Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences Thu, 28 May 2015 12:36:32 +0000 Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method. Yun Xue, Zhengling Liao, Meihang Li, Jie Luo, Qiuhua Kuang, Xiaohui Hu, and Tiechen Li Copyright © 2015 Yun Xue et al. All rights reserved. Statistical and Computational Methods for Genetic Diseases: An Overview Thu, 28 May 2015 11:29:41 +0000 The identification of causes of genetic diseases has been carried out by several approaches with increasing complexity. Innovation of genetic methodologies leads to the production of large amounts of data that needs the support of statistical and computational methods to be correctly processed. The aim of the paper is to provide an overview of statistical and computational methods paying attention to methods for the sequence analysis and complex diseases. Francesco Camastra, Maria Donata Di Taranto, and Antonino Staiano Copyright © 2015 Francesco Camastra et al. All rights reserved. Optimization and Corroboration of the Regulatory Pathway of p42.3 Protein in the Pathogenesis of Gastric Carcinoma Thu, 28 May 2015 11:01:21 +0000 Aims. To optimize and verify the regulatory pathway of p42.3 in the pathogenesis of gastric carcinoma (GC) by intelligent algorithm. Methods. Bioinformatics methods were used to analyze the features of structural domain in p42.3 protein. Proteins with the same domains and similar functions to p42.3 were screened out for reference. The possible regulatory pathway of p42.3 was established by integrating the acting pathways of these proteins. Then, the similarity between the reference proteins and p42.3 protein was figured out by multiparameter weighted summation method. The calculation result was taken as the prior probability of the initial node in Bayesian network. Besides, the probability of occurrence in different pathways was calculated by conditional probability formula, and the one with the maximum probability was regarded as the most possible pathway of p42.3. Finally, molecular biological experiments were conducted to prove it. Results. In Bayesian network of p42.3, probability of the acting pathway “S100A11→RAGE→P38→MAPK→Microtubule-associated protein→Spindle protein→Centromere protein→Cell proliferation” was the biggest, and it was also validated by biological experiments. Conclusions. The possibly important role of p42.3 in the occurrence of gastric carcinoma was verified by theoretical analysis and preliminary test, helping in studying the relationship between p42.3 and gastric carcinoma. Yibin Hao, Tianli Fan, and Kejun Nan Copyright © 2015 Yibin Hao et al. All rights reserved. Unified Modeling of Familial Mediterranean Fever and Cryopyrin Associated Periodic Syndromes Thu, 28 May 2015 09:27:45 +0000 Familial mediterranean fever (FMF) and Cryopyrin associated periodic syndromes (CAPS) are two prototypical hereditary autoinflammatory diseases, characterized by recurrent episodes of fever and inflammation as a result of mutations in MEFV and NLRP3 genes encoding Pyrin and Cryopyrin proteins, respectively. Pyrin and Cryopyrin play key roles in the multiprotein inflammasome complex assembly, which regulates activity of an enzyme, Caspase 1, and its target cytokine, IL-1β. Overproduction of IL-1β by Caspase 1 is the main cause of episodic fever and inflammatory findings in FMF and CAPS. We present a unifying dynamical model for FMF and CAPS in the form of coupled nonlinear ordinary differential equations. The model is composed of two subsystems, which capture the interactions and dynamics of the key molecular players and the insults on the immune system. One of the subsystems, which contains a coupled positive-negative feedback motif, captures the dynamics of inflammation formation and regulation. We perform a comprehensive bifurcation analysis of the model and show that it exhibits three modes, capturing the Healthy, FMF, and CAPS cases. The mutations in Pyrin and Cryopyrin are reflected in the values of three parameters in the model. We present extensive simulation results for the model that match clinical observations. Yasemin Bozkurt, Alper Demir, Burak Erman, and Ahmet Gül Copyright © 2015 Yasemin Bozkurt et al. All rights reserved. Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus Thu, 28 May 2015 09:07:40 +0000 The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations. Steffen Grunert and Dirk Labudde Copyright © 2015 Steffen Grunert and Dirk Labudde. All rights reserved. From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming Tue, 26 May 2015 11:53:45 +0000 The purpose of this work was to develop a mathematical model of the drug dissolution () from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of versus extrudate diameter () and the time variable () and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations’ parameters. Two inputs were found important for the drug dissolution: and . The extrudates length () was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of versus and resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs’ black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies. Aleksander Mendyk, Sinan Güres, Renata Jachowicz, Jakub Szlęk, Sebastian Polak, Barbara Wiśniowska, and Peter Kleinebudde Copyright © 2015 Aleksander Mendyk et al. All rights reserved. Inside of the Linear Relation between Dependent and Independent Variables Mon, 25 May 2015 11:53:36 +0000 Simple and multiple linear regression analyses are statistical methods used to investigate the link between activity/property of active compounds and the structural chemical features. One assumption of the linear regression is that the errors follow a normal distribution. This paper introduced a new approach to solving the simple linear regression in which no assumptions about the distribution of the errors are made. The proposed approach maximizes the probability of observing the event according to the random error. The use of the proposed approach is illustrated in ten classes of compounds with different activities or properties. The proposed method proved reliable and was showed to fit properly the observed data compared to the convenient approach of normal distribution of the errors. Lorentz Jäntschi, Lavinia L. Pruteanu, Alina C. Cozma, and Sorana D. Bolboacă Copyright © 2015 Lorentz Jäntschi et al. All rights reserved. Inducing Herd Immunity against Seasonal Influenza in Long-Term Care Facilities through Employee Vaccination Coverage: A Transmission Dynamics Model Mon, 25 May 2015 09:42:52 +0000 Introduction. Vaccinating healthcare workers (HCWs) in long-term care facilities (LTCFs) may effectively induce herd immunity and protect residents against influenza-related morbidity and mortality. We used influenza surveillance data from all LTCFs in New Mexico to validate a transmission dynamics model developed to investigate herd immunity induction. Material and Methods. We adjusted a previously published transmission dynamics model and used surveillance data from an active system among 76 LTCFs in New Mexico during 2006-2007 for model validation. We used a deterministic compartmental model with a stochastic component for transmission between residents and HCWs in each facility in order to simulate the random variation expected in such populations. Results. When outbreaks were defined as a dichotomous variable, our model predicted that herd immunity could be induced. When defined as an attack rate, the model demonstrated a curvilinear trend, but insufficiently strong to induce herd immunity. The model was sensitive to changes in the contact parameter β but was robust to changes in the visitor contact probability. Conclusions. These results further elucidate previous studies’ findings that herd immunity may not be induced by vaccinating HCWs in LTCFs; however, increased influenza vaccination coverage among HCWs reduces the probability of influenza infection among residents. Aaron M. Wendelboe, Carl Grafe, Micah McCumber, and Michael P. Anderson Copyright © 2015 Aaron M. Wendelboe et al. All rights reserved. Vaccination Control in a Stochastic SVIR Epidemic Model Sun, 24 May 2015 14:34:47 +0000 For a stochastic differential equation SVIR epidemic model with vaccination, we prove almost sure exponential stability of the disease-free equilibrium for , where denotes the basic reproduction number of the underlying deterministic model. We study an optimal control problem for the stochastic model as well as for the underlying deterministic model. In order to solve the stochastic problem numerically, we use an approximation based on the solution of the deterministic model. Peter J. Witbooi, Grant E. Muller, and Garth J. Van Schalkwyk Copyright © 2015 Peter J. Witbooi et al. All rights reserved. Temporal Unmixing of Dynamic Fluorescent Images by Blind Source Separation Method with a Convex Framework Sun, 24 May 2015 13:59:37 +0000 By recording a time series of tomographic images, dynamic fluorescence molecular tomography (FMT) allows exploring perfusion, biodistribution, and pharmacokinetics of labeled substances in vivo. Usually, dynamic tomographic images are first reconstructed frame by frame, and then unmixing based on principle component analysis (PCA) or independent component analysis (ICA) is performed to detect and visualize functional structures with different kinetic patterns. PCA and ICA assume sources are statistically uncorrelated or independent and don’t perform well when correlated sources are present. In this paper, we deduce the relationship between the measured imaging data and the kinetic patterns and present a temporal unmixing approach, which is based on nonnegative blind source separation (BSS) method with a convex analysis framework to separate the measured data. The presented method requires no assumption on source independence or zero correlations. Several numerical simulations and phantom experiments are conducted to investigate the performance of the proposed temporal unmixing method. The results indicate that it is feasible to unmix the measured data before the tomographic reconstruction and the BSS based method provides better unmixing quality compared with PCA and ICA. Duofang Chen, Jimin Liang, and Kui Guo Copyright © 2015 Duofang Chen et al. All rights reserved. Adaptively Tuned Iterative Low Dose CT Image Denoising Sun, 24 May 2015 10:54:34 +0000 Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction. SayedMasoud Hashemi, Narinder S. Paul, Soosan Beheshti, and Richard S. C. Cobbold Copyright © 2015 SayedMasoud Hashemi et al. All rights reserved. Biomedical Signal and Image Processing for Clinical Decision Support Systems 2014 Thu, 21 May 2015 08:23:51 +0000 Kayvan Najarian, Kevin R. Ward, and Shahram Shirani Copyright © 2015 Kayvan Najarian et al. All rights reserved. Mathematical Methods and Applications in Medical Imaging 2014 Wed, 20 May 2015 13:48:03 +0000 Liang Li, Tianye Niu, and Yi Gao Copyright © 2015 Liang Li et al. All rights reserved. RONI Based Secured and Authenticated Indexing of Lung CT Images Wed, 20 May 2015 09:22:58 +0000 Medical images need to be transmitted with the patient’s information without altering the image data. The present paper discusses secured indexing of lung CT image (SILI) which is a secured way of indexing the lung CT images with the patient information. Authentication is provided using the sender’s logo information and the secret key is used for embedding the watermark into the host image. Watermark is embedded into the region of Noninterest (RONI) of the lung CT image. RONI is identified by segmenting the lung tissue from the CT scan image. The experimental results show that the proposed approach is robust against unauthorized access, noise, blurring, and intensity based attacks. I. Jasmine Selvakumari Jeya and J. Suganthi Copyright © 2015 I. Jasmine Selvakumari Jeya and J. Suganthi. All rights reserved. Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space Tue, 19 May 2015 13:37:47 +0000 There has been rising interest in the discovery of novel drug indications because of high costs in introducing new drugs. Many computational techniques have been proposed to detect potential drug-disease associations based on the creation of explicit profiles of drugs and diseases, while seldom research takes advantage of the immense accumulation of interaction data. In this work, we propose a matrix factorization model based on known drug-disease associations to predict novel drug indications. In addition, genomic space is also integrated into our framework. The introduction of genomic space, which includes drug-gene interactions, disease-gene interactions, and gene-gene interactions, is aimed at providing molecular biological information for prediction of drug-disease associations. The rationality lies in our belief that association between drug and disease has its evidence in the interactome network of genes. Experiments show that the integration of genomic space is indeed effective. Drugs, diseases, and genes are described with feature vectors of the same dimension, which are retrieved from the interaction data. Then a matrix factorization model is set up to quantify the association between drugs and diseases. Finally, we use the matrix factorization model to predict novel indications for drugs. Wen Dai, Xi Liu, Yibo Gao, Lin Chen, Jianglong Song, Di Chen, Kuo Gao, Yongshi Jiang, Yiping Yang, Jianxin Chen, and Peng Lu Copyright © 2015 Wen Dai et al. All rights reserved. Performance Enhancement of Pharmacokinetic Diffuse Fluorescence Tomography by Use of Adaptive Extended Kalman Filtering Tue, 19 May 2015 11:43:02 +0000 Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems. Xin Wang, Linhui Wu, Xi Yi, Yanqi Zhang, Limin Zhang, Huijuan Zhao, and Feng Gao Copyright © 2015 Xin Wang et al. All rights reserved. Modeling Neurovascular Coupling from Clustered Parameter Sets for Multimodal EEG-NIRS Tue, 19 May 2015 11:07:56 +0000 Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters () for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data. M. Tanveer Talukdar, H. Robert Frost, and Solomon G. Diamond Copyright © 2015 M. Tanveer Talukdar et al. All rights reserved. Noninvasive Quantitative Evaluation of the Dentin Layer during Dental Procedures Using Optical Coherence Tomography Tue, 19 May 2015 10:11:11 +0000 A routine cavity preparation of a tooth may lead to opening the pulp chamber. The present study evaluates quantitatively, in real time, for the first time to the best of our knowledge, the drilled cavities during dental procedures. An established noninvasive imaging technique, Optical Coherence Tomography (OCT), is used. The main scope is to prevent accidental openings of the dental pulp chamber. Six teeth with dental cavities have been used in this ex vivo study. The real time assessment of the distances between the bottom of the drilled cavities and the top of the pulp chamber was performed using an own assembled OCT system. The evaluation of the remaining dentin thickness (RDT) allowed for the positioning of the drilling tools in the cavities in relation to the pulp horns. Estimations of the safe and of the critical RDT were made; for the latter, the opening of the pulp chamber becomes unavoidable. Also, by following the fractures that can occur when the extent of the decay is too large, the dentist can decide upon the right therapy to follow, endodontic or conventional filling. The study demonstrates the usefulness of OCT imaging in guiding such evaluations during dental procedures. Cosmin Sinescu, Meda Lavinia Negrutiu, Adrian Bradu, Virgil-Florin Duma, and Adrian Gh. Podoleanu Copyright © 2015 Cosmin Sinescu et al. All rights reserved. Adhesion Pulmonary Nodules Detection Based on Dot-Filter and Extracting Centerline Algorithm Tue, 19 May 2015 09:16:52 +0000 A suspected pulmonary nodule detection method was proposed based on dot-filter and extracting centerline algorithm. In this paper, we focus on the distinguishing adhesion pulmonary nodules attached to vessels in two-dimensional (2D) lung computed tomography (CT) images. Firstly, the dot-filter based on Hessian matrix was constructed to enhance the circular area of the pulmonary CT images, which enhanced the circular suspected pulmonary nodule and suppresses the line-like areas. Secondly, to detect the nondistinguishable attached pulmonary nodules by the dot-filter, an algorithm based on extracting centerline was developed to enhance the circle area formed by the end or head of the vessels including the intersection of the lines. 20 sets of CT images were used in the experiments. In addition, 20 true/false nodules extracted were used to test the function of classifier. The experimental results show that the method based on dot-filter and extracting centerline algorithm can detect the attached pulmonary nodules accurately, which is a basis for further studies on the pulmonary nodule detection and diagnose. Liwei Liu, Xin Wang, Yang Li, Liping Wang, and Jianghui Dong Copyright © 2015 Liwei Liu et al. All rights reserved. Breast Cancer Detection with Reduced Feature Set Tue, 19 May 2015 08:48:33 +0000 This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%–40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden’s index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity. Ahmet Mert, Niyazi Kılıç, Erdem Bilgili, and Aydin Akan Copyright © 2015 Ahmet Mert et al. All rights reserved. A Reconstruction Method of Blood Flow Velocity in Left Ventricle Using Color Flow Ultrasound Tue, 19 May 2015 07:38:23 +0000 Vortex flow imaging is a relatively new medical imaging method for the dynamic visualization of intracardiac blood flow, a potentially useful index of cardiac dysfunction. A reconstruction method is proposed here to quantify the distribution of blood flow velocity fields inside the left ventricle from color flow images compiled from ultrasound measurements. In this paper, a 2D incompressible Navier-Stokes equation with a mass source term is proposed to utilize the measurable color flow ultrasound data in a plane along with the moving boundary condition. The proposed model reflects out-of-plane blood flows on the imaging plane through the mass source term. The boundary conditions to solve the system of equations are derived from the dimensions of the ventricle extracted from 2D echocardiography data. The performance of the proposed method is evaluated numerically using synthetic flow data acquired from simulating left ventricle flows. The numerical simulations show the feasibility and potential usefulness of the proposed method of reconstructing the intracardiac flow fields. Of particular note is the finding that the mass source term in the proposed model improves the reconstruction performance. Jaeseong Jang, Chi Young Ahn, Kiwan Jeon, Jung Heo, DongHak Lee, Chulmin Joo, Jung-il Choi, and Jin Keun Seo Copyright © 2015 Jaeseong Jang et al. All rights reserved. Automatically Identifying Fusion Events between GLUT4 Storage Vesicles and the Plasma Membrane in TIRF Microscopy Image Sequences Tue, 19 May 2015 06:56:36 +0000 Quantitative analysis of the dynamic behavior about membrane-bound secretory vesicles has proven to be important in biological research. This paper proposes a novel approach to automatically identify the elusive fusion events between VAMP2-pHluorin labeled GLUT4 storage vesicles (GSVs) and the plasma membrane. The differentiation is implemented to detect the initiation of fusion events by modified forward subtraction of consecutive frames in the TIRFM image sequence. Spatially connected pixels in difference images brighter than a specified adaptive threshold are grouped into a distinct fusion spot. The vesicles are located at the intensity-weighted centroid of their fusion spots. To reveal the true in vivo nature of a fusion event, 2D Gaussian fitting for the fusion spot is used to derive the intensity-weighted centroid and the spot size during the fusion process. The fusion event and its termination can be determined according to the change of spot size. The method is evaluated on real experiment data with ground truth annotated by expert cell biologists. The evaluation results show that it can achieve relatively high accuracy comparing favorably to the manual analysis, yet at a small fraction of time. Jian Wu, Yingke Xu, Zhouyan Feng, and Xiaoxiang Zheng Copyright © 2015 Jian Wu et al. All rights reserved. An Adaptive Thresholding Method for BTV Estimation Incorporating PET Reconstruction Parameters: A Multicenter Study of the Robustness and the Reliability Tue, 19 May 2015 06:20:58 +0000 Objective. The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. Method. In a multicenter study, a phantom with spheres of different diameters (6.5–57.4 mm) was filled with 18F-FDG at different target-to-background ratios (TBR: 2.5–70) and scanned for different acquisition periods (2–5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. Results. The goodness of the model fit was high (R2: 0.74–0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. Conclusions. Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration. M. Brambilla, R. Matheoud, C. Basile, C. Bracco, I. Castiglioni, C. Cavedon, M. Cremonesi, S. Morzenti, F. Fioroni, M. Giri, F. Botta, F. Gallivanone, E. Grassi, M. Pacilio, E. De Ponti, M. Stasi, S. Pasetto, S. Valzano, and D. Zanni Copyright © 2015 M. Brambilla et al. All rights reserved. Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry Tue, 19 May 2015 05:46:06 +0000 X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm. Xiaolei Jiang, Li Zhang, Ran Zhang, Hongxia Yin, and Zhenchang Wang Copyright © 2015 Xiaolei Jiang et al. All rights reserved. Few-View Prereconstruction Guided Tube Current Modulation Strategy Based on the Signal-to-Noise Ratio of the Sinogram Mon, 18 May 2015 14:23:19 +0000 The radiation dose reduction without sacrificing the image quality as an important issue has raised the attention of CT manufacturers and different automatic exposure control (AEC) strategies have been adopted in their products. In this paper, we focus on the strategy of tube current modulation. It is deduced based on the signal-to-noise (SNR) of the sinogram. The main idea behind the proposed modulation strategy is to keep the SNR of the sinogram proximately invariable using the few-view reconstruction as a good reference because it directly affects the noise level of the reconstructions. The numerical experiment results demonstrate that, compared with constant tube current, the noise distribution is more uniform and the SNR and CNR of the reconstruction are better when the proposed strategy is applied. Furthermore it has the potential to distinguish the low-contrast target and to reduce the radiation dose. Ming Chang, Yongshun Xiao, and Zhiqiang Chen Copyright © 2015 Ming Chang et al. All rights reserved. CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization Mon, 18 May 2015 14:17:13 +0000 Radiation dose reduction without losing CT image quality has been an increasing concern. Reducing the number of X-ray projections to reconstruct CT images, which is also called sparse-projection reconstruction, can potentially avoid excessive dose delivered to patients in CT examination. To overcome the disadvantages of total variation (TV) minimization method, in this work we introduce a novel adaptive TpV regularization into sparse-projection image reconstruction and use FISTA technique to accelerate iterative convergence. The numerical experiments demonstrate that the proposed method suppresses noise and artifacts more efficiently, and preserves structure information better than other existing reconstruction methods. Hongliang Qi, Zijia Chen, and Linghong Zhou Copyright © 2015 Hongliang Qi et al. All rights reserved. Adaptive Autoregressive Model for Reduction of Noise in SPECT Mon, 18 May 2015 14:12:03 +0000 This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-AR method). The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method). A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR%) and the full width at half maximum (FWHM) of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects. Reijo Takalo, Heli Hytti, Heimo Ihalainen, and Antti Sohlberg Copyright © 2015 Reijo Takalo et al. All rights reserved. A Novel Application of Multiscale Entropy in Electroencephalography to Predict the Efficacy of Acetylcholinesterase Inhibitor in Alzheimer’s Disease Mon, 18 May 2015 13:57:29 +0000 Alzheimer’s disease (AD) is the most common form of dementia. According to one hypothesis, AD is caused by the reduced synthesis of the neurotransmitter acetylcholine. Therefore, acetylcholinesterase (AChE) inhibitors are considered to be an effective therapy. For clinicians, however, AChE inhibitors are not a predictable treatment for individual patients. We aimed to disclose the difference by biosignal processing. In this study, we used multiscale entropy (MSE) analysis, which can disclose the embedded information in different time scales, in electroencephalography (EEG), in an attempt to predict the efficacy of AChE inhibitors. Seventeen newly diagnosed AD patients were enrolled, with an initial minimental state examination (MMSE) score of . After 12 months of AChE inhibitor therapy, 7 patients were responsive and 10 patients were nonresponsive. The major difference between these two groups is Slope 2 (MSE6 to 20). The area below the receiver operating characteristic (ROC) curve of Slope 2 is 0.871 (95% CI = 0.69–1). The sensitivity is 85.7% and the specificity is 60%, whereas the cut-off value of Slope 2 is −0.024. Therefore, MSE analysis of EEG signals, especially Slope 2, provides a potential tool for predicting the efficacy of AChE inhibitors prior to therapy. Ping-Huang Tsai, Shih-Chieh Chang, Fang-Chun Liu, Jenho Tsao, Yung-Hung Wang, and Men-Tzung Lo Copyright © 2015 Ping-Huang Tsai et al. All rights reserved. An SEIV Epidemic Model for Childhood Diseases with Partial Permanent Immunity Mon, 18 May 2015 13:56:14 +0000 An SEIV epidemic model for childhood disease with partial permanent immunity is studied. The basic reproduction number has been worked out. The local and global asymptotical stability analysis of the equilibria are performed, respectively. Furthermore, if we take the treated rate as the bifurcation parameter, periodic orbits will bifurcate from endemic equilibrium when passes through a critical value. Finally, some numerical simulations are given to support our analytic results. Mei Bai and Lishun Ren Copyright © 2015 Mei Bai and Lishun Ren. All rights reserved. High Order Statistics and Time-Frequency Domain to Classify Heart Sounds for Subjects under Cardiac Stress Test Mon, 18 May 2015 13:28:07 +0000 This paper considers the problem of classification of the first and the second heart sounds (S1 and S2) under cardiac stress test. The main objective is to classify these sounds without electrocardiogram (ECG) reference and without taking into consideration the systolic and the diastolic time intervals criterion which can become problematic and useless in several real life settings as severe tachycardia and tachyarrhythmia or in the case of subjects being under cardiac stress activity. First, the heart sounds are segmented by using a modified time-frequency based envelope. Then, to distinguish between the first and the second heart sounds, new features, named , , and , based on high order statistics and energy concentration measures of the Stockwell transform (S-transform) are proposed in this study. A study of the variation of the high frequency content of S1 and S2 over the HR (heart rate) is also discussed. The proposed features are validated on a database that contains 2636 S1 and S2 sounds corresponding to 62 heart signals and 8 subjects under cardiac stress test collected from healthy subjects. Results and comparisons with existing methods in the literature show a large superiority for our proposed features. Ali Moukadem, Samuel Schmidt, and Alain Dieterlen Copyright © 2015 Ali Moukadem et al. All rights reserved.