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magnetic-resonance-image
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Total 175 records
Identifying brain functional connectivity alterations during different stages of Alzheimer’s disease
, Article International Journal of Neuroscience ; Volume 132, Issue 10 , 2022 , Pages 1005-1013 ; 00207454 (ISSN) ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
Taylor and Francis Ltd
2022
Abstract
Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed. Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated...
Elastic Registration of Breast Magnetic Resonance Images
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emad (Supervisor)
Abstract
Breast cancer is considered as the most common type of cancer in women worldwide and mammography is currently utilized as the principal method for screening the breast cancer. Breast Magnetic resonance imaging (MRI) can be used as a complementary imaging technique besides mammography. MRI technique involves scanning a patient before and repeatedly after the injection of the contrast agent (DCE-BMRI). This examination often takes 7-10 minutes and any movement of the patient’s breasts due to breath, heartbeat or deliberate movement, made in this relatively long acquisition period, leads to a distortion in images called motion artifact. This problem makes the quantitative analysis of the images...
Functional Connectivity Detection in Resting-State Brain using functional Magnetic Resonance Imaging
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadeddin (Supervisor) ; Soltanianzadeh, Hamid (Supervisor)
Abstract
The functional network of the human brain is altered in many neurological and psychiatric disorders. Characterizing brain activity in terms of functionally segregated regions does not reveal anything about the communication among different brain regions and how such inter-communication could influence neural activity in each local region. The aim of this project is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the simulated, realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral...
Analysis of Functional Connectivity Among Brain Networks Using FMRI
,
M.Sc. Thesis
Sharif University of Technology
;
Vosughi Vahdat, Bijan
(Supervisor)
;
Amini, Arash
(Supervisor)
Abstract
Development of the fMRI imaging method gives the scientists the opportunity to record functional images from the brain with high spatial resolution and several researches were conducted on this field. Autistic people’s brain has functional differences with normal people. In this paper these differences have been studied. At first fMRI datasets from autistic subjects and control have been recorded and preprocessed. Then the independent components from these datasets have been extracted using group ICA method. Any independent component is an image depicting a brain network. There is a time series for each image which shows the temporal variations of each component. In the next step, the...
Functional Mapping of Regions Involved In Addiction Using Magnetic Resonance Imaging and Proposing a New Measure for Multivariate Methods
, M.Sc. Thesis Sharif University of Technology ; Vosughi Vahdat, Bijan (Supervisor) ; Ekhtiari, Hamed (Co-Advisor)
Abstract
Methamphetamine (meth) abuse and addiction (MA), with its serious medical, psychiatric and social complications, is a growing national disaster in Iran. Response control deficit during exposure to drug related cues is one of the main neurocognitive cores in MA and results in continued drug use and treatment failure. There have been many studies focused on cue exposure, but most of their paradigms required subjects to passively view the cues; this aspect of these paradigms cause a wide gap between reality and experimental studies. Developing a functional and structural neuroimaging protocol to map realistic brain circuits that are involved in craving among meth users is of importance....
Affecting on Brain Activation by Transcranial Direct Current Stimulation
, M.Sc. Thesis Sharif University of Technology ; Vosoughi Vahdat, Bijan (Supervisor) ; Oghabian, Mohammad Ali (Co-Advisor)
Abstract
Transcranial direct current stimulation (tDCS) over the different brain regions has been documented in clinical and laboratory experiments. Anodal tDCS on the dorsolateral prefrontal cortex (DLPFC) has shown promising effects in enhancing cognition. Furthermore, such stimulations have been proposed in treatment of several neurological and psychological disorders. Investigations have verified the positive effect of such stimulations on drug addicts by diminishing their drug craving after stimulation. In spite of the extended research in this field, the effect of tDCS on different brain region and brain networks has yet not been studied through computational models. In this study, we evaluated...
An implementation of a CBIR system based on SVM learning scheme
, Article Journal of Medical Engineering and Technology ; Volume 37, Issue 1 , 2013 , Pages 43-47 ; 03091902 (ISSN) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
2013
Abstract
Content-based image retrieval (CBIR) has been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture and shape or the semantic meaning of the images. A CBIR system can be used to locate medical images in large databases. This paper presents a CBIR system for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the support vector machine (SVM) learning method. This system can retrieve similar images from the database in two groups: normal and tumoural. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination...
Rotating field gradient (RFG) MR offers improved orientational sensitivity
, Article Proceedings - International Symposium on Biomedical Imaging, 16 April 2015 through 19 April 2015 ; Volume 2015-July , 2015 , Pages 955-958 ; 19457928 (ISSN) ; 9781479923748 (ISBN) ; Memic, M ; Avram, A. V ; Afzali, M ; Basser, P. J ; Westin, C. F ; Sharif University of Technology
IEEE Computer Society
2015
Abstract
Rotating field gradients (RFGs), generated by simultaneously applying sine- and cosine-modulated gradient waveforms along two perpendicular directions, provide an alternative diffusion sensitization mechanism for magnetic resonance imaging and spectroscopy. Two RFGs with a 90-degree phase shift between them are applied around the 180-degree RF pulse in a spin echo sequence to measure the diffusion orientation distribution function (dODF) directly. The technique obviates transforming the data from a space reciprocal to the displacement space. Here, we compare RFG results with those obtained by two pulsed field gradient (PFG) techniques: q-ball imaging (QBI) and its extension to constant solid...
Tracking the 3D configuration of human joint using an MR image registration technique
, Article ASME 2010 5th Frontiers in Biomedical Devices Conference and Exhibition, BIOMED 2010, 20 September 2010 through 21 September 2010 ; 2010 , Pages 93-94 ; 9780791849453 (ISBN) ; Farahmand, F ; Jafari, A ; Sharif University of Technology
Abstract
Surface registration is a necessary step and widely used in medical image-aided surgery. It's relevance to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities. In this study, the kinematic relations between two point clouds with different coordinate definitions have been generated. Using Influence Method of surface modeling for extracting point clouds functions, the transformation matrix would be resulted. The proposed method was applied for an experimental femur data points(651 points) using the MRI images. These data points were transformed in a 30 degrees flexion of knee. This...
Design and fabrication of a new multi-loop saddle coil for 1.5 T MRI
, Article Review of Scientific Instruments ; Volume 90, Issue 11 , 2019 ; 00346748 (ISSN) ; Mohammadzadeh, M ; Sharif University of Technology
American Institute of Physics Inc
2019
Abstract
Radiofrequency coils provide high-resolution magnetic resonance (MR) imaging of human tissues. A small RF coil produces MR images with a higher resolution compared to the commercial volume MR coils from mass limited samples. Signal to noise ratio (SNR) plays a key role in the optimal design of receiver radiofrequency coils. In this work, we present a three-loop saddle coil suitable for MR imaging of digits of the human body. The geometry of the introduced coil is optimized to achieve the highest SNR. The coil performance is evaluated through comparing the measured SNR maps of the optimal coil derived from MR images of a saline phantom with the corresponding measured SNR maps of a commercial...
Multiple sclerosis diagnosis based on analysis of subbands of 2-D wavelet transform applied on MR-images
, Article 2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007, Amman, 13 May 2007 through 16 May 2007 ; 2007 , Pages 717-721 ; 1424410312 (ISBN); 9781424410316 (ISBN) ; Moradzadeh, H ; Vaziri, R ; Dehestani Ardekani, R ; Fatemizadeh, E ; Sharif University of Technology
2007
Abstract
In this study, we have proposed a novel approach to investigate the features of four subbands of 2-D wavelet transform in magnetic resonance images (MRIs) for normal and abnormal brains which defected by Multiple Sclerosis (MS). Concurrently, another method extracts different kinds of features in spatial domain. Totally, 116 features have been extracted. Before applying the algorithm, we have to use a registration method because of variety in size of brain images. All extracted features have been passed over the Principal Component Analysis (PCA) and have been pushed to an Artificial Neural Network (ANN) that is a feed-forward type. According to changing in position of defected parts of...
Noise reduction from Magnetic Resonance images using nonseperable transforms
, Article Medical Imaging 2006: Image Processing, San Diego, CA, 13 February 2006 through 16 February 2006 ; Volume 6144 III , 2006 ; 16057422 (ISSN); 0819464236 (ISBN); 9780819464231 (ISBN) ; Shamsollahi, M. B ; Sharif University of Technology
2006
Abstract
Multi-scale transforms have got a lot of applications in image processing, in recent years. Wavelet transform is a powerful multiscale transform for denoising noisy signals and images, but the usual two-dimensional separable wavelets are sub-optimal. These separable wavelet transforms can successfully identify zero dimensional singularities in images, but can weakly identify one dimensional singularities such as edges, curves and lines. In this sense, non-separable transforms such as Ridgelet and Curvelet transforms are proposed by Candes and Donoho. The coefficients produced by these non-separable transforms have shown to be sparser than wavelet coefficients. This fact results in better...
An iterative approach for reconstruction of arbitrary sparsely sampled magnetic resonance images
, Article 18th IEEE Symposium on Computer-Based Medical Systems, Dublin, Ireland, 23 June 2005 through 24 June 2005 ; 2005 , Pages 27-32 ; 10637125 (ISSN) ; Soleymani, M ; Hossein Zadeh, G. A ; Sharif University of Technology
2005
Abstract
In many fast MR imaging techniques, K-space is sampled sparsely in order to gain a fast traverse of K-space. These techniques use non-Cartesian sampling trajectories like radial, zigzag, and spiral. In the reconstruction procedure, usually interpolation methods are used to obtain missing samples on a regular grid. In this paper, we propose an iterative method for image reconstruction which uses the black marginal area of the image. The proposed iterative solution offers a great enhancement in the quality of the reconstructed image in comparison with conventional algorithms like zero filling and neural network. This method is applied on MRI data and its improved performance over other methods...
MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules
, Article Journal of Medical Engineering and Technology ; Vol. 38, issue. 4 , 2014 , p. 211-219 ; Fatemizadeh, E ; Behnam, H ; Sharif University of Technology
Abstract
Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to...
Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series
, Article Biomedical Signal Processing and Control ; Volume 8, Issue 6 , 2013 , Pages 909-919 ; 17468094 (ISSN) ; Shayesteh, M. G ; Zali Vargahan, B ; Sharif University of Technology
2013
Abstract
In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper...
Modified Gadonanotubes as a promising novel MRI contrasting agent
, Article DARU, Journal of Pharmaceutical Sciences ; Volume 21, Issue 1 , 2013 ; 15608115 (ISSN) ; Atyabi, F ; Shanehsazzadeh, S ; Sobhani, Z ; Adeli, M ; Dinarvand, R ; Sharif University of Technology
2013
Abstract
Background and purpose of the study. Carbon nanotubes (CNTs) are emerging drug and imaging carrier systems which show significant versatility. One of the extraordinary characteristics of CNTs as Magnetic Resonance Imaging (MRI) contrasting agent is the extremely large proton relaxivities when loaded with gadolinium ion (Gdn 3+) clusters. Methods. In this study equated Gdn 3+ clusters were loaded in the sidewall defects of oxidized multiwalled (MW) CNTs. The amount of loaded gadolinium ion into the MWCNTs was quantified by inductively coupled plasma (ICP) method. To improve water solubility and biocompatibility of the system, the complexes were functionalized using diamine-terminated...
A content-based approach to medical images retrieval
, Article International Journal of Healthcare Information Systems and Informatics ; Volume 8, Issue 2 , 2013 , Pages 15-27 ; 15553396 (ISSN) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
2013
Abstract
Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR...
Comparison of classification and dimensionality reduction methods used in fMRI decoding
, Article Iranian Conference on Machine Vision and Image Processing, MVIP ; 2013 , Pages 175-179 ; 21666776 (ISSN) ; 9781467361842 (ISBN) ; Fatemizadeh, E ; Sharif University of Technology
2013
Abstract
In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve the classification performance; therefore, seven methods in region of interest (RDI) have been compared to answer the following question: which dimensionality reduction procedure performs best? In both tasks, in addition to measuring prediction accuracy, we estimated standard deviation of...
Evaluating the effect of ultrasmall superparamagnetic iron oxide nanoparticles for a long-term magnetic cell labeling
, Article Journal of Medical Physics ; Volume 38, Issue 1 , 2013 , Pages 34-40 ; 09716203 (ISSN) ; Oghabian, M. A ; Allen, B. J ; Amanlou, M ; Masoudi, A ; Daha, F. J ; Sharif University of Technology
2013
Abstract
In order to evaluate the long-term viability, the iron content stability, and the labeling efficiency of mammalian cells using magnetic cell labeling; dextran-coated ultrasmall superparamagnetic iron oxide (USPIOs) nanoparticles with plain surfaces having a hydrodynamic size of 25 nm were used for this study. Tests were carried out in four groups each containing 5 flasks of 5.5 × 10 6 AD-293 embryonic kidney cells. The cell lines were incubated for 24 h using four different iron concentrations with and without protamine sulfate (Pro), washed with phosphate-buffered saline (PBS) and centrifuged three times to remove the unbounded USPIOs. Cell viability was also verified using USPIOs. There...
Adaptive sparse representation for MRI noise removal
, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 24, Issue 5 , October , 2012 , Pages 383-394 ; 10162372 (ISSN) ; Fatemizadeh, E ; Behnam, H ; Sharif University of Technology
World Scientific
2012
Abstract
Sparse representation is a powerful tool for image processing, including noise removal. It is an effective method for Gaussian noise removal by taking advantage of a fixed and learned dictionary. In this study, the variable distribution of Rician noise is reduced in magnetic resonance (MR) images by sparse representation based on reconstruction error sets. Standard deviation of Gaussian noise is used to find these errors locally. The proposed method represents two formulas for local error calculation using standard deviation of noise. The acquired results from the real and simulated images are comparable, and in some cases, better than the best Rician noise removal method due to the...