用偏振敏感技术对光纤带光缆进行应力研究

本文介绍有关高密度光纤带光缆应力特性的实验结果，实验用两种技术进行的：偏振模色散法和偏振光时域反射法，它们之间的相关性和从理论上得出的数据表明：在这样的光缆中，双折射部分是决定性的。     

Electrical impedance spectroscopy of the human prostate

Tissue electrical impedance is a function of its architecture and has been used to differentiate normal and cancer tissues in a variety of organs including breast, cervix, skin, and bladder. This paper investigates the possibility of differentiating normal and malignant prostate tissue using bioimpedance spectra. A probe was designed to measure impedance spectra over the range of 10 kHz to 1 MHz. The probe was fully characterized using discrete loads and saline solutions of different concentrations. Impedance spectra of five ex vivo prostates were measured in the operating room immediately following radical prostatectomy. Wilcoxon signed-rank tests were used to compare the normal and malignant findings. The impedance probe had a signal-to-noise ratio (SNR) > 84 dB across the entire spectrum and measured a tissue volume of approximately 46 mm(3). At 10 kHz, prostate conductivity (or) ranged from 0.232 S/m to 0.310 S/m for tumor and from 0.238 S/m to 0.901 S/m for normal tissue. At 1 MHz the ranges were 0.301 S/m to 0.488 S/m for tumor and 0.337 S/m to 1.149 S/m for normal. Prostate permittivity (epsilonr) ranged from 6.64 x10(4) to 1.25 x 10(5) for tumor and from 9.08 x 10(4) to 4.49 x 10(5) for normal tissues at 10 kHz. And, at 1 MHz the er ranges were 9.23 x 10(2) to 1.88 x 10(3) for tumor and 1.16 x 10(3) to 2.18 x 10(3) for normal tissue. Both sigma and epsilonr of tumor tissue were found to be significantly lower than that of normal tissue (P < 0.0001). Conductivity and permittivity are both higher in normal prostate tissues than they are in malignant tissue making them suitable parameters for tissue differentiation. This is in agreement with trends observed in other tissues reported in much of the literature. Expanded studies are needed to further validate this finding and to explore the biological mechanism responsible for generating the results.     

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.     

Entropy-based measures for quantifying sleep-stage transition dynamics: relationship to sleep fragmentation and daytime sleepiness

We present two novel entropy-based measures that quantify sleep-stage transition dynamics (sleep structure) from polysomnogram derived hypnograms: Walsh spectral entropy (WSE) and Haar spectral entropy (HSE). These measures quantify patterns of temporal regularity of a categorical time series without requiring numerical encoding (scaling) of the (categorical) sleep stages. Additionally, we show that conditional entropy (CE) is well suited for quantifying predictability of the hypnogram. The relationship of those measures with traditional sleep fragmentation indices (arousal index, total sleep time, and sleep efficiency) is explored for a 394 participant sample of the Cleveland Family Study, an epidemiologic study in which standardized single-night polysomnogram data were collected. The new entropy-based sleep structure measures (WSE, HSE, and CE) are positively correlated (moderate to weak) with the traditional sleep fragmentation indices. Because the sleep structure measures developed in this paper provide direct information related to temporal patterns of sleep that is not contained in traditional sleep fragmentation measures, the correlation between these new alternative sleep structure measures and the traditional sleep fragmentation measures is less important. Our goal is not to develop alternative measures that correlate highly with traditional measures of sleep fragmentation, but rather to provide methods to quantify sleep structure by examining other (e.g., dynamic sleep-stage transition) properties of the hypnogram. Additionally, the relationship of the new entropy-based and traditional measures with daytime sleepiness as quantified by the Epworth sleepiness scale (ESS) is investigated. Multiple linear regression analysis shows that WSE has a stronger relationship with ESS than the traditional measures, even after both are adjusted for common confounders (age, race, gender, and body mass index). This further suggests that the entropy-based measures, especially WSE, are capturing additional temporal patterns of sleep not captured in the traditional sleep fragmentation measures, and have a relationship with daytime sleepiness.     

Ray contribution masks for structure adaptive sinogram filtering

The patient dose in computed tomography (CT) imaging is linked to measurement noise. Various noise-reduction techniques have been developed that adapt structure preserving filters like anisotropic diffusion or bilateral filters to CT noise properties. We introduce a structure adaptive sinogram (SAS) filter that incorporates the specific properties of the CT measurement process. It uses a point-based forward projector to generate a local structure representation called ray contribution mask (RCM). The similarities between neighboring RCMs are used in an enhanced variant of the bilateral filtering concept, where the photometric similarity is replaced with the structural similarity. We evaluate the performance in four different scenarios: The robustness against reconstruction artifacts is demonstrated by a scan of a high-resolution-phantom. Without changing the modulation transfer function (MTF) nor introducing artifacts, the SAS filter reduces the noise level by 13.6%. The image sharpness and noise reduction capabilities are visually assessed on in vivo patient scans and quantitatively evaluated on a simulated phantom. Unlike a standard bilateral filter, the SAS filter preserves edge information and high-frequency components of organ textures well. It shows a homogeneous noise reduction behavior throughout the whole frequency range. The last scenario uses a simulated edge phantom to estimate the filter MTF for various contrasts: the noise reduction for the simple edge phantom exceeds 80%. For low contrasts at 55 Hounsfield units (HU), the mid-frequency range is slightly attenuated, at higher contrasts of approximately 100 HU and above, the MTF is fully preserved.     

Automatic detection and quantification of tree-in-bud (TIB) opacities from CT scans

This study presents a novel computer-assisted detection (CAD) system for automatically detecting and precisely quantifying abnormal nodular branching opacities in chest computed tomography (CT), termed tree-in-bud (TIB) opacities by radiology literature. The developed CAD system in this study is based on 1) fast localization of candidate imaging patterns using local scale information of the images, and 2) M?bius invariant feature extraction method based on learned local shape and texture properties of TIB patterns. For fast localization of candidate imaging patterns, we use ball-scale filtering and, based on the observation of the pattern of interest, a suitable scale selection is used to retain only small size patterns. Once candidate abnormality patterns are identified, we extract proposed shape features from regions where at least one candidate pattern occupies. The comparative evaluation of the proposed method with commonly used CAD methods is presented with a dataset of 60 chest CTs (laboratory confirmed 39 viral bronchiolitis human parainfluenza CTs and 21 normal chest CTs). The quantitative results are presented as the area under the receiver operator characteristics curves and a computer score (volume affected by TIB) provided as an output of the CAD system. In addition, a visual grading scheme is applied to the patient data by three well-trained radiologists. Interobserver and observer-computer agreements are obtained by the relevant statistical methods over different lung zones. Experimental results demonstrate that the proposed CAD system can achieve high detection rates with an overall accuracy of 90.96%. Moreover, correlations of observer-observer (R(2)=0.8848, and observer-CAD agreements (R(2)=0.824, validate the feasibility of the use of the proposed CAD system in detecting and quantifying TIB patterns.     

Multichannel ECG data compression based on multiscale principal component analysis

In this paper, multiscale principal component analysis (MSPCA) is proposed for multichannel electrocardiogram (MECG) data compression. In wavelet domain, principal components analysis (PCA) of multiscale multivariate matrices of multichannel signals helps reduce dimension and remove redundant information present in signals. The selection of principal components (PCs) is based on average fractional energy contribution of eigenvalue in a data matrix. Multichannel compression is implemented using uniform quantizer and entropy coding of PCA coefficients. The compressed signal quality is evaluated quantitatively using percentage root mean square difference (PRD), and wavelet energy-based diagnostic distortion (WEDD) measures. Using dataset from CSE multilead measurement library, multichannel compression ratio of 5.98:1 is found with PRD value 2.09% and the lowest WEDD value of 4.19%. Based on, gold standard subjective quality measure, the lowest mean opinion score error value of 5.56% is found.     

Accuracy of carotid strain estimates from ultrasonic wall tracking: a study based on multiphysics simulations and in vivo data

We used a multiphysics model to assess the accuracy of carotid strain estimates derived from a 1-D ultrasonic wall tracking algorithm. The presented tool integrates fluid-structure interaction (FSI) simulations with an ultrasound simulator (Field II), which allows comparison of the ultrasound (US) images with a ground truth. Field II represents tissue as random points on which US waves reflect and whose position can be updated based on the flow field and vessel wall deformation from FSI. We simulated the RF-signal of a patient-specific carotid bifurcation, including the blood pool as well as the vessel wall and surrounding tissue. Distension estimates were obtained from a wall tracking algorithm using tracking points at various depths within the wall, and further processed to assess radial and circumferential strain. The simulated data demonstrated that circumferential strain can be estimated with reasonable accuracy (especially for the common carotid artery and at the lumen-intima and media-adventitia interface), but the technique does not allow to reliably assess intra-arterial radial strain. These findings were supported by in vivo data of 10 healthy adults, showing similar circumferential and radial strain profiles throughout the arterial wall. We concluded that these deviations are present due to the complex 3-D vessel wall deformation, the presence of specular reflections and, to a lesser extent, the spatially varying beam profile, with the error depending on the phase in the cardiac cycle and the scanning location.     

Object tracking with particle filtering in fluorescence microscopy images: application to the motion of neurofilaments in axons

Neurofilaments are long flexible cytoplasmic protein polymers that are transported rapidly but intermittently along the axonal processes of nerve cells. Current methods for studying this movement involve manual tracking of fluorescently tagged neurofilament polymers in videos acquired by time-lapse fluorescence microscopy. Here, we describe an automated tracking method that uses particle filtering to implement a recursive Bayesian estimation of the filament location in successive frames of video sequences. To increase the efficiency of this approach, we take advantage of the fact that neurofilament movement is confined within the boundaries of the axon. We use piecewise cubic spline interpolation to model the path of the axon and then we use this model to limit both the orientation and location of the neurofilament in the particle tracking algorithm. Based on these two spatial constraints, we develop a prior dynamic state model that generates significantly fewer particles than generic particle filtering, and we select an adequate observation model to produce a robust tracking method. We demonstrate the efficacy and efficiency of our method by performing tracking experiments on real time-lapse image sequences of neurofilament movement, and we show that the method performs well compared to manual tracking by an experienced user. This spatially constrained particle filtering approach should also be applicable to the movement of other axonally transported cargoes.     

Impact of additive noise on system performance of a digital X-ray imaging system

The impact of additive noise on the performance of a digital X-ray imaging system was investigated. The X-ray system is uniquely designed for small animal studies with a focal spot of 20 microm and an adjustable source-to-object distance for radiography. The noise power spectrum and the detective quantum efficiency of this system were measured. The additive noise increased rapidly when the exposure time exceeded a certain range, since the charge-coupled devices of the detector had no cooling system. The noise power spectrum for the additive noise and the noise of the entire imaging system were studied and compared at different exposure times. The detective quantum efficiency was also measured at different exposure times. It was observed that for exposure times less than 10 s, the detective quantum efficiency ((DQE)(0)) is approximately 0.26, dropping to 0.13 at 4 lp/mm and to 0.026 at 8 lp/mm. However, when the exposure exceeds a certain limit (10 s in this study), the rapidly increased additive noise caused the system to be no longer quantum noise limited, resulting in a decreased detective quantum efficiency and a degraded system performance. For example, at an exposure of 20 s, the DQE(O) is approximately 0.22, dropping to 0.11 at 3 lp/mm and to 0.022 at 8 lp/mm.