Automated three-dimensional signature model for assessing brain injury in emergent stroke.

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.

Unsupervised segmentation of multiparameter MRI in experimental cerebral ischemia with comparison to T2, diffusion, and ADC MRI parameters and histopathological validation.

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.

Three-dimensional measurement of cerebral microvascular plasma perfusion, glial fibrillary acidic protein and microtubule associated protein-2 immunoreactivity after embolic stroke in rats: a double fluorescent labeled laser-scanning confocal microscopic study.

Early astroglial response to post-ischemic microvascular hypoperfusion may contribute to progressive cerebral microcirculatory impairment and ischemic neuronal injury. Using laser-scanning confocal microscopy and three fluorescent probes, we measured in three-dimensions cerebral microvascular plasma perfusion, astrocytic reactivity, and neuronal injury assessed by fluorescein isothiocyanate (FITC)-dextran, GFAP immunoreactivity, and microtubule associated protein-2 (MAP2) immunoreactivity, respectively, in rats subjected to 2 h of middle cerebral artery occlusion. Three-dimensional quantitative analysis revealed that 2 h of embolic ischemia resulted in a significant (P<0.05) reduction of cerebral microvascular plasma perfusion in the ipsilateral cortex and subcortex. Tissue within the ipsilateral cortex and subcortex with low plasma perfusion exhibited a significant (P<0.05) increase in GFAP immunoreactivity compared with the homologous contralateral tissue. Three-dimensional re-constructed images showed that prominent GFAP immunoreactive astrocytes surrounded large vessels with decreased plasma perfusion in downstream capillaries in the ipsilateral MCA territory when compared to the vessels in the contralateral homologous tissue. Triple fluorescence probe-stained sections showed that tissue with decreased plasma perfusion and with increased GFAP immunoreactivity was accompanied by a reduction of MAP2 immunoreactivity. The present study demonstrates that an impairment of microvascular perfusion induces an early increase in GFAP immunoreactivity, and reactive astrocytes may contribute to a further reduction of cerebral microvascular plasma perfusion. The three-dimensional quantitative imaging analysis used in the present study provides a means to investigate parenchymal cellular responses to changes of cerebral microvascular plasma perfusion after MCA occlusion.

Registration and warping of magnetic resonance images to histological sections.

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.

Identification of cerebral ischemic lesions in rat using Eigenimage filtered magnetic resonance imaging.

An accurate noninvasive time-independent identification of an ischemic cerebral lesion is an important objective of magnetic resonance imaging (MRI). This study describes a novel application of a multiparameter MRI analysis algorithm, the Eigenimage (EI) filter, to experimental stroke. 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 eigenimage. Rats (n=26) were subjected to permanent middle cerebral artery occlusion. T2- and T1-weighted coronal MRI were acquired on separate groups of animals. The animals were immediately sacrificed after each imaging session for histopathological analysis of tissue at 4-8 h, 16-24 h, and 48-168 h after stroke onset. Lesion areas from MRI were defined using EI. The EI defined lesion areas were coregistered and warped to the corresponding histopathological sections. The ischemic lesion as defined by EI exhibited ischemic cell damage ranging from scattered acute cell damage to pan necrosis. Ischemic cellular damage was not detected in homologous contralateral hemisphere regions. EI lesion areas overlaid on histopathological sections were significantly correlated (r=0.92, p<0.05) acutely, (r=0.98, p<0.05) subacutely, and (r=0.99, p<0.05) chronically. These data indicate that EI methodology can accurately segment ischemic damage after MCA occlusion from 4-168 h after stroke.

MR imaging of human brain at 3.0 T: preliminary report on transverse relaxation rates and relation to estimated iron content.

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.

A 3D deformable surface model for segmentation of objects from volumetric data in medical images.

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.

Time course of ADCw changes in ischemic stroke: beyond the human eye!

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.

Feature space analysis of MRI.

This paper presents an MRI feature-space image-analysis method and its application to brain tumor studies. The proposed method generates a transformed feature space in which the normal tissues (white matter, gray matter, and CSF) become orthonormal. As such, the method is expected to have site-to-site and patient-to-patient consistency, and is useful for identification of tissue types, segmentation of tissues, and quantitative measurements on tissues. The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of gadolinium) were acquired for 10 tumor patients. (2) Images were analyzed by an image analyst according to the proposed algorithm. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image-analysis results were compared with the biopsy results. Pre- and postsurgery feature spaces were also compared. The proposed method made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. The method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- with postsurgery and radiation feature spaces illustrated that the original solid tumor was not present in the second study, but a new tissue component appeared in a different location of the feature space. This tissue could be radiation necrosis generated as a result of radiation.

Segmentation of the hippocampus from brain MRI using deformable contours.

The application of a discrete dynamic contour model for segmentation of the hippocampus from brain MRI has been investigated. Solutions to several common problems of dynamic contours in this case and similar cases have been developed. A new method for extracting the discontinuous boundary of a structure with multiple edges near the structure has been developed. The method is based on detecting and following edges by external forces. The reliability of the final contour and the model stability have been improved by using a continuous mapping of the external energy and limiting movements of the contour. The problem of optimizing the internal force weight has been overcome by making it dependent on the amount of the external force. Finally, the results of applying the proposed algorithm, which implements the above modifications, to multiple applications have been evaluated.

Brain tumor segmentation and characterization by pattern analysis of multispectral NMR images.

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 T1- or T2-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.

Segmentation of acoustic neuromas with magnetic resonance imaging and Eigen image filtering.

OBJECTIVE: This study aimed to determine whether magnetic resonance imaging 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 representing the volumetric growth of a three-dimensional tumor as a linear diameter.

The temporal evolution of MRI tissue signatures after transient middle cerebral artery occlusion in rat.

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 < or = 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.

A multiresolution approach for contour extraction from brain images.

Many image registration methods use head surface, brain surface, or inner/outer surface of the skull to estimate rotation and translation parameters. The inner surface of the skull is also used for intracranial volume segmentation which is considered the first step in segmentation and analysis of brain images. The surface is usually characterized by a set of edge or contour points extracted from cross-sectional images. Automatic extraction of contour points is complicated by discontinuity of edges in the back of the eyes and ears and sometimes by a previous surgery or an inadequate field of view. We have developed an automated method for contour extraction that connects discontinuities using a multiresolution pyramid. Steps of the method are: (1) Contour points are found by an edge-tracking algorithm; (2) A multiresolution pyramid of contour points is constructed; (3) Contour points of reduced images are found; (4) From the continuous contour found at the lowest resolution, contour points at a higher resolution are found; (5) Step 4 is repeated until contour points at the highest resolution (original image) are found. The method runs fast and has been successful in extracting contours from MRI and CT images. We illustrate the method and its performance using MRI and CT images of the human brain.

Cerebral tumor volume calculations using planimetric and eigenimage analysis.

Volume determination in cerebral tumors requires accurate and reproducible segmentation. This task has been traditionally accomplished using planimetric methods which define the boundary of the lesion using thresholding and edge detection schemes. These methods lack accuracy and reproducibility when the contrast between the lesion and surrounding tissue is not maximized. Because of this limitation contrast agents are used providing reproducible results for the enhancing portion of the lesion. A novel approach for volume determination has been developed (eigenimage filter) which segments a desired feature (tissue type) from surrounding undesired features in a sequence of images. This method corrects for partial volume effects and has been shown to provide accurate and reproducible volume determinations. In addition, the eigenimage filter does not require the use of contrast and has the capability to segment a lesion into multiple regions. This allows different components of the lesion to be included and monitored in treatment. In this study planimetric methods and the eigenimage filter were compared for segmenting cerebral tumors and determining their volumes. The planimetric methods were reproducible in determining volumes for the enhancing portion of the lesion with interobserver percent differences < 8% and intraobserver percent differences < 4%. The eigenimage filter had interobserver percent differences < 7% and intraobserver percent differences < 3%. In the eigenimage procedure both the enhancing portion of the lesion as well as additional regions within the lesion were identified. Comparing the results obtained from the two methods demonstrated good agreement for presurgical studies (percent differences < 9%). When comparing postsurgical studies large differences were seen. In the postsurgical studies the eigenimage method allowed multiple regions to be followed in subsequent MRI and in two patients showed a volume change that suggested tumor recurrence more clearly. Since the amount of information obtained using the eigenimage filter may allow a more complete assessment of the lesion, it is suggested that it could improve the clinical evaluation of cerebral tumors.

Optimal linear transformation for MRI feature extraction.

This paper presents development and application of a feature extraction method for magnetic resonance imaging (MRI), without explicit calculation of tissue parameters. A three-dimensional (3-D) feature space representation of the data is generated in which normal tissues are clustered around prespecified target positions and abnormalities are clustered elsewhere. This is accomplished by a linear minimum mean square error transformation of categorical data to target positions. From the 3-D histogram (cluster plot) of the transformed data, clusters are identified and regions of interest (ROI's) for normal and abnormal tissues are defined. These ROI's are used to estimate signature (prototype) vectors for each tissue type which in turn are used to segment the MRI scene. The proposed feature space is compared to those generated by tissue-parameter-weighted images, principal component images, and angle images, demonstrating its superiority for feature extraction and scene segmentation. Its relationship with discriminant analysis is discussed. The method and its performance are illustrated using a computer simulation and MRI images of an egg phantom and a human brain.

Hearing loss in Paget's disease of bone: the relationship between pure-tone thresholds and mineral density of the cochlear capsule.

We have developed a unique method of quantitative computed tomography (QCT) that enables measurement of the density of the cochlear capsule in vivo. We performed pure-tone audiometry and QCT on 67 ears from 35 subjects with radiographically confirmed Paget's disease of the skull and on 40 ears from twenty volunteer subjects. The Pearson product-moment correlation coefficients (age- and sex-adjusted) in the group affected by Paget's disease were -0.63 for left ears and -0.73 for right ears for high-frequency air conduction pure-tone thresholds (mean of 1, 2, and 4 kHz) versus cochlear capsule density. Correlation coefficients (age- and sex-adjusted) between cochlear capsule density and air-bone gap (mean at 0.5 and 1 kHz) for the affected group were -0.67 for left ears and -0.63 for right ears. All correlations between hearing thresholds and cochlear capsule density in pagetic subjects were significant at p < 0.001. The regressions were consistent throughout the ranges of hearing level. There were no significant correlations between cochlear capsule mean density and hearing level in the volunteer subjects. These findings demonstrate the feasibility of precise and accurate density measurements in the temporal bone in vivo and support the use of the mean cochlear capsule density as a marker of disease effect. Alteration of cochlear capsule bone density may be related to the mechanisms of hearing loss in Paget's disease of bone.

A multidimensional nonlinear edge-preserving filter for magnetic resonance image restoration.

The paper presents a multidimensional nonlinear edge-preserving filter for restoration and enhancement of magnetic resonance images (MRI). The filter uses both interframe (parametric or temporal) and intraframe (spatial) information to filter the additive noise from an MRI scene sequence. It combines the approximate maximum likelihood (equivalently, least squares) estimate of the interframe pixels, using MRI signal models, with a trimmed spatial smoothing algorithm, using a Euclidean distance discriminator to preserve partial volume and edge information. (Partial volume information is generated from voxels containing a mixture of different tissues.) Since the filter's structure is parallel, its implementation on a parallel processing computer is straightforward. Details of the filter implementation for a sequence of four multiple spin-echo images is explained, and the effects of filter parameters (neighborhood size and threshold value) on the computation time and performance of the filter is discussed. The filter is applied to MRI simulation and brain studies, serving as a preprocessing procedure for the eigenimage filter. (The eigenimage filter generates a composite image in which a feature of interest is segmented from the surrounding interfering features.) It outperforms conventional pre and post-processing filters, including spatial smoothing, low-pass filtering with a Gaussian kernel, median filtering, and combined vector median with average filtering.