality. RESULTS CTDIVol of scanner E was 41.5 and 36.4 % less than the values of scanners S16 and S64, respectively; the DLP values were 40 and 38.3 % less. All differences were statistically significant (p < 0.0001). Signal-to-noise and contrast-to-noise ratios were best in S64; these differences also reached statistical significance. Image analysis, however, showed "non-inferiority" of scanner E regarding image quality. CONCLUSIONS The first experience with the new scanner shows that new dose reduction techniques allow for up to 40 % dose reduction while still maintaining image quality at a diagnostically usable level.

A multi-tracer study of groundwater origin and transit-time in the aquifers of the Venice region (Italy)

Located in the northeastern region of Italy, the Venetian Plain (VP) is a sedimentary basin containing an extensively exploited groundwater system. The northern part is characterised by a large undifferentiated phreatic aquifer constituted by coarse grain alluvial deposits and recharged by local rainfalls and discharges from the rivers Brenta and Piave. The southern plain is characterised by a series of aquitards and sandy aquifers forming a well-defined artesian multi-aquifer system. In order to determine origins, transit times and mixing proportions of different components in groundwater (GW), a multi tracer study (H, He/He, C, CFC, SF, Kr, Ar, Sr/Sr, O, H, cations, and anions) has been carried out in VP between the rivers Brenta and Piave. The geochemical pattern of GW allows a distinction of the different water origins in the system, in particular based on View the MathML source HCO3-,SO42-,Ca/Mg,NO3-, O, H. A radiogenic Sr signature clearly marks GW originated from the Brenta and Tertiary catchments. End-member analysis and geochemical modelling highlight the existence of a mixing process involving waters recharged from the Brenta and Piave rivers, from the phreatic aquifer and from another GW reservoirs characterised by very low mineralization. Noble gas excesses in respect to atmospheric equilibrium occur in all samples, particularly in the deeper aquifers of the Piave river, but also in phreatic water of the undifferentiated aquifers. He–H ages in the phreatic aquifer and in the shallower level of the multi-aquifer system indicate recharge times in the years 1970–2008. The progression of H–He ages with the distance from the recharge areas together with initial tritium concentration (H + Hetrit) imply an infiltration rate of about 1 km/y and the absence of older components in these GW. SF and Kr data corroborate these conclusions. H − He ages in the deeper artesian aquifers suggest a dilution process with older, tritium free waters. C Fontes–Garnier model ages of the old GW components range from 1 to 12 ka, yielding an apparent GW velocity of about 1–10 m/y. Increase of radiogenic He follows the progression of C ages. Ar, radiogenic He and C tracers yield model-dependent age-ranges in overall good agreement once diffusion of C from aquitards, GW dispersion, lithogenic Ar production, and He production-rate heterogeneities are taken into account. The rate of radiogenic He increase with time, deduced by comparison with C model ages, is however very low compared to other studies. Comparison with C and C data obtained 40 years ago on the same aquifer system shows that exploitation of GW caused a significant loss of the old groundwater reservoir during this time.

Pangea: A Workbench for Statically Analyzing Multi-Language Software Corpora

Software corpora facilitate reproducibility of analyses, however, static analysis for an entire corpus still requires considerable effort, often duplicated unnecessarily by multiple users. Moreover, most corpora are designed for single languages increasing the effort for cross-language analysis. To address these aspects we propose Pangea, an infrastructure allowing fast development of static analyses on multi-language corpora. Pangea uses language-independent meta-models stored as object model snapshots that can be directly loaded into memory and queried without any parsing overhead. To reduce the effort of performing static analyses, Pangea provides out-of-the box support for: creating and refining analyses in a dedicated environment, deploying an analysis on an entire corpus, using a runner that supports parallel execution, and exporting results in various formats. In this tool demonstration we introduce Pangea and provide several usage scenarios that illustrate how it reduces the cost of analysis.

Automatic estimation of noise parameters in Fourier-domain optical coherence tomography cross sectional images using statistical information

We present an application and sample independent method for the automatic discrimination of noise and signal in optical coherence tomography Bscans. The proposed algorithm models the observed noise probabilistically and allows for a dynamic determination of image noise parameters and the choice of appropriate image rendering parameters. This overcomes the observer variability and the need for a priori information about the content of sample images, both of which are challenging to estimate systematically with current systems. As such, our approach has the advantage of automatically determining crucial parameters for evaluating rendered image quality in a systematic and task independent way. We tested our algorithm on data from four different biological and nonbiological samples (index finger, lemon slices, sticky tape, and detector cards) acquired with three different experimental spectral domain optical coherence tomography (OCT) measurement systems including a swept source OCT. The results are compared to parameters determined manually by four experienced OCT users. Overall, our algorithm works reliably regardless of which system and sample are used and estimates noise parameters in all cases within the confidence interval of those found by observers.

Towards Information-Centric Wireless Multi-hop Communication

Information-centric networking (ICN) addresses drawbacks of the Internet protocol, namely scalability and security. ICN is a promising approach for wireless communication because it enables seamless mobile communication, where intermediate or source nodes may change, as well as quick recovery from collisions. In this work, we study wireless multi-hop communication in Content-Centric Networking (CCN), which is a popular ICN architecture. We propose to use two broadcast faces that can be used in alternating order along the path to support multi-hop communication between any nodes in the network. By slightly modifying CCN, we can reduce the number of duplicate Interests by 93.4 % and the number of collisions by 61.4 %. Furthermore, we describe and evaluate different strategies for prefix registration based on overhearing. Strategies that configure prefixes only on one of the two faces can result in at least 27.3 % faster data transmissions.

Automatic multi-resolution shape modeling of multi-organ structures.

Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases. In this paper, we introduce a new GEneralized Multi-resolution PDM (GEM-PDM) in the context of multi-organ analysis able to efficiently characterize the different inter-object relations, as well as the particular locality of each object separately. Importantly, unlike previous approaches, the configuration of the algorithm is automated thanks to a new agglomerative landmark clustering method proposed here, which equally allows us to identify smaller anatomically significant regions within organs. The significant advantage of the GEM-PDM method over two previous approaches (PDM and hierarchical PDM) in terms of shape modeling accuracy and robustness to noise, has been successfully verified for two different databases of sets of multiple organs: six subcortical brain structures, and seven abdominal organs. Finally, we propose the integration of the new shape modeling framework into an active shape-model-based segmentation algorithm. The resulting algorithm, named GEMA, provides a better overall performance than the two classical approaches tested, ASM, and hierarchical ASM, when applied to the segmentation of 3D brain MRI.