This presentation will focus on the value of established and newer MR methods that can be applied to the diagnosis and management of ischemic stroke with emphasis on future applications of MR to provide previously unmet needs of the treating clinician and clinical trials. Time alone is an inadequate indicator of the therapeutic window, especially when the time of stroke onset is uncertain. Thus, there is a need to predict the evolution of stroke in a way that more precisely and with greater resolution identifies the progression of cellular damage at the moment of investigation. This also would be of value for thrombolysis when knowledge of the degree and extent of tissue necrosis and the consequent potential for brain hemorrhage is of the utmost importance. To provide this, we perform postprocessing of diffusion-, T-1- and T-2-weighted images to produce the apparent diffusion coefficient of water, and T-1 and T-2 maps that are then further processed to provide maps and quantitation of the tissue signatures of ischemic histopathology. By these means, we can accomplish objective volumetric analysis of infarct size and of the proportions of potentially viable and salvageable tissue. We will show how this has the potential to predict long-term stroke outcome and facilitate decision-making in terms of safety of reperfusion strategies and the appropriateness of cytoprotective treatment. The value of our approach is to replace time as the therapeutic window and extend the opportunity of treatment to those patients presenting beyond the stringent time limits employed in current investigative clinical trials. Further, used as a surrogate marker of clinical outcome, this form of stroke analysis may speed proof of principle clinical trials in small numbers of stroke patients. Copyright (C) 2001 S. Karger AG, Basel.

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. J. Magn. Reson. Imaging 2000;11:425–437. © 2000 Wiley‐Liss, Inc.

PURPOSE To determine the transverse relaxation rates R2 and R2' from several gray matter regions and from frontal cortical white matter in healthy human brains in vivo and to determine the relationship between relaxation rates and iron concentration [Fe]. MATERIALS AND METHODS Six healthy adults aged 19-42 years underwent thin-section gradient-echo sampling of free induction decay and echo magnetic resonance (MR) imaging at 3.0 T. Imaging covered the mesencephalon and basal ganglia. RESULTS Relaxation rates (mean +/- SD) were highest in globus pallidus (R2 = 25.8 seconds-1 +/- 1.1, R2' = 12.0 seconds-1 +/- 2.1) and lowest in prefrontal cortex (R2 = 14.4 seconds-1 +/- 1.8, R2' = 3.4 seconds-1 +/- 1.1). Frontal white matter measurements were as follows: R2 = 18.0 seconds-1 +/- 1.2 and R2' = 3.9 seconds-1 +/- 1.2. For gray matter, both R2 and R2' showed a strong correlation (r = 0.92, P < .001 and r = 0.90, P < .001, respectively) with [Fe]. Although the slopes of the regression lines for R2' versus [Fe] and for R2 versus [Fe] were similar, the iron-independent component of R2' (2.2 seconds-1 +/- 0.6), the value when [Fe] = 0, was much less than that of R2 (12.7 seconds-1 +/- 0.7). CONCLUSION The small iron-independent component R2', as compared with that of R2, is consistent with the hypothesis that R2' has higher iron-related specificity.

We present a method for coregistration and warping of magnetic resonance images (MRI) to histological sections for comparison purposes. This methodology consists of a modified head and hat surface-based registration algorithm followed by a new automated warping approach using nonlinear thin plate splines to compensate for distortions between the data sets. To test the methodology, 15 male Wistar rats were subjected to focal cerebral ischemia via permanent occlusion of the middle cerebral artery. The MRI images were acquired in separate groups of animals at 16-24 h (n = 9) and 48-168 h (n = 6) postocclusion. After imaging, animals were immediately sacrificed and hematoxylin- and eosin-stained brain sections were obtained for histological analysis. The MRI was coregistered and warped to histological sections. The MRI lesion areas were defined using the Eigenimage (EI) filter technique. The EI is a linear filter that maximizes the projection of a desired tissue (ischemic tissue) while it minimizes the projection of undesired tissues (nonischemic tissue) onto a composite image called an EI. When using coregistration without warping the MRI lesion area demonstrated poor correlation (r = 0.55, p > 0.01) with a percent difference between the two lesion areas of 22.5% +/- 10.8%. After warping, the MRI and histology had significant correlation (r = 0.97, p < 0.01) and a decreased percent difference of 5.56% +/- 4.31%. This methodology is simple and robust for coregistration and warping of MRI to histological sections and can be utilized in many applications for comparison of MRI to histological data.

This paper presents reproducibility studies for the segmentation results obtained by our optimal MRI feature space method. The steps of the work accomplished are as follows. (1) Eleven patients with brain tumors were imaged by a 1.5 T General Electric Signa MRI System. Four T2- weighted and two T1-weighted images (before and after Gadolinium injection) were acquired for each patient. (2) Images of a slice through the center of the tumor were selected for processing. (3) Patient information was removed from the image headers and new names (unrecognizable by the image analysts) were given to the images. These images were blindly analyzed by the image analysts. (4) Segmentation results obtained by the two image analysts at two time points were compared to assess the reproducibility of the segmentation method. For each tissue segmented in each patient study, a comparison was done by kappa statistics and a similarity measure (an approximation of kappa statistics used by other researchers), to evaluate the number of pixels that were in both of the segmentation results obtained by the two image analysts (agreement) relative to the number of pixels that were not in both (disagreement). An overall agreement comparison was done by finding means and standard deviations of kappa statistics and the similarity measure found for each tissue type in the studies. The kappa statistics for white matter was the largest (0.80) followed by those of gray matter (0.68), partial volume (0.67), total lesion (0.66), and CSF (0.44). The similarity measure showed the same trend but it was always higher than kappa statistics. It was 0.85 for white matter, 0.77 for gray matter, 0.73 for partial volume, 0.72 for total lesion, and 0.47 for CSF.

H. Soltanian-Zadeh and J.P. Windham read with interest a paper by D.H. Laidlaw et al. (see ibid., vol. 17, no. 1, p. 74-86, 1998). H. Soltanian-Zadeh and J.P. Windham felt that the methods D.H. Laidlaw et al. presented are interesting and illustrate the significance of partial volume information in medical image analysis. However, D.H. Laidlaw et al. seem to be unaware of the details of the literature on optimal partial volume estimation from MRI, although they cite one of the references. As such, there are a few important points that D.H. Laidlaw et al. did not describe correctly. The purpose of this communication by H. Soltanian-Zadeh and J.P. Windham is to explain these points.

In this paper we present a new 3D discrete dynamic surface model. The model consists of vertices and edges, which connect adjacent vertices. Basic geometry of the model surface is generated by triangle patches. The model deforms by internal and external forces. Internal forces are obtained from local geometry of the model and are related to the local curvature of the surface. External forces, on the other hand, are based on the image data and are calculated from desired image features. We also present a method for generating an initial volume for the model from a stack of initial contours, drawn by the user on cross sections of the volumetric data.

A major problem in tumor treatment planning and evaluation is determination of the tumor extent. This paper presents a pattern analysis methodology for segmentation and characterization of brain tumors from multispectral NMR images. The proposed approach has been used in 15 clinical studies of cerebral tumor patients who have been scheduled for surgical biopsy and resection. The tissue biopsy results, obtained at specific spatial coordinates determined in the analysis, have been utilized to validate the methodology. It was found that in all cases the lesion had extended into normal tissue, at least to the location where the sample was taken. In most cases, the proposed method suggested that the lesion had extended several millimetres beyond the point from where the biopsy sample was taken. In some cases, the extent of the lesion into normal tissue was well beyond the boundary seen on T(1)- or T(2)-weighted images. It is concluded that the proposed approach indicates brain tumor infiltration more precisely than what is visualized in the original NMR images and therefore its utilization facilitates proper treatment planning for the cerebral tumor patients. (C) 1998 John Wiley & Sons, Ltd.

Using newly developed computerized image analysis, we studied the heterogeneity of apparent diffusion coefficient of water (ADCw) values in human ischemic stroke within 10 hours of onset.Echo-planar trace diffusion-weighted images from 9 patients with focal cortical ischemic stroke were obtained within 10 hours of symptom onset. An Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm was implemented to segment different tissue types with a series of DW images. ADCw maps were calculated from 4 DW images on a pixel-by-pixel basis. The segmented zones within the lesion were characterized as low, pseudonormal, or high, expressed as a ratio of the mean+/-SD of ADCw of contralateral noninvolved tissue.The average ADCW in the ischemic stroke region within 10 hours of onset was significantly depressed compared with homologous contralateral tissue (626.6+/-76.8 versus 842.9+/-60.4x10(-6) mm2/s; P<0.0001). Nevertheless, ISODATA segmentation yielded multiple zones within the stroke region that were characterized as low, pseudonormal, and high. The mean proportion of low:pseudonormal:high was 72%:20%:8%.Despite low average ADCW, computer-assisted segmentation of DW MRI detected heterogeneous zones within ischemic lesions corresponding to low, pseudonormal, and high ADCw not visible to the human eye. This supports acute elevation of ADCw in human ischemic stroke and, accordingly, different temporal rates of tissue evolution toward infarction.

BACKGROUND AND PURPOSE:Using newly developed computerized image analysis, we studied the heterogeneity of apparent diffusion coefficient of water (ADCw) values in human ischemic stroke within 10 hours of onset.METHODS:Echo-planar trace diffusion-weighted images from 9 patients with focal cortical ischemic stroke were obtained within 10 hours of symptom onset. An Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm was implemented to segment different tissue types with a series of DW images. ADCw maps were calculated from 4 DW images on a pixel-by-pixel basis. The segmented zones within the lesion were characterized as low, pseudonormal, or high, expressed as a ratio of the mean+/-SD of ADCw of contralateral noninvolved tissue.RESULTS:The average ADCW in the ischemic stroke region within 10 hours of onset was significantly depressed compared with homologous contralateral tissue (626.6+/-76.8 versus 842.9+/-60.4x10(-6) mm2/s; P<0.0001). Nevertheless, ISODATA segmentation yielded multiple zones within the stroke region that were characterized as low, pseudonormal, and high. The mean proportion of low:pseudonormal:high was 72%:20%:8%.CONCLUSIONS:Despite low average ADCW, computer-assisted segmentation of DW MRI detected heterogeneous zones within ischemic lesions corresponding to low, pseudonormal, and high ADCw not visible to the human eye. This supports acute elevation of ADCw in human ischemic stroke and, accordingly, different temporal rates of tissue evolution toward infarction.

This paper presents development and application of an automated scheme to minimize user dependency of the eigenimage filter. The steps of the new method are as follows: (1) User defines sample regions of interest on central location of the volume and generates the corresponding eigenimages. (2) Original images are segmented using a self organizing data analysis technique. (3) Regions for the tissue types are automatically found from the segmentation results. (4) Signature vectors are estimated from these regions and are compared with those obtained from the user initialization. If they are similar, these signature vectors are used to run eigenimage filtering. If they are not similar, the clustering parameters are adjusted and the procedure is repeated until similar signatures are found. (5) Next slice is loaded and signature vectors from the previous slice are used to get initial eigenimages. Cluster analysis is used to generate regions. The procedure described in the previous step is repeated until similar signatures are found. Then, final eigenimages are obtained. (6) Previous step is repeated until the last slice of the volume is analyzed. (7) Volume of each tissue type is estimated from the resulting eigenimages. Details and significance of each step are explained. Experimental results using simulation, phantom, and brain images are presented.

The application of a deformable model to the segmentation of hippocampus in brain MRI has been investigated. Common problems of the model in this case and similar cases have been discussed and solved. A new method for extracting discontinuous boundaries of an object with multiple unwanted edges has been developed. This method is based on detecting and following the edge by external forces. For improving the contour stability, its movement has been limited. Also, adaptive values for internal force weights have been used. In the next step, the model has been extended to 3D which is a Deformable Surface Model. A geometric structure used for this purpose. This helps in definition of normal vectors and internal forces. Finally, a method for generating the initial volume from individual initial polygons has been developed

We have developed a multiparameter magnetic resonance imaging (MRI) cluster analysis model of acute ischemic stroke using T2 relaxation times and the diffusion coefficient of water (ADCw). To test the ability of this model to predict cerebral infarction, male Wistar rats (n = 7) were subjected to 2 h of transient middle cerebral artery (MCA) occlusion, and diffusion and T2 weighted MRI were performed on these rats before, during and up to 7 days after MCA occlusion. MRI tissue signatures, specified by values of ADCw and T2 were assigned to tissue histopathology. Significant correlations were obtained between MRI signatures at different time points and histopathologic measurements of lesion area obtained at 1 week. In addition, we compared the temporal evolution of MRI tissue signatures to a separate population of animals at which histological data were obtained at select times of reperfusion. A significant shift (p ≤ 0.05) within signatures reflecting tissue histopathology was demonstrated as the ischemic lesion evolved over time. Our data suggest, that the MRI signatures are associated with the degree of ischemic cell damage. Thus, the tissue signature model may provide a noninvasive means to monitor the evolution of ischemic cell damage and to predict final outcome of ischemic cell damage. © 1997 Elsevier Science B.V.

Objective: This study aimed to determine whether magnetic resonance imagine with Eigen image filtering can segment acoustic neuromas as a preliminary requirement to the development and validation of a volumetric method of measuring tumor size and growth using Eigen image filtering.Study Design: This was an observational study.Setting: The study was performed in an academic, comprehensive multi-specialty group practice.Patients: Patients were a convenience sample of adults of both sexes who had acoustic neuromas identified by magnetic resonance imaging. Tumors ranged widely in size.Intervention: Magnetic resonance imaging with digital image analysis using Eigen image filtering was the intervention.Main Outcome Measure: Observation and analysis of magnetic resonance images was the main outcome measure.Results: The ability of magnetic resonance imaging with Eigen image filtering to segment acoustic neuromas of various sizes and shapes is illustrated.Conclusions: Magnetic resonance imaging with digital image analysis using Eigen image filtering is a promising method for volumetric measurement of acoustic neuroma size and growth. The potential for acoustic neuromas to grow may be underestimated by linear methods because of their relative lack of precision and of the geometric error that occurs in rep resenting the volumetric growth of a three-dimensional tumor as a linear diameter.