Hassan Bagher-Ebadian

MRI detects white matter reorganization after neural progenitor cell treatment of stroke

We evaluated the effects of neural progenitor cell treatment of stroke on white matter reorganization using MRI. Male Wistar rats (n = 26) were subjected to 3 h of middle cerebral artery occlusion and were treated with neural progenitor cells (n = 17) or without treatment (n = 9) and were sacrificed at 5-7 weeks thereafter. MRI measurements revealed that grafted neural progenitor cells selectively migrated towards the ischemic boundary regions. White matter reorganization, confirmed histologically, was coincident with increases of fractional anisotropy (FA, P < 0.01) after stroke in the ischemic recovery regions compared to that in the ischemic core region in both treated and control groups. Immunoreactive staining showed axonal projections emanating from neurons and extruding from the corpus callosum into the ipsilateral striatum bounding the lesion areas after stroke. Fiber tracking (FT) maps derived from diffusion tensor imaging revealed similar orientation patterns to the immunohistological results. Complementary measurements in stroke patients indicated that FT maps exhibit an overall orientation parallel to the lesion boundary. Our data demonstrate that FA and FT identify and characterize cerebral tissue undergoing white matter reorganization after stroke and treatment with neural progenitor cells.

Model Selection in Measures of Vascular Parameters using Dynamic Contrast Enhanced MRI: Experimental and Clinical Applications

A review of the selection of models in dynamic contrast‐enhanced MRI (DCE‐MRI) is conducted, with emphasis on the balance between the bias and variance required to produce stable and accurate estimates of vascular parameters. The vascular parameters considered as a first‐order model are the forward volume transfer constant Ktrans, the plasma volume fraction vp and the interstitial volume fraction ve. To illustrate the critical issues in model selection, a data‐driven selection of models in an animal model of cerebral glioma is followed. Systematic errors and extended models are considered. Studies with nested and non‐nested pharmacokinetic models are reviewed; models considering water exchange are considered. Copyright

Model selection for DCE-T1 studies in glioblastoma

Dynamic contrast enhanced T1‐weighted MRI using the contrast agent gadopentetate dimeglumine (Gd‐DTPA) was performed on 10 patients with glioblastoma. Nested models with as many as three parameters were used to estimate plasma volume or plasma volume and forward vascular transfer constant (Ktrans) and the reverse vascular transfer constant (kep). These constituted models 1, 2, and 3, respectively. Model 1 predominated in normal nonleaky brain tissue, showing little or no leakage of contrast agent. Model 3 predominated in regions associated with aggressive portions of the tumor, and model 2 bordered model 3 regions, showing leakage at reduced rates. In the patient sample, vp was about four times that of white matter in the enhancing part of the tumor. Ktrans varied by a factor of 10 between the model 2 (1.9 ↔ 10−3 min−1) and model 3 regions (1.9 ↔ 10−2 min−1). The mean calculated interstitial space (model 3) was 5.5%. In model 3 regions, excellent curve fits were obtained to summarize concentration‐time data (mean R2 = 0.99). We conclude that the three parameters of the standard model are sufficient to fit dynamic contrast enhanced T1 data in glioblastoma under the conditions of the experiment. Magn Reson Med, 2012.

Dynamic contrast enhanced MRI parameters and tumor cellularity in a rat model of cerebral glioma at 7 T

To test the hypothesis that a noninvasive dynamic contrast enhanced MRI (DCE‐MRI) derived interstitial volume fraction (ve) and/or distribution volume (VD) were correlated with tumor cellularity in cerebral tumor.

A modified fourier-based phase unwrapping algorithm with an application to MRI venography

To present a single‐step deterministic procedure for unwrapping MRI phase maps.

Intravenous Administration of Human Umbilical Cord Blood-Derived AC133+ Endothelial Progenitor Cells in Rat Stroke Model Reduces Infarct Volume: Magnetic Resonance Imaging and Histological Findings

Endothelial progenitor cells (EPCs) hold enormous therapeutic potential for ischemic vascular diseases. Previous studies have indicated that stem/progenitor cells derived from human umbilical cord blood (hUCB) improve functional recovery in stroke models. Here, we examined the effect of hUCB AC133+ EPCs on stroke development and resolution in a middle cerebral artery occlusion (MCAo) rat model. Since the success of cell therapies strongly depends on the ability to monitor in vivo the migration of transplanted cells, we also assessed the capacity of magnetic resonance imaging (MRI) to track in vivo the magnetically labeled cells that were administered. Animals were subjected to transient MCAo and 24 hours later injected intravenously with 107 hUCB AC133+ EPCs. MRI performed at days 1, 7, and 14 after the insult showed accumulation of transplanted cells in stroke‐affected hemispheres and revealed that stroke volume decreased at a significantly higher rate in cell‐treated animals. Immunohistochemistry analysis of brain tissues localized the administered cells in the stroke‐affected hemispheres only and indicated that these cells may have significantly affected the magnitude of endogenous proliferation, angiogenesis, and neurogenesis. We conclude that transplanted cells selectively migrated to the ischemic brain parenchyma, where they exerted a therapeutic effect on the extent of tissue damage, regeneration, and time course of stroke resolution.

Predicting Final Extent of Ischemic Infarction Using Artificial Neural Network Analysis of Multi-Parametric MRI in Patients with Stroke

In ischemic stroke, the extent of ischemic lesion recovery is one of the most important correlate of functional recovery in brain. Using a set of acute phase MR images (Diffusion-Weighted - DWI, T1-Weighted - T1WI, T2-Weighted T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a pixel-by-pixel forecast of the chronic T2WI. The ANN was trained and tested using 14 slices from 3 subjects using a K-Folding Cross-Validation (KFCV) method with 14 folds. The Area Under the Receiver Operator Characteristic Curve (AUROC) for 14 folds was used for training, testing and optimization of the ANN. After training and optimization, the ANN produced a map that was well correlated (r = 0.88, p ≪ 0.0001) with the T2WI at 3 months. To confirm that the trained ANN performed well against a new dataset, 13 slices from 4 other patients were shown to the trained ANN. The prediction made by the ANN had an excellent overall performance (AUROC = 0.82), and was very well correlated to the 3-month ischemic lesion on T2-Weighted image.

Neural network and fuzzy clustering approach for automatic diagnosis of coronary artery disease in nuclear medicine

We investigated feasibility of using fuzzy clustering and artificial neural network to predict coronary artery disease (CAD) in acute phase from planar and gated SPECT nuclear medicine images. We developed an automatic computerized scheme that helps physicians diagnose coronary artery disease based on 99Tc-Sestamibi myocardial perfusion images. Our study consisted of two separate studies with respect to patient population and imaging method. The first study included 58 subjects (30 male, 28 female) studies using planar rest and stress imaging and a second patient subset of 115 subjects (61 male, 54 female) using gated rest/stress SPECT imaging. After the myocardial perfusion scans, patients also had coronary angiography within three months of the imaging. Signal-to-noise ratio was improved by segmentation of myocardium from its background in both studies using fuzzy clustering with the Picard iteration algorithm. We extracted a set of adaptive features consistent with nature of nuclear medicine imaging and myocardium anatomy. Features were optimized and selected based on maximum separation in multidimensional feature space. A back-propagation artificial neural network (ANN) classifier was trained and tested for each study using the optimal features and the results of coronary angiographies as input and outputs, respectively. ANNs were trained, optimized, and tested using leave-one-out and Pohs Implementation of Weigned-Rumelhart-Huberman (PIWRH) methods, to diagnose the normal and abnormal patients based on their coronary angiograms. The performances of the optimal ANNs were analyzed by receiver operator characteristic (ROC) method. Results of ANN in the first study were compared to those of the physicians in nuclear medicine ward and two other physicians using ROC method. Results of ANN for the second study were compared to those of the nuclear medicine ward using ROC method. Both subsets demonstrate that the proposed method outperforms visual diagnosis and is therefore a useful adjunct for CAD diagnosis from planar and gated SPECT images.

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

To identify early MRI characteristics of ischemic stroke that predict final infarct size three months poststroke.

MRI Measurement of Change in Vascular Parameters in the 9L Rat Cerebral Tumor After Dexamethasone Administration

To demonstrate in the rat 9L cerebral tumor model that repeated MRI measurements can quantitate acute changes in the blood‐brain distribution of Gadomer after dexamethasone administration.