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Hamid Soltanian-Zadeh

Publications and source records attributed to Hamid Soltanian-Zadeh.

At least 19 recordsLinked to original sources
PubMed2006

Multisubject activation detection in fMRI by testing correlation of data with a signal subspace.

In this article, a generalized likelihood ratio test is proposed to assess the correlation between multisubject functional MRI (fMRI) time series and bases of a signal subspace for detecting the existence of group activation in each voxel of the brain. The signal subspace is generated by a design matrix using the time series of the desired effects. The proposed method leads to testing the product of eigenvalues of a specific matrix. The eigenvector corresponding to the largest eigenvalue is the weighting vector for the linear combination of time series of various subjects that has the maximum correlation with the signal subspace. In another method, namely, canonical correlation analysis, the largest eigenvalue of the above matrix is tested for activation detection. Surrogate data on resting state (no activation) are generated by randomization and used to estimate the statistical distribution of these parameters under the null hypothesis condition. A postprocessing step is applied to prevent false detection of voxels that are not sufficiently active (among subjects) by defining a minimum ratio for the active population. The proposed methods are applied on simulated and experimental fMRI data, and the results are compared with those of the general linear model (GLM; using the SPM and FMRISTAT toolboxes). The proposed methods showed higher detection sensitivity as compared with the GLM for activation detection in simulated data. Similarly, they detected more activated regions than did the GLM from multisubject experimental fMRI data on a visual (sensorimotor) event-related task.

Algorithms↗
PubMed2006

Characterization of cerebral tissue by MRI map ISODATA in embolic stroke in rat.

ISODATA using MRI parameter-weighted images has been previously employed to characterize ischemic cell damage after stroke in rats. In an effort to increase the objectivity and to further automate the ISODATA, MRI parameter maps were now employed. Male Wistar rats were subjected to embolic stroke and received treatment via a femoral vein at 4 h post-stroke. The control rats received saline and were sacrificed at 6, 24 and 48 h after stroke, respectively. Treated rats received rtPA alone or were treated with a combination of rtPA and an antibody, 7E3 F(ab')2, against the glycoprotein receptor that binds the platelet to fibrin. These rats were sacrificed at 24, or 48, h post-stroke. T1, T2 and diffusion maps were employed for map ISODATA analysis. H&E histological analysis of coronal sections of tissue was performed and compared with map ISODATA from the corresponding sections. ISODATA signatures were highly correlated (R approximately 0.80, P < 0.0001) with the ischemic cell damage analyzed at 6, 24 and 48 h post-stroke. At 24 and 48 h after stroke, ISODATA lesion sizes were highly correlated (R > 0.97, P < 0.001) with lesion sizes measured histologically. The combination treatment of rtPA and 7E3 F(ab')2 reduced both infarction size (P < 0.002) and average signature (P < 0.03) at 48 h after stroke, compared to saline-treated animals. No significant difference was found between saline and rtPA-alone-treated rats. The map ISODATA successfully provides objective and automated quantitation of the ischemic damage in both size and severity in an embolic stroke model of rat with and without a therapeutic intervention.

Animals↗
PubMed2006

Integrated MEG/EEG and fMRI model based on neural masses.

We introduce a bottom-up model for integrating electroencephalography (EEG) or magnetoencephalography (MEG) with functional magnetic resonance imaging (fMRI). An extended neural mass model is proposed based on the physiological principles of cortical minicolumns and their connections. The fMRI signal is extracted from the proposed neural mass model by introducing a relationship between the stimulus and the neural activity and using the resultant neural activity as input of the extended Balloon model. The proposed model, validated using simulations, is instrumental in evaluating the upcoming combined methods for simultaneous analysis of MEG/EEG and fMRI.

Algorithms↗
PubMed2005

Integrated MEG and fMRI model: synthesis and analysis.

An integrated model for magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) is proposed. In the model, the neural activity is related to the Post Synaptic Potentials (PSPs) which is common link between MEG and fMRI. Each PSP is modeled by the direction and strength of its current flow which are treated as random variables. The overall neural activity in each voxel is used for equivalent current dipole in MEG and as input of extended Balloon model in fMRI. The proposed model shows the possibility of detecting activation by fMRI in a voxel while the voxel is silent for MEG and vice versa. Parameters of the model can illustrate situations like closed field due to non-pyramidal cells, canceling effect of inhibitory PSP on excitatory PSP, and effect of synchronicity. In addition, the model shows that the crosstalk from neural activities of the adjacent voxels in fMRI may result in the detection of activations in these voxels that contain no neural activities. The proposed model is instrumental in evaluating and comparing different analysis methods of MEG and fMRI. It is also useful in characterizing the upcoming combined methods for simultaneous analysis of MEG and fMRI.

Algorithms↗
PubMed2005

Predicting final infarct size using acute and subacute multiparametric MRI measurements in patients with ischemic stroke.

PURPOSE: To identify early MRI characteristics of ischemic stroke that predict final infarct size three months poststroke. MATERIALS AND METHODS: Multiparametric MRI (multispin echo T2-weighted [T2W] imaging, T1-weighted [T1W] imaging, and diffusion-weighted imaging [DWI]) was performed acutely (<24 hours), subacutely (three to five days), and at three months. MRI was processed using maps of apparent diffusion coefficient (ADC), T2, and a self-organizing data analysis (ISODATA) technique. Analyses began with testing for individual MRI parameter effects, followed by multivariable modeling with assessment of predictive ability (R(2)) on final infarct size. RESULTS: A total of 45 patients were studied, 15 of whom were treated with tissue plasminogen activator (tPA) before acute MRI. The acute DWI and DWI-ISODATA mismatch lesion size, and the interactions of ADC, T2, and T2W imaging lesion with tPA remained in the final multivariable model (R(2) = 70%). A large acute DWI lesion or DWI < ISODATA lesion independently predicted increase in the final infract size, with predictive ability 68%. Predictive ability increased (R(2) = 83%) when subacute MRI parameters were included along with acute DWI, DWI-ISODATA mismatch, and acute T2W image lesion size by tPA treatment interaction. Subacute DWI > acute DWI lesion size predicted an increased final infarct size (P < 0.01). CONCLUSION: Acute-phase DWI and DWI-ISODATA mismatch strongly predict the final infarct size. An acute-to-subacute DWI lesion size change further increases the predictive ability of the model.

Acute Disease↗
PubMed2005

Map-ISODATA demarcates regional response to combination rt-PA and 7E3 F(ab')2 treatment of embolic stroke in the rat.

PURPOSE: To investigate the ability of map-ISODATA (Iterative Self-Organizing Data Analysis Technique) to classify the different categories of ischemic damage in the lesion and to evaluate a combined (thrombolysis plus antiplatelet) treatment efficacy in an embolic stroke of rat. MATERIALS AND METHODS: Rats subjected to embolic stroke with (N=12) and without (N=10) rt-PA and 7E3 F(ab')2 treatment (4 hours after embolization) were followed (at 2, 24, and 48 hours post-MCAO) with magnetic resonance imaging (MRI) using T1, T2, and apparent diffusion coefficient of water (ADCw). ISODATA was computed from T1, T2, and ADCw maps. The signatures characterized by the map-ISODATA were compared with histological quantitative evaluation and were employed to demarcate the specific regions in the lesion. RESULTS: The signature described by map-ISODATA is highly correlated with the degree of tissue damage in the lesion and can distinguish the severity of ischemic tissue injury. Based upon map-ISODATA, ischemic lesion area can be divided into three specific regions, each characterized by a distinct evolution of injury and treatment response. The combined treatment significantly reduces the lesion size between 24 and 48 hours and improves the outcome 48 hours post-MCAO compared with the control group. CONCLUSION: Map-ISODATA provides an accurate means to identify lesion area, to distinguish ischemic damage, and to detect treatment response. 7E3 F(ab')2 extends the rt-PA treatment window to at least four hours after the onset of embolic stroke of rat.

Analysis of Variance↗
PubMed2005

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PURPOSE: To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. MATERIALS AND METHODS: Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. RESULTS: The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. CONCLUSION: More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features.

Algorithms↗
PubMed2005

Content-based image database system for epilepsy.

We have designed and implemented a human brain multi-modality database system with content-based image management, navigation and retrieval support for epilepsy. The system consists of several modules including a database backbone, brain structure identification and localization, segmentation, registration, visual feature extraction, clustering/classification and query modules. Our newly developed anatomical landmark localization and brain structure identification method facilitates navigation through an image data and extracts useful information for segmentation, registration and query modules. The database stores T1-, T2-weighted and FLAIR MRI and ictal/interictal SPECT modalities with associated clinical data. We confine the visual feature extractors within anatomical structures to support semantically rich content-based procedures. The proposed system serves as a research tool to evaluate a vast number of hypotheses regarding the condition such as resection of the hippocampus with a relatively small volume and high average signal intensity on FLAIR. Once the database is populated, using data mining tools, partially invisible correlations between different modalities of data, modeled in database schema, can be discovered. The design and implementation aspects of the proposed system are the main focus of this paper.

Brain↗
PubMed2005

3-D quantification and visualization of vascular structures from confocal microscopic images using skeletonization and voxel-coding.

This paper presents an image processing approach for information extraction from three-dimensional (3-D) images of vasculature. It extracts quantitative information such as skeleton, length, diameter, and vessel-to-tissue ratio for different vessels as well as their branches. Furthermore, it generates 3-D visualization of vessels based on desired anatomical characteristics such as vessel diameter or 3-D connectivity. Steps of the proposed approach are: (1) pre-processing, (2) distance mappings, (3) branch labeling, (4) quantification, and (5) visualization. We have tested and evaluated the proposed algorithms using simulated images of multi-branch vessels and real confocal microscopic images of the vessels in rat brains. Experimental results illustrate performance of the methods and usefulness of the results for medical image analysis applications.

Animals↗
PubMed2005

Three-dimensional analysis of complex branching vessels in confocal microscopy images.

The characteristic of confocal microscopy (CM) vascular data is that it contains many tiny vessels with branching and complex structure. In this work, an automated method for quantitative analysis and reconstruction of cerebral vessels from CM images is presented in which the extracted centerline of the vessels plays the key role. To assess the efficiency and accuracy of different centerline extraction methods, a comparison among three fully automated approaches is given. The centerline extraction methods studied in this work are a snake model, a path planning approach, and a distance transform-based method. To evaluate the accuracy of the quantitative parameters of vessels such as length and diameter, we apply the method to synthetic data. These results indicate that the snake model and the path planning method are more accurate in extracting the quantitative parameters. The efficiency of the approach in clinical applications is then confirmed by applying the method to real CM images. All three methods investigated in this work are accurate enough to correctly distinguish between normal and stroke brain data, while the snake model is the fastest for clinical applications. In addition, three-dimensional visualization, reconstruction, and characterization of CM vascular images of rat brains are presented.

Brain↗
PubMed2005

Rotation-invariant multiresolution texture analysis using radon and wavelet transforms.

A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A kappa-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.

Algorithms↗
PubMed2005

Radon transform orientation estimation for rotation invariant texture analysis.

This paper presents a new approach to rotation invariant texture classification. The proposed approach benefits from the fact that most of the texture patterns either have directionality (anisotropic textures) or are not with a specific direction (isotropic textures). The wavelet energy features of the directional textures change significantly when the image is rotated. However, for the isotropic images, the wavelet features are not sensitive to rotation. Therefore, for the directional textures, it is essential to calculate the wavelet features along a specific direction. In the proposed approach, the Radon transform is first employed to detect the principal direction of the texture. Then, the texture is rotated to place its principal direction at 0 degrees. A wavelet transform is applied to the rotated image to extract texture features. This approach provides a features space with small intraclass variability and, therefore, good separation between different classes. The performance of the method is evaluated using three texture sets. Experimental results show the superiority of the proposed approach compared with some existing methods.

Algorithms↗
PubMed2004

Multiparametric ISODATA analysis of embolic stroke and rt-PA intervention in rat.

To increase the sensitivity of MRI parameters to detect tissue damage of ischemic stroke, an unsupervised analysis method, Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA), was applied to analyze the temporal evolution of ischemic damage in a focal embolic cerebral ischemia model in rat with and without recombinant tissue plasminogen activator (rt-PA) treatment. Male Wistar rats subjected to embolic stroke were investigated using a 7-T MRI system. Rats were randomized into control (n=9) and treated (n=9) groups. The treated rats received rt-PA via a femoral vein at 4 h after onset of embolic ischemia. ISODATA analysis employed parametric maps or weighted images (T1, T2, and diffusion). ISODATA results with parametric maps are superior to ISODATA with weighted images, and both of them were highly correlated with the infarction size measured from the corresponding histological section. At 24 h after embolic stroke, the average map ISODATA lesion sizes were 37.7+/-7.0 and 39.2+/-5.6 mm2 for the treated and the control group, respectively. Average histological infarction areas were 37.9+/-7.4 mm2 for treated rats and 39.4+/-6.1 mm2 for controls. The R2 values of the linear correlation between map ISODATA and histological data were 0.98 and 0.96 for treated and control rats, respectively. Both histological and map ISODATA data suggest that there is no significant difference in infarction area between non-treated and rt-PA-treated rats when treatment was administered 4 h after the onset of embolic stroke. The ISODATA lesion size analysis was also sensitive to changes of lesion size during acute and subacute stages of stroke. Our data demonstrate that the multiparameter map ISODATA approach provides a more sensitive quantitation of the ischemic lesion at all time points than image ISODATA and single MRI parametric analysis using T1, T2 or ADCw.

Algorithms↗
PubMed2004

Controlling the false positive rate in fuzzy clustering using randomization: application to fMRI activation detection.

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false-positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false-positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false-positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this article, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space. In both cases, the HRF-based feature space provides a greater sensitivity compared to the cross-correlation feature space and conventional cross-correlation analysis. Application of the proposed method to finger-tapping fMRI data, using HRF-based feature space, detected activation in sub-cortical regions, whereas both of the FCM with cross-correlation feature space and the conventional cross-correlation method failed to detect them.

Algorithms↗
PubMed2004

Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours.

Thalamus is an important neuro-anatomic structure in the brain. In this paper, an automated method is presented to segment thalamus from magnetic resonance images (MRI). The method is based on a discrete dynamic contour model that consists of vertices and edges connecting adjacent vertices. The model starts from an initial contour and deforms by external and internal forces. Internal forces are calculated from local geometry of the model and external forces are estimated from desired image features such as edges. However, thalamus has low contrast and discontinues edges on MRI, making external force estimation a challenge. The problem is solved using a new algorithm based on fuzzy C-means (FCM) unsupervised clustering, Prewitt edge-finding filter, and morphological operators. In addition, manual definition of the initial contour for the model makes the final segmentation operator-dependent. To eliminate this dependency, new methods are developed for generating the initial contour automatically. The proposed approaches are evaluated and validated by comparing automatic and radiologist's segmentation results and illustrating their agreement.

Algorithms↗
PubMed2004

Model-independent method for fMRI analysis.

This paper presents a fast method for delineation of activated areas of the brain from functional magnetic resonance imaging (fMRI) time series data. The steps of the work accomplished are as follows. 1) It is shown that the detection performance evaluated by the area under the receiver operating characteristic curve is directly related to the signal-to-noise ratio (SNR) of the composite image generated in the detection process. 2) Detection and segmentation of activated areas are formulated in a vector space framework. In this formulation, a linear transformation (image combination method) is shown to be desirable to maximize the SNR of the activated areas subject to the constraint of removing inactive areas. 3) An analytical solution for the problem is found. 4) Image pixel vectors and expected time series pattern (signature) for inactive pixels are used to calculate weighting vector and identify activated regions. 5) Signatures of the activated regions are used to segment different activities. 6) Segmented images by the proposed method are compared with those generated by the conventional methods (correlation, t-statistic, and z statistic). Detection performance and SNRs of the images are compared. The proposed approach outperforms the conventional methods of fMRI analysis. In addition, it is model-independent and does not require a priori knowledge of the fMRI response to the paradigm. Since the method is linear and most of the work is done analytically, numerical implementation and execution of the method are much faster than the conventional methods.

Adult↗
PubMed2004

Multiparametric iterative self-organizing MR imaging data analysis technique for assessment of tissue viability in acute cerebral ischemia.

BACKGROUND AND PURPOSE: Defining viability and the potential for recovery of ischemic brain tissue can be very valuable for patient selection for acute stroke therapies. Multiparametric MR imaging analysis of ischemic lesions indicates that the ischemic lesion is inhomogeneous in degree of ischemic injury and recovery potential. We sought to define MR imaging characteristics of ischemic lesions that are compatible with viable tissue. METHODS: We included patients with supratentorial ischemic stroke who underwent multiparametric MR imaging studies (axial multi-spin-echo T2-weighted imaging, T1-weighted imaging, and diffusion-weighted imaging) at the acute (< 24 hours) and outcome (3 months) phases of stroke. Using the algorithm Iterative Self-Organizing Data Analysis Technique (ISODATA), the lesion was segmented into clusters and each was assigned a number, called the tissue signature (white matter = 1, CSF = 12, all others between these two). Recovery was defined as at least a 20% size reduction from the acute phase ISODATA lesion volume to the outcome phase T2-weighted imaging lesion volume. The tissue signature data were collapsed into the following categories: < or = 3, 4, 5, and > or = 6. Logistic regression analysis included the following parameters: lesion volume, tissue signature value, apparent diffusion coefficient (ADC) value, relative ADC (rADC) expressed as a ratio, T2 value, and T2 ratio. The model with the largest goodness of fit value was selected. RESULTS: We included 48 patients (female-male ratio, 26:22; age, 64 [+/-14] years; 15 treated with recombinant tissue plasminogen activator [rt-PA] within 3 hours of onset; median National Institutes of Health Stroke Scale score, 7 [range, 2-26]). Median symptom onset-to-MR imaging time interval was 9.5 hours. With ISODATA processing, we generated 200 region-of-interest tissue records (one to nine tissue records per patient). Regarding tissue recovery, we detected a three-way interaction among ADC, ISODATA tissue signature, and previous treatment with rt-PA (P = .003). In the group not treated with rt-PA, ischemic tissues with acute rADC greater than the median (0.79) and tissue signature < or = 4 were more likely to recover (80% vs. 31% and 13%, odds ratio [95% CI]: 0.12 [0.05, 0.30] and 0.04 [0.01, 0.18] for tissue signatures 5 and 6, respectively). CONCLUSION: ISODATA multiparametric MR imaging of acute stroke clearly shows inhomogeneity and different viability of the ischemic lesion. Ischemic tissues with lower acute phase ISODATA tissue signature values (< or = 4) and higher rADC values (> or = 0.79) are much more likely to recover than those with higher signature values or lower rADC values. The effect of these factors on tissue recovery, however, is dependent on whether preceding treatment with rt-PA had been performed. Our approach can be a valuable tool in the design of therapeutic stroke trials with an extended time window.

Acute Disease↗