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Purpose: To show the feasibility of using magnetic resonance imaging (MRI) to quantify superparamagnetic iron oxide (SPIO)-labeled cells., Materials and Methods: Lymphocytes and 9L rat gliosarcoma cells were labeled with ferumoxides-protamine sulfate complex (FE-PRO). The cells were labeled efficiently (more than 95%) and the iron concentration inside each cell was measured by spectrophotometry (4.77-30.21 pg). Phantom tubes containing different numbers of labeled or unlabeled cells, as well as different concentrations of FE-PRO, were made. In addition, labeled and unlabeled cells were injected into fresh and fixed rat brains., Results: Cellular viability and proliferation of labeled and unlabeled cells were shown to be similar. T2-weighted images were acquired using 7T and 3T MRI systems, and R2 maps of the tubes containing cells, free FE-PRO, and brains were made. There was a strong linear correlation between R2 values and labeled cell numbers, but the regression lines were different for the lymphocytes and gliosarcoma cells. Similarly, there was strong correlation between R2 values and free iron. However, free iron had higher R2 values than the labeled cells for the same concentration of iron., Conclusion: Our data indicate that in vivo quantification of labeled cells can be done by careful consideration of different factors and specific control groups., ((c) 2007 Wiley-Liss, Inc.)

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., ((c) 2005 Wiley-Liss, Inc.)

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., (Copyright (c) 2005 Wiley-Liss, Inc.)

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.

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., (Copyright 2003 Wiley-Liss, Inc.)

The goal of this work was to develop a warping technique for mapping a brain image to another image or atlas data, with minimum user interaction and independent of gray level information. We have developed and tested three different methods for warping magnetic resonance (MR) brain images. We utilize a deformable contour to extract and warp the boundaries of the two images. A mesh-grid coordinate system is constructed for each brain, by applying a distance transformation to the resulting contours, and scaling. In the first method (MGC), the first image is mapped to the second image based on a one-to-one mapping between different layers defined by the mesh-grid. In the second method (IDW), the corresponding pixels in the two images are found using the above mesh-grid system and a local inverse-distance weights interpolation. In the third proposed method (TSB), a subset of grid points is used for finding the parameters of a spline transformation, which defines the global warping. The warping methods were applied to clinical MR consisting of diffusion-weighted and T2-weighted images of the human brain. The IDW and TSB methods were superior in ranking of diagnostic quality of the warped MR images to the MGC (P < 0.01) as defined by a neuroradiologist. The deformable contour warping produced excellent diagnostic quality for the diffusion-weighted images coregistered and warped to T2 weighted images. J. Magn. Reson. Imaging 2000;12:417-429., (Copyright 2000 Wiley-Liss, Inc.)

This study presents histological validation of an objective (unsupervised) computer segmentation algorithm, the iterative self-organizing data analysis technique (ISODATA), for analysis of multiparameter magnetic resonance imaging (MRI) data in experimental focal cerebral ischemia. T2-, T1-, and diffusion (DWI) weighted coronal images were acquired from 4 to 168 hours after stroke on separate groups of animals. Animals were killed immediately after MRI for histological analysis. MR images were coregistered/warped to histology. MRI lesion areas were defined using DWI, apparent diffusion coefficient (ADC) maps, T2-weighted images, and ISODATA. The last techniques clearly discriminated between ischemia-altered and morphologically intact tissue. ISODATA areas were congruent and significantly correlated (r = 0.99, P < 0.05) with histologically defined lesions. In contrast, DWI, ADC, and T2 lesion areas showed no significant correlation with histologically evaluated lesions until subacute time points. These data indicate that multiparameter ISODATA methodology can accurately detect and identify ischemic cell damage early and late after ischemia, with ISODATA outperforming ADC, DWI, and T2-weighted images in identification of ischemic lesions from 4 to 168 hours after stroke., (Copyright 2000 Wiley-Liss, Inc.)