Biomedical subjects

Hamid Soltanian-Zadeh

Publications and source records attributed to Hamid Soltanian-Zadeh.

12 recordsLinked to original sources

PubMed2003

Microvascular structure after embolic focal cerebral ischemia in the rat.

OBJECTIVES: We analyze morphological alterations of cerebral neovascularization after stroke using a new 3D imaging software program. METHODS: Male Wistar rats underwent unilateral embolic middle cerebral artery occlusion (MCAo) by a single fibrin rich clot. Subjects were sacrificed from 1 to 28 days post infarct. Vessel perimeters were measured on coronal sections stained with endothelial cell-specific antibody to von Willebrand's factor. Vessel segment lengths, diameters and number of vessels were analyzed on cerebral microvessels perfused with FITC-dextran 14 days after ischemia using LSCM and a 3-D vessel quantification program. RESULTS: The mean number of microvessels with enlarged perimeters significantly increased in the ipsilateral cortex at day 7 when compared to the contralateral cortex (29.7+/-14.7 vs. 3.7+/-2.5, P<0.05). Subsequently, differences in the number of microvessels with enlarged perimeters decreased on days 14 and 28. Fourteen days post-MCA occlusion, microvessel segment length (15.0 vs. 26.0 microm, P<0.05) and diameter (3.14 vs. 3.75 microm, P<0.05) significantly decreased in the ipsilateral hemisphere when compared to the contralateral hemisphere, respectively. Furthermore, the mean total number of these smaller microvessels increased in the ipsilateral hemisphere (57.33+/-14.5 vs. 32.22+/-11.7, P<0.05). CONCLUSIONS: Focal cerebral ischemia induces morphological changes (early dilated microvessels followed by decreased microvessel segment length and diameter) that are consistent with newly generated microvessels.

Animals↗

PubMed2003

MRI tissue characterization of experimental cerebral ischemia in rat.

PURPOSE: To extend the ISODATA image segmentation method to characterize tissue damage in stroke, by generating an MRI score for each tissue that corresponds to its histological damage. MATERIALS AND METHODS: After preprocessing and segmentation (using ISODATA clustering), the proposed method scores tissue regions between 1 and 100. Score 1 is assigned to normal brain matter (white or gray matter), and score 100 to cerebrospinal fluid (CSF). Lesion zones are assigned a score based on their relative levels of similarities to normal brain matter and CSF. To evaluate the method, 15 rats were imaged by a 7T MRI system at one of three time points (acute, subacute, chronic) after MCA occlusion. Then they were killed and their brains were sliced and prepared for histological studies. MRI of two or three slices of each rat brain (using two DWI (b = 400, b = 800), one PDWI, one T2WI, and one T1WI) was performed, and an MRI score between 1 and 100 was determined for each region. Segmented regions were mapped onto the histology images and scored on a scale of 1-10 by an experienced pathologist. The MRI scores were validated by comparison with histology scores. To this end, correlation coefficients between the two scores (MRI and histology) were determined. RESULTS: Experimental results showed excellent correlations between MRI and histology scores at different time points. Depending on the reference tissue (gray matter or white matter) used in the standardization, the correlation coefficients ranged from 0.73 (P < 0.0001) to 0.78 (P < 0.0001) using the entire dataset, including acute, subacute, and chronic time points. This suggests that the proposed multiparametric approach accurately identified and characterized ischemic tissue in a rat model of cerebral ischemia at different stages of stroke evolution. CONCLUSION: The proposed approach scores tissue regions and characterizes them using unsupervised clustering and multiparametric image analysis techniques. The method can be used for a variety of applications in the field of computer-aided diagnosis and treatment, including evaluation of response to treatment. For example, volume changes for different zones of the lesion over time (e.g., tissue recovery) can be evaluated.

Animals↗

PubMed2003

A signal subspace approach for modeling the hemodynamic response function in fMRI.

Many fMRI analysis methods use a model for the hemodynamic response function (HRF). Common models of the HRF, such as the Gaussian or Gamma functions, have parameters that are usually selected a priori by the data analyst. A new method is presented that characterizes the HRF over a wide range of parameters via three basis signals derived using principal component analysis (PCA). Covering the HRF variability, these three basis signals together with the stimulation pattern define signal subspaces which are applicable to both linear and nonlinear modeling and identification of the HRF and for various activation detection strategies. Analysis of simulated fMRI data using the proposed signal subspace showed increased detection sensitivity compared to the case of using a previously proposed trigonometric subspace. The methodology was also applied to activation detection in both event-related and block design experimental fMRI data using both linear and nonlinear modeling of the HRF. The activated regions were consistent with previous studies, indicating the ability of the proposed approach in detecting brain activation without a priori assumptions about the shape parameters of the HRF. The utility of the proposed basis functions in identifying the HRF is demonstrated by estimating the HRF in different activated regions.

Brain Mapping↗

PubMed2003

Multiwavelet grading of pathological images of prostate.

Histological grading of pathological images is used to determine level of malignancy of cancerous tissues. This is a very important task in prostate cancer prognosis, since it is used for treatment planning. If infection of cancer is not rejected by non-invasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time.

Algorithms↗

PubMed2003

Automatic landmark extraction from image data using modified growing neural gas network.

A new method for automatic landmark extraction from MR brain images is presented. In this method, landmark extraction is accomplished by modifying growing neural gas (GNG), which is a neural-network-based cluster-seeking algorithm. Using modified GNG (MGNG) corresponding dominant points of contours extracted from two corresponding images are found. These contours are borders of segmented anatomical regions from brain images. The presented method is compared to: 1) the node splitting-merging Kohonen model and 2) the Teh-Chin algorithm (a well-known approach for dominant points extraction of ordered curves). It is shown that the proposed algorithm has lower distortion error, ability of extracting landmarks from two corresponding curves simultaneously, and also generates the best match according to five medical experts.

Algorithms↗

PubMed2003

Multiresolution fMRI activation detection using translation invariant wavelet transform and statistical analysis based on resampling.

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods.

Algorithms↗

PubMed2003

Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering.

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data.

Algorithms↗

PubMed2003

Efficient center-line extraction for quantification of vessels in confocal microscopy images.

In this paper we present a novel method for the three-dimensional (3-D) centerline extraction of elongated objects such as vessels. This method combines the basic ideas in distance transform-based, thinning, and path planning methods to extract thin and connected centerlines. This efficient approach needs no user interaction or any prior knowledge of the object shape. We used the path planning approach, which has exclusively been used in the virtual endoscopy or robotics, to obtain the medial curve of the objects. To make our approach fully automated, a distance transform mapping is used to identify the end points of the object branches. The initial paths are also constructed on the surface of the object, traversing the same distance map. Then a thinning algorithm centralizes the paths. The proposed approach is especially efficient for centerline extraction of the complex branching structures. The method has been applied on the confocal microscopy images of rat brains and the results confirm its efficiency in extracting the medial curve of vessels, essential for the computation of quantitative parameters.

Algorithms↗

PubMed2002

A PC-based PACS display workstation with a language transparent interface.

The development of new imaging techniques and the increasing proportion of medical imaging modalities that generate images in digital form have catalyzed development of digital image management systems, termed picture archiving and communication systems. Digital images can easily be stored, retrieved, and transmitted to other locations. Once image data are generated, radiologists must be able to extract diagnostic signs from the data. To meet this need, we developed image visualization software called Persian Medical Display Software 1.0 (PMDS1.0), under the Windows platform. Global software architecture and its features, functions, and basic software and hardware requirements are explained in the article. Our studies show that the software is acceptable to Iranian radiologists.

Data Display↗

PubMed2002

Correlation of VEGF and angiopoietin expression with disruption of blood-brain barrier and angiogenesis after focal cerebral ischemia.

In an effort to elucidate the molecular mechanisms underlying cerebral vascular alteration after stroke, the authors measured the spatial and temporal profiles of blood-brain barrier (BBB) leakage, angiogenesis, vascular endothelial growth factor (VEGF), associated receptors, and angiopoietins and receptors after embolic stroke in the rat. Two to four hours after onset of ischemia, VEGF mRNA increased, whereas angiopoietin 1 (Ang 1) mRNA decreased. Three-dimensional immunofluorescent analysis revealed spatial coincidence between increases of VEGF immunoreactivity and BBB leakage in the ischemic core. Two to 28 days after the onset of stroke, increased expression of VEGF/VEGF receptors and Ang/Tie2 was detected at the boundary of the ischemic lesion. Concurrently, enlarged and thin-walled vessels were detected at the boundary of the ischemic lesion, and these vessels developed into smaller vessels via sprouting and intussusception. Three-dimensional quantitative analysis of cerebral vessels at the boundary zone 14 days after ischemia revealed a significant (P < 0.05) increase in numbers of vessels (n = 365) compared with numbers (n = 66) in the homologous tissue of the contralateral hemisphere. Furthermore, capillaries in the penumbra had a significantly smaller diameter (4.8 +/- 2.0 microm) than capillaries (5.4 +/- 1.5 microm) in the homologous regions of the contralateral hemisphere. Together, these data suggest that acute alteration of VEGF and Ang 1 in the ischemic core may mediate BBB leakage, whereas upregulation of VEGF/VEGF receptors and Ang/Tie2 at the boundary zone may regulate neovascularization in ischemic brain.

Angiopoietin-1↗

PubMed2002

Multiparametric MRI ISODATA ischemic lesion analysis: correlation with the clinical neurological deficit and single-parameter MRI techniques.

BACKGROUND AND PURPOSE: The purpose of this study was to show that the computer segmentation algorithm Iterative Self-Organizing Data Analysis Technique (ISODATA), which integrates multiple MRI parameters (diffusion-weighted imaging [DWI], T2-weighted imaging [T2WI], and T1-weighted imaging [T1WI]) into a single composite image, is capable of defining the ischemic lesion in a time-independent manner equally as well as the MRI techniques considered the best for each phase after stroke onset (ie, perfusion weighted imaging [PWI] and DWI for the acute phase and T2WI for the outcome phase). METHODS: We measured MRI parameters of PWI, DWI, T2WI, and T1WI from patients at the acute phase (<30 hours) and DWI, T2WI, and T1WI at the outcome phase (3 months) of ischemic stroke. The clinical neurological deficit was graded with the National Institutes of Health Stroke Scale (NIHSS). We compared the ISODATA lesion size with the PWI, DWI, and T2WI lesion sizes measured within the same slice at each phase. The lesion sizes were also correlated with NIHSS score of each phase. RESULTS: We included 11 patients; 9 (82%) were women, and 7 (64%) were black. The mean+/-SD age was 65.5+/-9.3 years (range, 45 to 82 years). The median NIHSS score was 15 (minimum, 4; maximum, 24)at the acute phase and 3 (minimum, 0; maximum, 22) at the outcome phase. The median time interval from stroke symptom onset to the acute MRI study was 10 hours (range, 6 to 29 hours), and the mean time interval to the outcome study was 93+/-11 days (range, 72 to 106 days). In the acute phase, the ISODATA lesion size had high correlation with the PWI lesion size (r=0.95; 95% CI, 0.89 to 0.98; P<0.0001), DWI lesion size (r=0.83; 95% CI, 0.66 to 0.92; P<0.0001), and T2WI lesion size (r=0.67; 95% CI, 0.39 to 0.84; P=0.008) and moderate correlation with NIHSS score (r=0.59; 95% CI, 0.02 to 0.88; P=0.06). In the outcome phase, the ISODATA lesion size had high correlation with the T2WI lesion size (r=0.97; 95% CI, 0.94 to 0.99; P<0.0001) and NIHSS score (r=0.78; 95% CI, 0.34 to 0.94; P=0.004). CONCLUSIONS: The integrated ISODATA method can identify and characterize the ischemic lesion independently of time elapsed since stroke onset. The ISODATA lesion size highly correlates with the PWI and DWI lesion size in the acute phase and with the T2WI lesion size in the outcome phase of ischemic stroke, as well as with the clinical neurological status of the patient.

Aged↗

PubMedDate not supplied

Snake modeling and distance transform approach to vascular centerline extraction and quantification.

A new method for fully automated centerline extraction and quantification of microvascular structures in confocal microscopy (CM) images is presented. Our method uses the idea of active contour models as well as path planning and distance transforms for the three-dimensional centerline extraction of elongated objects such as vessels. The proposed approach is especially efficient for centerline extraction of complex branching structures. The method performance is validated in several CM images of both normal and stroked rat brains as well as simulated objects. The results confirm the efficiency of the proposed method in extracting the medial curve of vessels, which is essential for the computation of quantitative parameters.

Algorithms↗