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magnetic-resonance-image
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Total 175 records
Blind Source Separation Analysis of brain fMRI for Activation Detection
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadeddin (Supervisor) ; Babaiezadeh, Massoud (Co-Advisor)
Abstract
Functional Magnetic Resonance Imaging (fMRI) is one of the imaging techniques that are used to study human brain function and neurological disease diagnosis. Popular techniques in fMRI utilize the blood oxygenation level dependent (BOLD) contrast, which is based on the differing magnetic properties of oxygenated (diamagnetic) and deoxygenated (paramagnetic) blood. In order to analyze fMRI data, hypothesis-driven or data-driven methods can be used. Among data-driven techniques, Independent Component Analysis (ICA) provides a powerful method for the exploratory analysis of fMRI data. In this thesis, we use ICA on fMRI data for detecting active regions in brain, without a-priori knowledge of...
Effect of Obesity on Spinal Loads during Various Activities: A Combined in Vivo-Modeling Approach
, M.Sc. Thesis Sharif University of Technology ; Arjmand, Navid (Supervisor) ; Parnianpour, Mohammad (Supervisor)
Abstract
Obesity is a worldwide growing health challenge affecting ~30% of the world's population. Increased rate of disc degeneration and herniation, low back pain and surgery has been reported in obese individuals. Although obesity-related low back diseases have multifactorial etiology, presumably greater mechanical loads on the spine of heavier individuals during their daily activities may be considered as a risk factor. Likely larger trunk muscle sizes, disc sizes and thus passive stiffness in heavier individuals may however partly or fully offset the effect of their additional body weight on the spinal loads. In absence of in vivo approaches, the present study aims to construct subject-specific...
Joint Analysis of fMRI Multi-subject Data to Extract Common Spatial and Temporal Sources
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
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 thesis, this source model is referred to as joint/partiallyjoint/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) factorization....
Temporal Analysis of Functional Brain Connectivity Using EEG Signals
, M.Sc. Thesis Sharif University of Technology ; Mohammadzadeh, Narges Hoda (Supervisor)
Abstract
Human has different emotions such as happiness, sadness, anger, etc. Recognizing these emotions plays an important role in human-machine interface. Emotion recognition can be divided into approaches, physiological and non-physiological signals. Non-physiological signals include facial expressions, body gesture, and voice, and physiological signals include electroencephalograph (EEG), electrocardiograph (ECG), and functional magnetic resonance imaging (fMRI). EEG signal has been absorbed a lot of attention in emotion recognition because recording of EEG signal is easy and it is non-invasive. Analysis of connectivity and interaction between different areas of the brain can provide useful...
Designing EEG-based Deep Neural Network for Analysis of Functional and Effective Brain Connectivity
, M.Sc. Thesis Sharif University of Technology ; Mohammadzadeh, Hoda (Supervisor) ; Amini, Arash (Supervisor)
Abstract
Brain states analysis during consciousness is emerging research in brain-computer interface(BCI). Emotion recognition can be applied to learn brain states and stages of neural activities. Therefore, emotion recognition is crucial to the analysis of brain functioning. Electrical signals such as electroencephalogram (EEG), electrocardiogram (ECG) and functional magnetic resonance imaging(fMRI) are frequently used in emotion recognition researches. Convenience in recording, non-invasive nature and high temporal resolution are the factors that have made EEG popular in brain researches. EEG can be used to identify brain region activity solely or the connectivity of various regions in time in the...
Dynamic Functional Connectivity in Autism Spectrum Disorder Using Resting-State fMRI
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadeddin (Supervisor)
Abstract
Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders that cause repetitive behaviors and social and communication skills abnormalities. Autistic Disorder(AD) is one of the disorders in ASD that is being investigated in this study. There has been an increase in research about AD in recent years due to the increasing AD prevalence and the high autistic living costs. The dynamic functional connectivity between healthy and autistic groups has been analyzed by using graph theory. The brain is modeled as a dynamic graph using resting-state fMRI. The graph theory metric is calculated in the dynamic graph of each subject, and the distinction of the two groups is checked using...
Effect of Reward Training on Visual Representation of Objects in the Brain
, M.Sc. Thesis Sharif University of Technology ; Ghazizadeh, Ali (Supervisor)
Abstract
Sight is probably our most important sense. Every day, humans are exposed to many visual stimuli in their surroundings. The human brain is able to identify and prioritize important and valuable stimuli and memorize them. Identifying and remembering these valuable stimuli is vital to meeting the needs and maintaining survival. The aim of the proposed research is to find the effect of reward learning on the coding of visual objects in the human brain. Previous results have shown that long-term reward-object association make valuable objects more recognizable behaviorally. Studies have also shown that visual stimuli and the pattern of activity of primary visual cortex neurons are closely...
Graph Learning for Brain Connectivity Map Based on fMRI Data
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadeddin (Supervisor) ; Amini, Arash (Co-Supervisor)
Abstract
In recent years, due to the structural need of most medical data for graphic models such as the graphic model of patients and the loss of data correlation in previous methods, graphic methods have been designed and developed. On the other hand, with the growing presence of magnetic resonance imaging devices in various medical centers, a large amount of functional magnetic resonance images of healthy and sick people have become available to researchers. In this study, our goal is to use a new method in the field of graphic modeling so that we can extract functional connectivity graphs from functional magnetic resonance images and measure the performance of these graphs in different groups of...
Disease Classification Based on Graph Learning using fMRI Datasets
, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor)
Abstract
In the past few years, the available knowledge in graph-based processing has made significant progress, and as a result, powerful tools have been created. In this regard, graph learning with the assumption of data smoothness on the final result can be considered a successful example. Briefly, in graph learning, to describe the relationship between the problem components, a graph is learned using the available data whose nodes represent the problem components, and its edges represent how much these components are connected. The usefulness of this method lies in the possibility of using the obtained graph as the input to currently known methods of classification and achieving better results...
An investigation on the optimum conditions of synthesizing a magnetite based ferrofluid as MRI contrast agent using Taguchi method
, Article Materials Science- Poland ; Volume 31, Issue 2 , 2013 , Pages 253-258 ; 01371339 (ISSN) ; Hosseini, H. R. M ; Sharif University of Technology
2013
Abstract
In this study, some stabilized magnetite based ferrofluids were synthesized using Dextran as a stabilizing agent. In order to achieve optimum experimental conditions for synthesizing ferrofluids as MRI contrast agents, the Taguchi method was used. This approach was employed to design and minimize the number of required experiments. By using the Taguchi orthogonal (L16) array, four parameters including solution temperature and alkalinity, reaction temperature and stirring rate were selected at four predetermined levels for 16 experiments. Synthesizing processes established based on this set of experimental conditions were carried out and the obtained ferrofluids were characterized using PCS,...
MRI image reconstruction via new K-space sampling scheme based on separable transform
, Article Iranian Conference on Machine Vision and Image Processing, MVIP ; September , 2013 , Pages 127-130 ; 21666776 (ISSN) ; 9781467361842 (ISBN) ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
Reducing the time required for MRI, has taken a lot of attention since its inventions. Compressed sensing (CS) is a relatively new method used a lot to reduce the required time. Usage of ordinary compressed sensing in MRI imaging needs conversion of 2D MRI signal (image) to 1D signal by some techniques. This conversion of the signal from 2D to 1D results in heavy computational burden. In this paper, based on separable transforms, a method is proposed which enables the usage of CS in MRI directly in 2D case. By means of this method, imaging can be done faster and with less computational burden
High angular resolution diffusion image registration
, Article Iranian Conference on Machine Vision and Image Processing, MVIP ; Sept , 2013 , Pages 232-236 ; 21666776 (ISSN) ; 9781467361842 (ISBN) ; Fatemizadeh, E ; Soltanian Zadeh, H ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
Diffusion Tensor Imaging (DTI) is a common method for the investigation of brain white matter. In this method, it is assumed that diffusion of water molecules is Gaussian and so, it fails in fiber crossings where this assumption does not hold. High Angular Resolution Diffusion Imaging (HARDI) allows more accurate investigation of microstructures of the brain white matter; it can present fiber crossing in each voxel. HARDI contains complex orientation information of the fibers. Therefore, registration of these images is more complicated than the scalar images. In this paper, we propose a HARDI registration algorithm based on the feature vectors that are extracted from the Orientation...
Nonrigid registration of breast MR images using residual complexity similarity measure
, Article Iranian Conference on Machine Vision and Image Processing, MVIP, Zanjan ; Sept , 2013 , Pages 241-244 ; 21666776 (ISSN); 9781467361842 (ISBN) ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
Elimination of motion artifact in breast MR images is a significant issue in pre-processing step before utilizing images for diagnostic applications. Breast MR Images are affected by slow varying intensity distortions as a result of contrast agent enhancement. Thus a nonrigid registration algorithm considering this effect is needed. Traditional similarity measures such as sum of squared differences and cross correlation, ignore the mentioned distortion. Therefore, efficient registration is not obtained. Residual complexity is a similarity measure that considers spatially varying intensity distortions by maximizing sparseness of the residual image. In this research, the results obtained by...
Automatic B-spline image registration using histogram-based landmark extraction
, Article 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012 ; 2012 , Pages 1004-1008 ; 9781467316668 (ISBN) ; Abbasi Asl, R ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
2012
Abstract
Recognition and correction of inhomogeneous displacement caused by patient's movement has been recently discussed as an interesting topic in medical image processing. Considering consistency in general structure of the image during distortion, histogram could be employed as a fast implementation method in feature domain. Accordingly, attribute vectors could be defined for each pixel based on spatial features to find corresponding points in two images. Consequently a point-based and non-rigid transformation approach will be designed. A B-spline image registration has been applied to match those pairs with a defined smoothness factor. This algorithm is a step-by-step registration process...
Interpolation of orientation distribution functions (ODFs) in Q-ball imaging
, Article 2012 19th Iranian Conference of Biomedical Engineering, ICBME 2012 ; 2012 , Pages 213-217 ; 9781467331302 (ISBN) ; Fatemizadeh, E ; Soltanian Zadeh, H ; Sharif University of Technology
2012
Abstract
Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain but in the regions with crossing fibers, it fails. To resolve this problem, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding directions is used and for reconstruction, the Q-ball method is applied. In this method, orientation distribution function (ODF) of fibers can be calculated. Mathematical models play a crucial role in the field of ODF. For instance, in registering Q-ball images for applications like group analysis or atlas construction, one needs to interpolate...
Sparse signal processing using iterative method with adaptive thresholding (IMAT)
, Article 2012 19th International Conference on Telecommunications, ICT 2012, 23 April 2012 through 25 April 2012, Jounieh ; 2012 ; 9781467307475 (ISBN) ; Azghani, M ; Imani, P ; Pakrouh, P ; Heydari, S.J ; Golmohammadi, A ; Kazerouni, A ; Khalili, M. M ; Sharif University of Technology
IEEE
2012
Abstract
Classical sampling theorem states that by using an anti-aliased low-pass filter at the Nyquist rate, one can transmit and retrieve the filtered signal. This approach, which has been used for decades in signal processing, is not good for high quality speech, image and video signals where the actual signals are not low-pass but rather sparse. The traditional sampling theorems do not work for sparse signals. Modern approach, developed by statisticians at Stanford (Donoho and Candes), give some lower bounds for the minimum sampling rate such that a sparse signal can be retrieved with high probability. However, their approach, using a sampling matrix called compressive matrix, has certain...
Effect of different diffusion maps on registration results
, Article 2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP 2011 - Proceedings ; 2011 ; 9781457715358 (ISBN) ; Fatemizadeh, E ; Soltanian Zadeh, H ; Sharif University of Technology
Abstract
In this paper, we compare registration results obtained using different diffusion maps extracted from diffusion tensor imaging (DTI). Fractional Anisotropy (FA) and Ellipsoidal Area Ratio (EAR) are two diffusion maps (indices) that may be used for image registration. First, we use FA maps to find deformation matrix and register diffusion weighted images. Then, we use EAR maps and finally we use both of FA and EAR maps to register diffusion weighted images. The difference between FA values before deformation and after registration using the FA alone or EAR alone has a median of 0.57 and using both of them has a median of 0.29. Therefore, the results of registration using both of the FA and...
Failure tolerance of motif structure in biological networks
, Article PLoS ONE ; Volume 6, Issue 5 , May , 2011 ; 19326203 (ISSN) ; Jalili, M ; Sharif University of Technology
2011
Abstract
Complex networks serve as generic models for many biological systems that have been shown to share a number of common structural properties such as power-law degree distribution and small-worldness. Real-world networks are composed of building blocks called motifs that are indeed specific subgraphs of (usually) small number of nodes. Network motifs are important in the functionality of complex networks, and the role of some motifs such as feed-forward loop in many biological networks has been heavily studied. On the other hand, many biological networks have shown some degrees of robustness in terms of their efficiency and connectedness against failures in their components. In this paper we...
Variations in trunk muscle activities and spinal loads following posterior lumbar surgery: A combined in vivo and modeling investigation
, Article Clinical Biomechanics ; Volume 30, Issue 10 , 2015 , Pages 1036-1042 ; 02680033 (ISSN) ; Arjmand, N ; Sharif University of Technology
Abstract
Background Iatrogenic injuries to paraspinal muscles during posterior lumbar surgery cause a reduction in their contractile cross-sectional area and thus presumably their postoperative activation. This study investigates the effect of such intraoperative injuries on postoperative patterns of muscle activations and spinal loads during various activities using a combined modeling and in vivo MR imaging approach. Methods A three-dimensional, multi-joint, musculoskeletal model was used to estimate pre- and postoperative muscle forces and spinal loads under various activities in upright and flexed postures. According to our in vivo pre- and postoperative (∼ 6 months) measurements in six patients...
Toward epileptic brain region detection based on magnetic nanoparticle patterning
, Article Sensors (Switzerland) ; Volume 15, Issue 9 , September , 2015 , Pages 24409-24427 ; 14248220 (ISSN) ; Shamloo, A ; Alasty, A ; Ghafar Zadeh, E ; Sharif University of Technology
MDPI AG
2015
Abstract
Resection of the epilepsy foci is the best treatment for more than 15% of epileptic patients or 50% of patients who are refractory to all forms of medical treatment. Accurate mapping of the locations of epileptic neuronal networks can result in the complete resection of epileptic foci. Even though currently electroencephalography is the best technique for mapping the epileptic focus, it cannot define the boundary of epilepsy that accurately. Herein we put forward a new accurate brain mapping technique using superparamagnetic nanoparticles (SPMNs). The main hypothesis in this new approach is the creation of super-paramagnetic aggregates in the epileptic foci due to high electrical and...