(Stroke. 2001;32:943.)
© 2001 American Heart Association, Inc.
Original Contributions |
A Model for Multiparametric MRI Tissue Characterization in Experimental Cerebral Ischemia With Histological Validation in Rat
Part 1
Presented and published in part at the 25th International Stroke Conference, New Orleans, La, February 10–12, 2000.
From the Departments of Neurology (M.A.J., Z.G.Z., R.A.K., A.V.G., M.C.) and Radiology, Medical Image Analysis Research (M.A.J., H.S-Z., D.J.P.), Henry Ford Health Sciences Center, Detroit, Mich; Department of Electrical and Computer Engineering, University of Tehran (Iran) (H.S-Z.); Department of Physics, Oakland University, Rochester, Mich (M.A.J., M.C.); and Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Md (M.A.J.).
Correspondence to Michael Chopp, PhD, Department of Neurology, Center for Stroke Research, Henry Ford Hospital E&R 3056, 2799 W Grand Blvd, Detroit, MI 48202. E-mail chopp{at}neuro.hfh.edu
 |
Abstract |
|---|
 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Background and Purpose—After stroke, brain tissue undergoes time-dependent heterogeneous histopathological change. These tissue alterations have MRI characteristics that allow segmentation of ischemic from nonischemic tissue. Moreover, MRI segmentation generates different zones within the lesion that may reflect heterogeneity of tissue damage.
Methods—A vector tissue signature model is presented that uses multiparametric MRI for segmentation and characterization of tissue. An objective (unsupervised) computer segmentation algorithm was incorporated into this model with the use of a modified version of the Iterative Self-Organizing Data Analysis Technique (ISODATA). The ability of the model to characterize ischemic tissue after permanent middle cerebral ischemia occlusion in the rat was tested. Multiparametric ISODATA measurements of the ischemic tissue were compared with quantitative histological characterization of the tissue from 4 hours to 1 week after stroke.
Results—The ISODATA segmentation of tissue identified a gradation of cerebral tissue damage at all time points after stroke. The histological scoring of ischemic tissue from 4 hours to 1 week after stroke on all the animals was significantly correlated with ISODATA segmentation (r=0.78, P<0.001; n=20) when a multiparametric (T2-, T1-, diffusion-weighted imaging) data set was used, less correlated (r=0.70, P<0.01; n=20) when a T2- and T1-weighted data set was used, and not correlated (r=-0.12, P>0.47; n=20) when only a diffusion-weighted imaging data set was used.
Conclusions—Our data indicate that an integrated set of MRI parameters can distinguish and stage ischemic tissue damage in an objective manner.
Key Words: cerebral ischemia, focal • computer-assisted image processing • diffusion imaging • magnetic resonance imaging • signal processing, computer assisted, ISODATA • stroke, acute • stroke classification • tissue signature
|
Introduction |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
MRI parameters within an ischemic lesion are time dependent and heterogeneous, and it is unlikely that a single MR parameter can characterize the complexity of cerebral tissue.1 2 3 4 5 In clinical practice, diffusion-, T2-, and T1-weighted images (DWI, T2WI, T1WI, respectively) are acquired during the progression of stroke at most medical centers. An integration of these MRI data may provide complementary information about the status of the tissue.
MRI generates multiparametric data because of its unique ability to form images influenced by different types of tissue parameters (ie, proton density–weighted imaging, T2WI, T1WI, and DWI).6 In addition, by using these MR images, an n-dimensional feature space can be constructed by plotting each MR image on a separate axis.7 In particular, unsupervised segmentation methods, such as K-means and fuzzy c-means, can be applied to multiparametric MRI data for exploratory clustering and analysis. However, the difficulty with K-means and fuzzy c-means segmentation methodologies is that the number of tissue clusters should be known a priori.7 In practice, the number of tissue clusters is not usually known during the evolution of cerebral ischemia because the ischemic tissue damage is heterogeneous and time dependent.3 4 8 These challenges require an algorithm that can adjust the number of tissue clusters in an iterative fashion and incorporate multiple MRI parameters.
To address the difficulty of characterizing ischemic brain tissue damage independent of time, a modification of a previously reported tissue signature model for classifying ischemic tissue damage was implemented.2 3 This supervised tissue signature model required an operator to define a rectangular box on a scatterplot using the mean and SD of normal tissue from T2 and apparent diffusion coefficient of water (ADC) maps.2 The need for supervision to determine clusters was a drawback of the model, and validation of this model was incomplete.
We have developed an unsupervised segmentation algorithm that incorporates multiparametric MRI.8 9 The algorithm is based on an Iterative Self-Organizing Data Analysis Technique, commonly referred to as ISODATA.10 ISODATA is an iterative, multistep process that assigns the input data into a set of clusters. This study presents histological validation of a novel vector signature model based on ISODATA segmentation of abnormal from normal tissue.
|
Materials and Methods |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Theory
Angle Model Classification of Tissue Clusters
We hypothesized that different tissue classes in brain, such as white matter, gray matter, cerebrospinal fluid, and abnormal tissue, will exhibit distinct characteristic MR signal intensities and that these different MR signals can provide a way to classify the tissue. Therefore, to classify ischemia in tissue, a novel model of tissue signatures was developed on the basis of the angular separation of abnormal tissue from normal tissue by the use of vectors.8 The tissue signature vector is a mathematical descriptor to identify and/or classify tissue types in n-dimensional feature space created from the multiparametric MRI data, as described below. The tissue signature vector is defined as follows:
 | (1) |
. Each of the MR images represents a
dimension in feature space, and in our study a 5-dimensional feature
space is used for the ISODATA model. A representation of tissue
signature vectors defining different tissues is demonstrated in
Figure 2A. The corresponding ISODATA theme map
(Figure 2B) is constructed from tissue clusters and tissue
signature vectors that represent the centroid for each tissue
cluster (see below).
|
|
A feature space or variable space can be
constructed by plotting the signal intensity of each MR image on a
separate axis, which, in other terms, is a multidimensional histogram
of the MR image signal intensity. To demonstrate the concept of feature
space and different tissue clusters created from 3 MR images with the
angular separation between the tissue clusters, a
representation of a 3-dimensional feature space is shown in
Figure 2C
using only T2WI, T1WI, and DWI as input
parameters. Using the distribution of the tissue clusters
in feature space defined by ISODATA, a tissue signature vector is
created for each cluster, allowing the different tissue signature
vectors to be tested for similarity. We hypothesize that if tissue
clusters are close together in the feature space, then their
coordinates and measurements will be similar, and the angles between
tissue signature vectors will be small. On the other hand, if the
clusters are far apart in the feature space, this would imply that the
measurements are different, and the angular separation will be
large.
Within the context of cerebral ischemia, in regard to tissue that has undergone less severe ischemic damage, (1) the tissue will exhibit less histological damage and a low histological score (see below), and we hypothesize that (2) the tissue will have a small or no difference in angle from the normal tissue. As the severity of ischemia increases, there is a corresponding increase in histological (ie, morphological) damage to the tissue, as indicated by both the angular separation between tissue clusters and histological scoring.
The ISODATA Technique
The ISODATA technique is an unsupervised segmentation
method related to the K-means
algorithm10 and has the
ability to adjust the number of clusters. The main advantage of the
ISODATA method is that it requires no initial training or a priori
knowledge of the exact number of tissue classes before
segmentation.
ISODATA Parameter Selection for
Experimental Focal Ischemia in Rat
For experimental stroke studies using small animals,
parameters are initialized as follows. We set the number of
initial clusters (K) to K=15 and desired clusters to K=5. Note that
this number (K=5 for desired clusters) will be automatically adjusted
by ISODATA if the algorithm determines that the number of desired
clusters is inadequate to represent the structure of the data.
Other parameters are as follows: the minimum number of
pixels in a cluster: 
N=5; the SD of gray
matter: S (in rat, gray matter is the
predominant tissue); the Euclidean distance between normal tissue in
brain: C, ie, white and gray matter; the
maximum number of cluster pairs to lump together in 1 iteration: L=1;
the maximum number of iterations: I=100; and the cluster center
constant:
j=0.25
jmax, where
jmax is the maximum element for each SD
vector of the cluster.
The ISODATA Algorithm
Our modified ISODATA algorithm consisted of the
following steps (there are a total of 14 steps in the ISODATA
algorithm; for a complete description, see Jacobs et
al8 ). (1) The clustering
parameters are inputted into the program. (2) The MRI data
are partitioned into random clusters. (3) Cluster centers are
determined for each cluster. (4) Intra-Euclidean distances (InAD) are
calculated on a pixel-by-pixel basis between each pixel and its cluster
center. (5) Inter-Euclidean distances (IED) are calculated between
each of the cluster centers. (6) Splitting and merging of clusters are
performed on the basis of InAD and IED. Clusters that have a large InAD
are split, ie, the cluster has a large SD. Clusters the have a small
IED are merged. (7) Steps 5 and 6 are repeated until the algorithm
converges or reaches the maximum number of iterations allowed.
Convergence is defined as the minimization of the variance of the
clusters between iterations.
After convergence is reached, the tissue clusters are formed
into a theme map
(Figure 2B). A theme map is a color-coded composite image
that reflects the different types of tissue classes that were segmented
from the data set.
With the use of the ISODATA tissue clusters within the
ischemic hemisphere, abnormal tissue signature vectors are
constructed. A normal tissue signature vector, ie, the reference
vector, representing the corresponding normal tissue in the
contralateral hemisphere, is also constructed. Then the angular
separation between each tissue signature vector is calculated with the
use of the inner product between vectors; this is demonstrated in
Figure 2C. The angle measurement may provide an index for
comparison with the histological
score.
Experimental Animal Model
All studies were performed in accordance with the
institutional guidelines for animal research under a protocol approved
by the Institutional Care of Experimental Animals Committee. Male
Wistar rats (n=20) weighing 270 to 310 g were anesthetized
with 3.5% halothane and maintained with 0.75% to 1.5% halothane in
70% N2O and 30% O2 with
the use of a face mask. Rectal temperature was controlled at 37°C
with a feedback-regulated water-heating pad. Rats were subjected to
permanent occlusion of the middle cerebral artery (MCA) (n=20) by a
method of intraluminal vascular
occlusion.11 This method
produces a focal infarct in the striatum, ie, caudate putamen and
globus pallidus, that may extend into the
cortex.12 Ischemic
animals were classified on the basis of time of euthanasia after
stroke: acute (4 to 8 hours; n=5), subacute (16 to 24 hours; n=9),
and chronic (48 to 168 hours; n=6).
MRI Acquisition
After MCA occlusion, the animal was placed in the
magnet, and MRI data sets (DWI, T2WI, and T1WI) were acquired on a 7-T,
20-cm-bore, superconducting magnet (Magnex Scientific Inc)
interfaced to a SMIS (Surrey Medical Imaging Systems Ltd) console. A
5-cm–internal diameter birdcage radio frequency coil and 12-cm-bore
actively shielded gradient coil set capable of producing magnetic field
gradients up to 20 G/cm were used. The head of the animal was secured
with stereotaxic ear bars to reduce motion during the
experiment. Once the animal was placed inside the magnet, 2 orthogonal
interleaved fast low-angle shot (FLASH) images (coronal and sagittal
planes) were acquired for accurate positioning of the animal, as
previously
described.13
During MRI measurements, anesthesia was maintained with 0.75% to 1.5% halothane in a 70% N2O and 30% O2 gas mixture. Rectal temperature was monitored and controlled with a feedback-controlled water bath. MRI studies were performed on animals after MCA occlusion at acute, subacute, and chronic postischemia time points.
Multislice (2-mm-thick contiguous slices) DWI, T2WI, and T1WI were obtained with a 128x128 image matrix with a 32-mm field of view. DWI were acquired by the pulsed gradient, spin-echo method described by Le Bihan et al,14 with repetition time/echo time (TR/TE)=1500/40 ms and diffusion-weighted gradients (7 slices, with incremented b values of 0, 200, 400, 600, and 800 s/mm2; number of excitations [NEX]=2) applied along the z axis. T2WI were acquired by a multiecho sequence (7 slices; TR/TE=3000/30, 60, 90, and 120 ms; NEX=1), and a T1WI inversion recovery image (5 slices; TR/TE=6000/30 ms; inversion time=750 ms; NEX=1) was also obtained.
MRI Image Preprocessing and
Analysis
MR image analysis was performed with a SUN
UltraSPARC2 workstation (Sun Microsystems Inc). All MR images were
reconstructed with a 128x128 matrix with the use of in-house software
and subsequently processed with Eigentool image analysis
software.15 16
Preprocessing on the MRI data consisted of several steps that included subimaging, inhomogeneity correction, and noise reduction. Subimaging of the intracranial volume was done to segment the image background, skull, and scalp from brain tissue.17 After subimaging, an inhomogeneity correction method was applied to the MRI data set.18 Finally, image noise was reduced with the use of a nonlinear restoration filter that reduces white noise while preserving edges and partial volume information.19 Maps of ADC and T2 were created for each time point using a least-squares fit from the slope of the signal intensity on a pixel-by-pixel basis.
MRI Data Analysis
Coregistration and warping of the MRI to histology
were accomplished by a previously reported 2-step
methodology.20 After
coregistration and warping, 3 different sets of MRI image data were
used in the ISODATA algorithm: (1) 5 DWI; (2) 2 T2WI (TE=30, 90 ms),
T1WI, and 2 DWI (b=600, 800
s/mm2); and (3) 4 T2WI and 1 T1WI. These
choices of MR parameters were selected on the basis of
preliminary studies of both laboratory animals and humans demonstrating
that these sets of MR parameters define and characterize
stroke over time. Each of these data sets was used as an input into the
ISODATA algorithm to create 3 different theme maps
(Figure 2B). From each of the theme maps, ISODATA lesion
tissue clusters were defined, and a region of interest (ROI) was
automatically created. The ISODATA ROIs were overlaid onto ADC and T2
maps to obtain quantitative values for each tissue cluster. Similar ROI
analysis was performed on the normal tissue clusters. The
values of the ADC and T2 from the total lesion area within the
ipsilateral hemisphere were normalized to the contralateral hemisphere
and expressed as ratios. Comparison of the ISODATA ROI analysis
with ADC and T2 maps enabled us to address the following question: Does
the angle model segment ischemic and/or infarcted
tissue?
Histopathological Analysis
Tissue Processing
All animals were killed immediately after imaging for
histopathological evaluation. Animals were deeply anesthetized
with ketamine (44 mg/kg) and xylazine (13 mg/kg) by
intraperitoneal injection and were transcardially
perfused with heparinized saline and 10% neutral buffered formalin.
The brain was removed and immersed in the same fixative overnight.
Fourteen coronal blocks of brain tissue were cut at 1-mm intervals with
the use of a rat brain matrix. The tissue was processed and embedded in
paraffin. Paraffin sections from each block (6 µm thick) were cut and
stained with hematoxylin and eosin (H&E) for evaluation of
ischemic cell damage.
Regional Light Microscopy
Analysis
The coregistered/warped ISODATA-defined lesion areas
were overlaid onto the corresponding H&E histological
sections, and 2 to 4 fields of view (392x280
µm2) in the ipsilateral hemisphere and the
homologous areas in the contralateral hemisphere were digitized under a
light microscope with a x40 objective lens (Olympus BX40) with a
charge-coupled device camera (Hitachi RP-111) interfaced with
Global Laboratory image analysis system (Data
Translation).
Each image was analyzed with an MCID image analysis system (Imaging Research), and the coordinates for each image were recorded. A value of the mean gray scale of the entire image and 2 SDs of the mean was used to measure the number of cells and vacuolization. In addition, for measurement of vacuolization, we inverted images to obtain improved gray scale visualization. These values were selected on the basis of our preliminary study, in which we manually counted numbers of cells for each image and then compared the numbers of cells counted by the MCID with several different thresholds of gray scale intensity within the same image. We compared the number of cells obtained from both manual and computer counting of >200 images using the mean gray scale value from the entire image and 2 SDs. Differences in the numbers of cells between these 2 methods were <5%. In addition, 2 blinded observers (Z.G.Z., A.V.G.) measured the number of cells in same image (n=102) with these values, with an interobserver difference of 1.25%. Changes in vacuolization are presented as a percentage of the field, in which areas of vacuoles were divided by total field area.
Histological Grading
Measurements
On the basis of prior studies in our
laboratory,12 21
the criteria for ischemic neuronal damage were the presence of
scalloping at the cytoplasmic border, triangular shrunken neurons
(acute ischemic neuronal damage), and eosinophilic and ghost
neurons (chronic ischemic damage). Because the MCID image
analysis system could not differentiate these morphological
changes, we combined numbers of cells quantified by the MCID with
visual observation of ischemic neuronal damage under the light
microscope on the basis of the criteria outlined above. To reflect the
heterogeneous nature of the ischemic lesion
evolution, we developed a grading scale ranging from 0 to 10 for the
present study, with no neuronal damage scored 0 and the most severe
neuronal damage scored 10. Neuronal shrinkage and morphological
alterations were all considered potentially reversibly damaged tissue
and were scaled as follows: 0, no neuronal damage; 1, <20%; 2, 21%
to 50%; 3, >50%; and 4 to 5, combinations of 2 and 3 such that 1 is
added to the score if <50% reduction in number of total cells is seen
compared with the contralateral hemisphere or 2 is added for >50%
reduction in the number of total cells compared with the contralateral
hemisphere.
Neuronal necrosis, eosinophilia, red neurons, and ghost neurons were considered potentially irreversible damaged tissue, assigned the following scale: 6, <20%; 7, 21% to 50%; 8, >50%; and 9 to 10, combinations of 7 and 8 such that 1 is added to the score if <50% reduction in number of total cells is seen compared with the contralateral hemisphere or 2 is added for >50% reduction in the number of total cells compared with the contralateral hemisphere.
Histological grade accounts for both morphological changes in neurons, characterizing reversible and irreversible neuronal damage, as well as parenchymal cell loss, which encompasses total cell loss of neurons and glia.
Statistical Analysis
Paired
t tests were used to determine
statistical significance between the histological
measurements determined from each ISODATA cluster and corresponding
homologous contralateral regions with the null hypothesis that there is
no association between the histological score and the
angular measurement. Linear regression analysis was performed
to correlate the angle measurements with the
histological grading of the ISODATA-defined tissue
clusters. All parametric map values are presented as
mean±SD. Statistical significance was assigned for
P<0.05.
|
Results |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
The ISODATA-defined lesion revealed ischemic cellular damage localized to the ipsilateral hemisphere. A representative ISODATA theme map at 4 to 8 hours after stroke is shown in Figure 3
. At all time points, the ISODATA theme map had at
least 2 distinct zones within the ischemic hemisphere.
Ischemic cell damage changed over time. After acute (4 to 8
hours) stroke, ischemic cellular changes consisted of shrunken
and triangular neuronal cell bodies with neuronal perikarya and
perineuronal vacuolation
(Figure 3A, 3C, 3E). At the subacute (16 to 24 hours)
time points, ischemic neuronal damage was seen as cytoplasmic
eosinophilia (red neurons) mixed with ischemic neuronal damage,
as described
above.12 21 At
the chronic (48 to 168 hours) time points after cerebral
ischemia, abundant ghost neurons and pannecrosis were noted
within the ISODATA-defined clusters
(Figure 4A and 4C). These histological
findings are consistent with previously reported
histological characteristics of evolving cerebral
ischemic cell
damage.12 21
|
|
Representative ADC and T2 values from the
multiparametric data set (T2WI, T1WI, and DWI) are shown in
Table 1. The values obtained from the overlaid ISODATA
regions onto the ADC and T2 maps were consistent with
previously reported studies of ischemic regions after
stroke.4 22 These
data suggest that the ISODATA changes reflect the evolution of
ischemic brain tissue injury from normal to final
infarction.
|
ISODATA Model Using T2WI, T1WI, and
DWI
Correlations of angle measurements with
histological scoring from the multiparametric
set consisting of 2 T2WI, 1 T1WI, and 2 DWI using the ISODATA model are
summarized in
Table 2. Compared with the DWI and T2WI/T1WI data sets (see
below), the multiparametric data set that combined the T2WI,
T1WI, and DWI exhibited the highest correlation between the angle
measurements and histological scoring
(r=0.78,
P<0.007; n=20). The average
angle between abnormal and normal tissue was 7.8±1.8° (range, 6.1°
to 16.5°), with histological score of 4.2±1.4
(range, 2.0 to 7.0), 4 to 8 hours after stroke. This angular separation
continued to increase to 15.4±8.8° (range, 6.3° to 37.4°), with
histological score of 7.1±2.6 (range, 3.0 to 10.0), at
the chronic time point (48 to 168 hours).
|
ISODATA Model Using DWI
Acutely, the ISODATA model using the DWI data set
exhibited a significant correlation
(r=0.79,
P<0.02) between the angle
measurement model and the histological score of the
tissue. However, no correlation with the histological
score was seen at subacute and chronic time points
(r=-0.05,
P>0.68;
r=-0.04,
P>0.90, respectively).
Overall, the DWI ISODATA angle measurements failed to correlate with
the histological scoring on all animals
(r=0.12,
P>0.47; n=20). These data are
summarized in
Table 2. The average angle between abnormal and normal
tissue was 5.6±2.8° (range, 2.2° to 12.8°), with a
histological score of 3.8±0.48 (range, 2.0 to 7.0) at
4 to 8 hours after ictus. The angular separation then decreased to
2.9±1.7° (range, 0.68° to 6.9°) at the chronic time points, with
corresponding histological scores of 7.2±0.74 (range,
3.0 to 10.0).
ISODATA Model Using T2WI and T1WI
The ISODATA model using 4 T2WI and 1 T1WI showed a
correlation between the angle measurements and
histological scoring
(r=0.70,
P<0.01; n=20;
Table 2). The average angle between abnormal and normal
tissue was similar to that in the multiparametric data set
(data not shown).
|
Discussion |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Using an unsupervised segmentation algorithm, we have developed, tested, and validated a novel MR tissue signature model of ischemic cell damage based on the angular separation between abnormal and normal tissue clusters in a rat model of experimental focal cerebral ischemia. The tissue signature model was developed with the use of tissue signature vectors from different classes of tissues obtained from ISODATA cluster analysis in conjunction with multiparametric MRI data. Histological scoring of ROIs identified from the coregistered/warped segmented ISODATA tissue clusters overlaid onto H&E histological slides was performed to validate the model. The histological score of the ischemic tissue at each time point correlated significantly with the angular separation of abnormal tissue when the multiparametric MR data set was used, suggesting a truly independent measure of the state of the tissue. Note that the tissue clusters are separated in n-dimensional feature space by the Euclidean distance classifier used in ISODATA. This provides an additional criterion to ensure that there is no overlapping of clusters and may be used for further classification of ischemic tissue.
Of the 3 MRI data sets used in the ISODATA segmentation, the multiparametric set that included DWI, T2WI, and T1WI consistently produced better correlation with the histological scoring than the other data sets at each time point. Only the DWI input data set exhibited a higher correlation between the angular separation and the histological score at the acute stage of stroke, but it failed to correlate with the status of the tissue at later time points. DWI measures cytotoxic edema and is very sensitive to the acute changes in tissue water (ie, cytotoxic edema).23 24 During the transition from the subacute to chronic stages, the ADC pseudonormalizes4 and results in a lack of correlation between the DWI ISODATA model and histological scoring.
The findings of this study (1) support the hypothesis that a multiparametric approach is needed to accurately identify and stage ischemic tissue2 3 ; (2) show the versatility of the modified ISODATA algorithm, which incorporates several different MRI parameters to segment multiple tissue classes; and (3) accurately define the state of the tissue in this model of experimental stroke.
The ISODATA model overcomes several limitations of our previously reported tissue signature model.2 3 First, we have eliminated the use of rectangles to define tissue classes by incorporating an unsupervised cluster analysis technique. Cluster analysis can recognize structures within a data set, assuming that there is some type of structure that can be grouped into different tissue classes. In MRI data, this assumption of different classes is well founded, since there is a clear distinction between cerebrospinal fluid and white and gray matter in brain. In addition to the normal tissue classes, pathological disease states can result in additional tissue classes. Second, the number of MRI images needed for identification of the ischemic tissue is arbitrary and can be defined by the user. By increasing the dimensionality of the model, increased separation of different tissues can be realized.25 26 Finally, the ISODATA model provides an objective classification of the tissue clusters independent of time. This is supported by the histological scoring of the tissue clusters coupled with the morphological changes noted within each subregion of the ischemic lesion. The results of this study show the advantages of using multiparametric MRI data to characterize ischemic tissue.
Recent reports26 27 have discussed a multiparametric imaging approach using T2WI, proton density, ADC, and bolus tracking cerebral blood flow estimates incorporated into an unsupervised segmentation methodology in different models of experimental cerebral ischemia with27 and without reperfusion.26 Carano et al26 demonstrated that the unsupervised K-means algorithm outperformed supervised segmentation methodologies, including fuzzy methods for segmentation of ischemic tissue approximately 3 hours after stroke, giving the best classification rate and correlation with 24-hour 2,3,5-tripheyltetrazolium chloride–stained histological sections. Our results from the present study support these findings and emphasize the need for a multiparametric MR approach to characterize the complex evolution of cerebral ischemia. However, there are differences between our multiparametric approach and those reported.
First, ISODATA is related to K-means, but the advantage of ISODATA is that it has additional splitting and merging techniques to adjust the numbers of initial clusters selected in the data.8 9 This adjustment of the number of tissue clusters is important because the number of tissue clusters is not known during the evolution of cerebral ischemia. Second, in this study the animals were imaged and killed at specific time points with the use of H&E staining to identify the histological lesion area and morphological characteristics. Third, coregistration and warping was used to overlay the ISODATA regions onto the H&E histological slides.20 This allowed for a direct mapping between ISODATA and histology. It must be stressed that these reports, coupled with our studies, provide complementary data for the difficult task of characterizing stroke and developing methods to provide automated computer-assisted techniques that can be used in the clinical setting.
The addition of perfusion data into the ISODATA model would likely increase the performance of our ISODATA model in identification and characterization of acute stroke "tissue at risk" and is currently under active investigation. The application of this tissue characterization method to clinical stroke is detailed in a second article (part 2) in this issue of Stroke.28
Conclusion
We have demonstrated that integration of
multiparametric MRI data in the ISODATA angle model provides
useful information about the histological status of
ischemic
tissue.
|
Acknowledgments |
|---|
This work was supported in part by National Institutes of Health grants NS23393 and CA61263. The authors are greatly saddened by the death of Dr Joe P. Windham, who contributed to the initial development of the research effort. The authors greatly appreciate the programming efforts of Lucie Bower, Thomas Brusca, and Dr James Ewing and the secretarial support of Rita Tobey.
Received August 14, 2000; revision received November 28, 2000; accepted December 20, 2000.
|
References |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
- Fisher M, Prichard JW, Warach S. New magnetic resonance techniques for acute ischemic stroke. JAMA. 1995;274:908–911.[Abstract]
- Jiang Q, Chopp M, Zhang ZG, Knight RA, Jacobs MA, Windham JP, Peck D, Ewing JR, Welch KMA. The temporal evolution of MRI tissue signatures after transient middle cerebral artery occlusion in rat. J Neurol Sci. 1997;145:15–23.[Medline] [Order article via Infotrieve]
-
Welch KMA, Windham
JP, Knight RA, Nagesh V, Hugg JW, Jacobs MA, Peck D, Booker P, Dereski
MO, Levine SR. A model to predict the histopathology of human stroke
using diffusion and T2-weighted magnetic resonance imaging.
Stroke. 1995;26:1983–1989.
[Abstract/Free Full Text] - Knight RA, Dereski MO, Helpern JA, Ordidge RJ, Chopp M. Magnetic resonance imaging assessment of evolving focal cerebral ischemia: comparison with histopathology in rats. Stroke. 1994;25:1252–1262.[Abstract]
- Baird AE, Warach S. Magnetic resonance imaging of acute stroke. J Cereb Blood Flow Metab. 1998;18:583–609.[Medline] [Order article via Infotrieve]
-
Vannier MW,
Butterfield RL, Rickman DL, Jordan DM, Murphy WA, Biondetti PR.
Multispectral magnetic resonance image analysis.
Radiology. 1985;154:221–224.
[Abstract/Free Full Text] - Clarke LP, Velthuizen RP, Camacho MA, Heine JJ, Vaidyanathan M, Hall LO, Thatcher RW, Silbiger ML. MRI segmentation: methods and applications. Magn Reson Imaging. 1995;13:343–368.[Medline] [Order article via Infotrieve]
- Jacobs MA, Knight RA, Soltanian-Zadeth H, Zheng ZG, Goussev AV, Peck DJ, Windham JP, Chopp M. Unsupervised segmentation of multiparameter MRI in experimental cerebral ischemia with comparison to T2, diffusion, and ADC MRI parameters and histopathological validation. J Magn Reson Imaging. 2000;11:425–437.[Medline] [Order article via Infotrieve]
- Soltanian-Zadeh H, Windham JP, Robbins L. Semi-supervised segmentation of MRI stroke studies. Proc SPIE. 1997;3034:437–448.
- Ball GH, Hall DJ. ISODATA: A Novel Method of Data Analysis and Pattern Classification. Menlo Park, Calif: Stanford Research Institute; 1965.
-
Zea Longa,
Weinstein PR, Carlson S, Cummins R. Reversible middle cerebral artery
occlusion without craniectomy in rats.
Stroke. 1989;20:84–91.
[Abstract/Free Full Text] - Zhang RL, Chopp M, Chen H, Garcia JH. Temporal profile of ischemic tissue damage, neutrophil response, and vascular plugging following permanent and transient (2H) middle cerebral artery occlusion in the rat. J Neurol Sci. 1994;125:3–10.[Medline] [Order article via Infotrieve]
-
Knight RA,
Ordidge RJ, Helpern JA, Chopp M, Rodolosi LC, Peck D. Temporal
evolution of ischemic damage in rat brain measured by proton
nuclear magnetic resonance imaging.
Stroke. 1991;22:802–808.
[Abstract/Free Full Text] -
Le Bihan D,
Breton E, Lallemand D, Grenier P, Cabanis E, Laval Jeantet M. MR
imaging of intravoxel incoherent motions: application to diffusion and
perfusion in neurologic disorders.
Radiology. 1986;161:401–407.
[Abstract/Free Full Text] - Windham JP, Abd-Allah MA, Reimann DA, Froelich JW, Haggar AM. Eigenimage filtering in MR imaging. J Comput Assist Tomogr. 1988;12:1–9.[Medline] [Order article via Infotrieve]
- Jacobs MA, Knight RA, Windham JP, Zheng ZG, Soltanian-Zadeth H, Goussev AV, Peck DJ, Chopp M. Identification of cerebral ischemic lesions in rat using eigenimage filtered MRI. Brain Res. 1999:1/2:83–94.
- Soltanian-Zadeh H, Windham JP. A multiresolution approach for contour extraction from brain images. Med Phys. 1997;24:1844–1853.[Medline] [Order article via Infotrieve]
- Dawant BM, Zijdenbos AP, Margolin RA. Correction of intensity variations in MRI images for computer-aided tissue classification. IEEE Trans Med Imaging. 1993;4:770–781.
- Soltanian-Zadeh H, Windham JP, Yagle AE. A multidimensional nonlinear edge-preserving filter for magnetic resonance image restoration. IEEE Trans Image Proc. 1995;4:147–161.
- Jacobs MA, Windham JP, Soltanian-Zadeth H, Peck DJ, Knight RA. Registration and warping of magnetic resonance images to histological sections. Med Phys. 1999;26:1568–1578.[Medline] [Order article via Infotrieve]
- Garcia JH, Yoshida Y, Chen H, Li Y, Zhang ZG, Lian J, Chen S, Chopp M. Progression from ischemic injury to infarct following middle cerebral artery occlusion in the rat. Am J Pathol. 1993;142:623–635.[Abstract]
- Mintorovitch J, Moseley ME, Chileuitt L, Shimizu H, Cohen Y, Weinstein PR. Comparison of diffusion and T2 weighted MRI for the early detection of cerebral ischemia and reperfusion in rats. Magn Reson Med. 1991;18:39–50.[Medline] [Order article via Infotrieve]
- Hoehn-Berlage M, Norris DG, Kohno K, Mies G, Leibfritz D, Hossmann K. Evolution of regional changes in apparent diffusion coefficient during focal ischemia of rat brain: the relationship of quantitative diffusion NMR imaging to reduction in cerebral blood flow and metabolic disturbances. J Cereb Blood Flow Metab. 1995;15:1002–1011.[Medline] [Order article via Infotrieve]
-
Minematsu K, Li
L, Fisher M, Sotak CH, Davis MA, Fiandaca MS. Diffusion weighted
magnetic resonance imaging: rapid and quantitative detection of focal
brain ischemia.
Neurology. 1992;42:235–240.
[Abstract/Free Full Text] - Clarke LP, Velthuizen RP, Camacho MA, Heine JJ, Vaidyanathan M, Hall LO, Thatcher RW, Silbiger ML. MRI segmentation: methods and applications. Magn Reson Imaging. 1995;13:343–368.
- Carano RA, Takano K, Helmer KG, Tatlisumak T, Irie K, Petruccelli JD, Fisher M, Sotak CH. Determination of focal ischemic lesion volume in the rat brain using multispectral analysis. J Magn Reson Imaging. 1998;8:1266–1278.[Medline] [Order article via Infotrieve]
- Li F, Carano RA, Irie K, Tatlisumak T, Silva MD, Pschorni U, Takano K, Sotak CH, Fisher M. Neuroprotective effects of a novel broad-spectrum cation channel blocker, LOE 908 MS, on experimental focal ischemia: a multispectral study. J Magn Reson Imaging. 1999;10:138–145.[Medline] [Order article via Infotrieve]
-
Jacobs MA,
Mitsias P, Soltanien-Zadeh H, Santhakumar S, Ghanei A, Hammond R, Peck
DJ, Chopp M, Patel S. Multiparametic MRI tissue characterization in
clinical stroke with correlation to clinical outcome: part 2.
Stroke. 2001;32:950–957.
[Abstract/Free Full Text]
This article has been cited by other articles:
 |
|
 |
|
  P. D. Mitsias, J. R. Ewing, M. Lu, M. M. Khalighi, M. Pasnoor, H. B. Ebadian, Q. Zhao, S. Santhakumar, M. A. Jacobs, N. Papamitsakis, et al. Multiparametric Iterative Self-Organizing MR Imaging Data Analysis Technique for Assessment of Tissue Viability in Acute Cerebral Ischemia AJNR Am. J. Neuroradiol., October 1, 2004; 25(9): 1499 - 1508. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. D. Mitsias, M. A. Jacobs, R. Hammoud, M. Pasnoor, S. Santhakumar, N. I.H. Papamitsakis, H. Soltanian-Zadeh, M. Lu, M. Chopp, and S. C. Patel Multiparametric MRI ISODATA Ischemic Lesion Analysis: Correlation With the Clinical Neurological Deficit and Single-Parameter MRI Techniques Stroke, December 1, 2002; 33(12): 2839 - 2844. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. A. Jacobs, P. Mitsias, H. Soltanian-Zadeh, S. Santhakumar, A. Ghanei, R. Hammond, D. J. Peck, M. Chopp, and S. Patel Multiparametric MRI Tissue Characterization in Clinical Stroke With Correlation to Clinical Outcome : Part 2 Stroke, April 1, 2001; 32(4): 950 - 957. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||