Biomedical Image Processing / Medical Image Processing
Somayeh Maleki Balajoo; Davoud Asemani; Hamid Soltanian-Zadeh
Volume 12, Issue 2 , September 2018, , Pages 111-124
Abstract
Early alterations of functional connectivity (FC) within the default mode network (DMN) have been reported in Alzheimer’s disease (AD). Recently, the resting-state brain networks have been described with non-stationary profiles since inter- and intra-FC of the brain networks changes over time, ...
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Early alterations of functional connectivity (FC) within the default mode network (DMN) have been reported in Alzheimer’s disease (AD). Recently, the resting-state brain networks have been described with non-stationary profiles since inter- and intra-FC of the brain networks changes over time, even at rest. To fully understand the FC changes that characterize AD, the underlying change of its dynamic pattern needs to be captured. The purpose of this study was to evaluate dynamic FC (dFC) patterns within the DMN in patients with AD relative to healthy aging. Here, a sparse logistic regression (SLR) model was employed to estimate the dFC networks in patients with AD (n = 24) compared with healthy control group (n = 37) using resting-state functional magnetic resonance imaging (rs-fMRI) data. To analyze the dFC network, we introduced a temporal variability-functional pattern (TV-FP) score, which shows how the functional pattern of a given region changes over time. This score is able to quantify the temporal patterns of regions involved in a dFC network. We compared TV-FP score across groups. The results indicate that the main regions of DMN, such as the anterior medial prefrontal cortex (aMPFC) and lateral temporal cortex (LTC), are associated with a significantly increased TV-FP score in the AD group when compared to the HC group. The FC pattern of these regions is impaired in AD according to a conventional static functional connectivity (sFC) analysis. These findings may suggest that aMPFC and LTC may tend to reorganize their functional pattern to compensate for the related functional deficiency due to AD.
Biomedical Image Processing / Medical Image Processing
Somayeh Maleki Balajoo; Davoud Asemani; Hamid Soltanian-Zadeh
Volume 9, Issue 1 , April 2015, , Pages 99-111
Abstract
Although the cognitive deficits due to age-related brain differences have been largely analyzed, the altered connectivity of task related functional networks in aging requires more studies. As the brain of old adults experience some alterations in task performance during cognitive challenges, the related ...
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Although the cognitive deficits due to age-related brain differences have been largely analyzed, the altered connectivity of task related functional networks in aging requires more studies. As the brain of old adults experience some alterations in task performance during cognitive challenges, the related effects on connectivity of functional networks are here evaluated using event-related functional Magnetic Resonance Imaging (fMRI). The fMRI data have been acquired for simple visual and motor tasks. For each subject, several Functional Connectivity (FC) networks including, motor, visual and the default mode networks are firstly calculated using a conventional voxel-wise correlation analysis with predefined region of interest. Then, the strength of functional connectivity is assessed and compared for different age groups. The current study has evaluated three hypotheses on FC of aging brain: the frontal regions involved with motor network try to compensate for declines in the posterior regions, default-mode network is less suppressed and, the posterior regions involved with visual network exhibit less connectivity. The first two hypotheses are accepted by analysis results but visual network behaves differently. Also, results show that the task related functional connectivity is considerably altered in old adults compared to young adults. Old adults demonstrate higher connectivity strength on average with a slightly smaller variance than young adults.
Biomedical Image Processing / Medical Image Processing
Mohammad Reza Rezaeian; Gholam Ali Hossein-Zadeh; Hamid Soltanian Zadeh
Volume 8, Issue 1 , March 2014, , Pages 87-99
Abstract
Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is very complex and depends on radio frequency (RF) power and RF pulse shape. Two approaches ...
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Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is very complex and depends on radio frequency (RF) power and RF pulse shape. Two approaches have been used to saturate contrast agent (CA) protons: continuous wave CEST (CW-CEST) and pulsed CEST. To find the optimal RF pulse, numerical solution of Bloch-McConnell equations (BME) may be used. In this paperwe find the optimum values of RF pulse parameters that maximize the CEST contrast. Discrete pulses have lower specific absorption ratio (SAR) than CW RF pulses. However, since discretization is performed on continuous RF pulses, optimizing the continuous RF pulses leads to the optimization of discrete RF pulses. Therefore, in this paper, Rectangular, Gaussian and Fermi pulses are investigated as CW RF pulses. In this investigation, in addition to considering the SAR limitation, 60 dB approximation for the RF pulse amplitude is used. To compare the efficiency of pulses, their resultant flip angles (FA) are assumed equal. Efficiency of CW-CEST is investigated using two parameters, CEST ratio and SAR. According to these parametres, rectangular, Fermi and Gaussian RF pulses have the best performance respectively. Since implementation of rectangular RF is harder than Gaussian and Fermi RF pulses, Fermi and Gaussian RF pulses are desired. Our results suggest that it is possible to maximize CEST ratio by optimizing parameters of rectangular (with an amplitude of 5.7μT), Gaussian (σ about 0.7s) and Fermi (a-value about 0.3s) pulses. Results are verified by empirical formulation of CEST ratio.
Biomedical Image Processing / Medical Image Processing
Neda Behzadfar; Hamid Soltanian Zadeh
Volume 7, Issue 3 , June 2013, , Pages 219-236
Abstract
Segmentation of tumors in magnetic resonance images is an important task. However, it is quite time consuming and has low accuracy and reproducibility when performed manually. Automating the process is challenging, due to high diversity in appearance of tumor tissue in different patients and in many ...
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Segmentation of tumors in magnetic resonance images is an important task. However, it is quite time consuming and has low accuracy and reproducibility when performed manually. Automating the process is challenging, due to high diversity in appearance of tumor tissue in different patients and in many cases, similarity between tumor and normal tissues. This paper presents semi-automatic approach for analysis of multi-parametric magnetic resonance images (MRI) to segment a highly malignant brain tumor called Glioblastoma multiform (GBM). MRI studies of 12 patients with GBM tumors are used. To show that the proposed method identifies Gd-enhanced tumor pixels from T1-post contrast images minimal user interactions. They are also used to illustrate that the segmentation results obtained by the proposed approach are close to those of an expert, by showing excellent correlations among them (R2=0.97). In order to evaluate the proposed method in practical applications, effects of treatment of GBM brain tumors using Bevacizumab are predicted. Bevacizumab is a recent therapy for stopping tumor growth and even shrinking tumor through inhibition of vascular development (angiogenesis). To this end, two image series of 12 patients before and after treatment and relative changes in the volumes of the Gd-enhanced regions in T1-post contrast images are used as measure of response. The proposed method applies signal decomposition with KNN classifier to minimize user interactions and increase reproducibility of the results. Then histogram analysis is applied to extract statistical features from Gd-enhanced regions of tumor and quantify its micro structural characteristics. Predictive models developed in this work have large regression coefficients (maximum R2=0.91) indicating their capability to predict response to therapy. The results obtained by the proposed approach are compared with those of previous work where excellent correlations are obtained.
Biomedical Image Processing / Medical Image Processing
Maryam Afzali; Emadoddin Fatemizadeh; Hamid Soltanian Zadeh
Volume 7, Issue 1 , June 2013, , Pages 57-64
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 ...
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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 ODFs. To this end, principal diffusion directions (PDDs) of each ODF are needed. In this paper, PDDs are defined as vectors that connect the corresponding local maxima of ODF values. Then, ODFs are interpolated using PDDs.We find the principal direction of ODF of the dataset to be interpolated and then rotate it to lie in the direction of the reference dataset. Now that ODFs are parallel, we apply linear interpolation to generate interpolated data. The proposed method is evaluated and compared with previous protocols. Experimental results show that the proposed interpolation algorithm preserves the principal direction of fiber tracts without producing any deviations in the tracts. It is shown that changes in the entropy of the interpolated ODFs are almost linear and the bloating effect (blurring of the principal directions) can be removed.
Biomedical Image Processing / Medical Image Processing
Hadi Sabahi; Hamid Soltanian Zadeh; Lisa Scarpace; Tom Mikkelsen
Volume 5, Issue 4 , June 2011, , Pages 289-295
Abstract
In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, ...
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In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance Images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pretreatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and posttreatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.
Biomedical Image Processing / Medical Image Processing
Effat Yahaghi; Yashar Nohi; Amir Movafeghi; Hamid Soltanian Zadeh
Volume 4, Issue 1 , June 2010, , Pages 1-11
Abstract
Magnetic resonance imaging (MRI) is a non-ionizing method for identification and evaluation of soft tissue lesions. Perfusion MRI evaluates soft tissues by measuring changes in magnetization of water molecules due to a contrast agent. To this end, concentration curves in the plasma and tissue are estimated ...
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Magnetic resonance imaging (MRI) is a non-ionizing method for identification and evaluation of soft tissue lesions. Perfusion MRI evaluates soft tissues by measuring changes in magnetization of water molecules due to a contrast agent. To this end, concentration curves in the plasma and tissue are estimated by MRI and effective longitudinal relaxation time (T1eff) of the tissue was calculated. To interpret the results, the effects of water exchange on the effective longitudinal relaxation time should be studied. This work presents such a study in which the equations of two- and three-compartmental models of rat brain tissue are solved using Hion and Runge-Kutta numerical methods for different input functions and simulated by Monte Carlo method. Since the exchange of water and contrast agent among different tissue compartments is a diffusion phenomenon, Monte Carlo method is applicable. Results of the numerical methods were compared with those of Monte Carlo simulation. The results of the two methods were almost identical with a maximum relative difference of less than 1%. In this work, concentration of contrast agent in plasma is estimated from MRI of a rat brain tissue. This data is used in the Monte Carlo method to obtain T1eff and exchange rate constants. An advantage of our method is that T1eff is obtained from real data and not from the curve fitting method as commonly used. We derive concentration of contrast agent as a function of time in extravascular space for different constants (K). Then, the curves of simulated and real data were compared to obtain the exchange rate constant of each compartment. The results showed that K of an abnormal tissue was larger than that of the normal tissues. As such, this parameter may be used for diagnosis and treatment of the soft tissue diseases.
Biomedical Image Processing / Medical Image Processing
Ladan Amini; Hamid Soltanian Zadeh; Caro Lucas; Masoume Giti
Volume -2, Issue 1 , July 2005, , Pages 17-34
Abstract
Based on a discrete dynamic contour model, a method for segmentation of brain structures like thalamus and red nucleus from magnetic resonance images (MRI) is developed. A new method for solving common problems in extracting the discontinuous boundary of a structure from a low contrast image is presented. ...
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Based on a discrete dynamic contour model, a method for segmentation of brain structures like thalamus and red nucleus from magnetic resonance images (MRI) is developed. A new method for solving common problems in extracting the discontinuous boundary of a structure from a low contrast image is presented. External and internal forces deform the dynamic contour model. Internal forces are obtained from local geometry of the contour, which consist of vertices and edges, connecting adjacent vertices. The image data and desired image features such as image energy are utilized to obtain external forces. The problem of low contrast image data and unclear edges in the image energy is overcome by the proposed algorithm that uses several methods like thresholding, unsupervised clustering methods such as fuzzy C-means (FCM), edge-finding filters like Prewitt, and morphological operations. We also present a method for generating an initial contour for the model from the image data automatically. Evaluation and validation of the methods are conducted by comparing radiologist and automatic segmentation results. The average of the similarity between segmentation results is 0.8 for the left and right thalami indicating excellent performance of the new method. Additional noise and intensity inhomogeneity changed the evaluation results slightly illustrating the robustness of the proposed method to the image noise and intensity inhomogeneity.
