Sharif Digital Repository

Fetal stem cells, which can be isolated from the organs of fetuses, differentiate along multiple lineages. Their advantages over their adult counterparts include better intrinsic homing and engraftment and lower immunogenicity, and they are less ethically contentious. It is noteworthy that Mesenchymal Stem Cells (MSC) can be activated and mobilized at the site of damaged tissue. Since vascular delivery suffers from a pulmonary first pass effect, direct or systemic injection of MSCs into the damaged tissue is preferred, particularly in the case of versatile tissue ischemia. Ultrasound applies acoustic energy with a frequency above human hearing (20 kHz). Ultrasound imaging or sonography... 

In this study, we are proposing a novel nonlinear classification approach to discriminate between Alzheimer's Disease (AD) and a control group using T1-weighted and T2- weighted Magnetic Resonance Images (MRI's) of brain. Since T1-weighted images and T2-weighted images have inherent physical differences, obviously each of them has its own particular medical data and hence, we extracted some specific features from each. Then the variations of the relevant eigenvalues of the extracted features were tracked to pick up the most informative ones. The final features were assigned to two parallel systems to be nonlinearly categorized. Considering the fact that AD defects the white and gray regions... 

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000... 

In this extended summary, our aim is analyzing functional magnetic resonance imaging (fMRI) data by independent component analysis (ICA) in order to find regions of brain which were activated by neural activity in human brain. We employ the minimum description length (MDL) criterion to reduce the dimension of the data and estimate the number of components, which makes ICA work more efficiently. We also use a simple oscillating index method to select automatically the components of interest. MDL and oscillating index criteria have not already been used in applying ICA for analyzing fMRI data. In order to investigate the advantage of using MDL and oscillating index, we perform some experiments... 

Tissue segmentation in magnetic resonance brain scans is the most critical task in different aspects of brain analysis. Because manual segmentation of brain magnetic resonance imaging (MRI) images is a time-consuming and labor-intensive procedure, automatic image segmentation is widely used for this purpose. As Markov Random Field (MRF) model provides a powerful tool for segmentation of images with a high level of artifacts, it has been considered as a superior method. But because of the high computational cost of MRF, it is not appropriate for online processing. This article has proposed a novel method based on a proper combination of MRF model and watershed algorithm in order to alleviate... 

In this study, clustering based method for activation detection in functional magnetic resonance imaging (fMRI) is employed. Moreover, some features are obtained by fitting two models namely FIR filter and Gamma function, to hemodynamic response function (HRF). After applying clustering methods (that require number of clusters as an input) to feature space, our simulations show that number of clusters can affect activation detection significantly. Therefore a newly proposed clustering algorithm namely evolving neural gas (ENG) that gives optimal number of clusters is exploited. In addition to ENG, the result of four clustering algorithms namely k-means, fuzzy C-means, neural gas, and clara... 

Recently, graphene oxide and its unconventional magnetism have attracted much interest due to their novel applications in spintronics, memory chips and theranostics. Owing to the excellent biocompatibility, cellular uptake, bio-conjugation possibilities, flexible chemical modification and characteristic broad-wavelength absorbance, graphene oxide and its derivatives have been utilized as contrast agents for various imaging modalities such as photoluminescence, photoacoustic or ultrasound. Despite their suitable applications in bioimaging and due to lack of magnetic moment, graphene oxide cannot confer magnetic resonance imaging contrast without incorporating the magnetic component. Such... 

Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-joint/individual multiple datasets unidimensional (JpJI-MDU), and accordingly, a source extraction method is developed. Method: We present a deflation-based algorithm utilizing higher order cumulants to analyze the JpJI-MDU source model. The algorithm maximizes a cost function which leads to an eigenvalue problem solved with thin-SVD (singular value decomposition)... 

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It presents various symptoms, hence, utilizing the information obtained from functional magnetic resonance imaging (fMRI) time-series data can be useful. Finding functional connections in typically developed control (TDC) and ADHD patients can be helpful in classification. The aim of this paper is to present a multifold method for the study of fMRI data to diagnose ADHD patients. In the proposed method, first, by applying the Stockwell transform (ST), we obtain detailed information about the time-series of the region of interests (ROIs) in the time and frequency domains. ST provides information about... 

Fuzzy connectedness and fuzzy clustering are two well-known techniques for fuzzy image segmentation. The former considers the relation of pixels in the spatial space but does not inherently utilize their feature information. On the other hand, the latter does not consider the spatial relations among pixels. In this paper, a new segmentation algorithm is proposed in which these methods are combined via a notion called membership connectedness. In this algorithm, two kinds of local spatial attractions are considered in the functional form of membership connectedness and the required seeds can be selected automatically. The performance of the proposed method is evaluated using a developed... 

Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

In the present study, first, Fe3O4nanoparticles were functionalized using glutaric acid and then composited with CQDs. Doxorubicin (DOX) drug was loaded to evaluate the performance of the nanocomposite for targeted drug delivery applications. The XRD pattern confirmed the presence of characteristic peaks of CQDs and Fe3O4. In the FTIR spectrum, the presence of carboxyl functional groups on Fe3O4/CQDs was observed; DOX (positive charge) is loaded onto Fe3O4/CQDs (negative charge) by electrostatic absorption. FESEM and AFM images showed that the particle sizes of Fe3O4and CQDs were 23-75 and 1-3 nm, respectively. The hysteresis curves showed superparamagnetic properties for Fe3O4and Fe3O4/CQDs... 

Superparamagnetic Iron Oxide Nanoparticles (SPIONs) were synthesized and coated with pseudopolyrotaxanes (PPRs) and proposed as a novel hybrid nanostructure for medical imaging and drug delivery. PPRs were prepared by addition of α-cyclodextrin rings to functionalized polyethylene glycol (PEG) chain with hydrophobic triazine end-groups. Non-covalent interactions between SPIONs and PPRs led to the assembly of SPIONs@PRs hybrid nanomaterials. Measurements of the 1H Nuclear Magnetic Resonance (NMR) relaxation times T1 and T2 allowed us to determine the NMR dispersion profiles. Comparison between our SPIONs@PRs hybrid nano-compound and the commercial SPION compound, Endorem®, showed a higher... 

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the... 

Intracranial pressure (ICP) is derived from cerebral blood pressure and cerebrospinal fluid (CSF) circulatory dynamics and can be affected in the course of many diseases. Computer analysis of the ICP time pattern plays a crucial role in the diagnosis and treatment of those diseases. This study proposes the application of Linninger et al.s [IEEE Trans. Biomed. Eng. , vol. 52, no. 4, pp. 557565, Apr. 2005] fluidsolid interaction model of CSF hydrodynamic in ventricular system based on our clinical data from a group of patients with brain parenchyma tumor. The clinical experiments include the arterial blood pressure (ABP), venous blood pressure, and ICP in the subarachnoid space (SAS). These... 

The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of...