From the Departments of Neurology (V.N., K.M.A.W., S.R.L., L.D'O.,
M.D.B.) and Diagnostic Radiology (J.P.W., S.P., D.H., D.P., K.R., H.S.-Z.),
Nuclear Magnetic Resonance and Stroke Research Centers, Henry Ford Health
Sciences Center, Detroit Campus of Case Western Reserve University, Detroit,
Mich; and Department of Electrical and Computer Engineering, University of
Tehran, Tehran, Iran (H.S.-Z.).
Correspondence to Dr K.M.A Welch, NMR Research Center, Department of Neurology (K-11), Henry Ford Hospital, 2799 W Grand Blvd, Detroit, MI 48202-2689. E-mail cwmru{at}neuro.hfh.edu
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.
Imaging Methods
Image Analysis
Tissue was segmented with the use of an ISODATA clustering
algorithm.7 ISODATA is a robust segmentation
algorithm that has the ability to self-adjust the number of
clusters.10 It is an unsupervised clustering
algorithm based on techniques of multivariate
statistical analysis in which cluster centers are iteratively
determined sample means. In addition, our algorithm includes a set of
merging and splitting procedures.7 Image
segmentation is based on both the spatial and feature domain properties
of the MRI data. The spatial domain properties include the relationship
between a pixel and its neighbor, for example, connectivity of pixels
with similar gray levels or distance from the closest pixel with the
same gray level. The feature domain properties include those of the
image gray level distribution. In this algorithm, the euclidean
distance in feature space between tissue patterns is used as a measure
of their dissimilarity. To prepare the data for the clustering
algorithm, a feature vector is constructed at each spatial location
from the set of input data. The number of MR images per slice
determines the dimension of the feature space in which the clusters are
formed. The ISODATA algorithm identifies cluster centers in the
multidimensional feature space (here, 4-dimensional feature space
defined by the 4 DW images) and then classifies pixels to the closest
cluster center. There is no specific shape associated with clusters in
the feature space; they may have arbitrary shapes. If desired, the
operator may generate a visualization of the partitioned feature space
to examine cluster shapes (Figure 1d
The resulting segmented regions were classified into normal (white
matter, gray matter, and cerebrospinal fluid) and abnormal tissue
(zones of stroke and partial volumes) by superimposing the clusters on
the highest DWI and visually examining the location of the cluster (an
example is illustrated in Figure 1d
An ADCw map of each slice was generated from the
4 sets of DW images. The ADCw was calculated on a
pixel-by-pixel basis on the basis of the Stejskal and Tanner
equation11
Interoperator reliability measurements were performed; results of
the ISODATA segmentation were identical, and the differences in
the ADCw values were <1%. Also,
ADCw maps were evaluated with the use of (1)
unprocessed images and (2) processed (noise-filtered) images; the
values obtained from the unprocessed and processed images for
both normal tissue and ischemic regions had <1%
difference.
Data Analysis
Statistical Analysis
Further evidence of ADCw
heterogeneity was obtained when multiple zones were
segmented within the ISODATA isolated ischemic lesion in all 9
patients. The following figures were chosen to illustrate patterns of
segmentation observed in different patients. Figure 1a
Figure 2
Our present computer analysis eliminated user bias.
ISODATA automatically isolated normal and abnormal clusters. There are
no potential artifacts or weaknesses associated with the clustering
algorithm, assuming that (1) data are free of artifacts, (2) the
parameters for the algorithm are set appropriately, and (3)
the results are interpreted correctly. To ensure that these conditions
were met, images with motion artifacts or any nonuniformity were not
used in the analysis, and noise in the images was suppressed.
Methods were developed for optimal selection of the
parameters of the algorithm, and the operator was trained
to understand the mathematical basis of the algorithm and its behavior
through simulation and phantom studies for which the truth was
known.7 We used this procedure to isolate total
ischemic volumes.
When we calculated the average ADCw from each
patient and the mean and SD of these averages from the total group of
stroke patients studied within 10 hours, the ADCw
was significantly reduced compared with nonischemic brain.
Despite this, using ISODATA analysis of ischemic to
contralateral noninvolved homologous tissue ADCw
ratios, we showed that pseudonormal and high ADCw
values were segmented throughout the ischemic volumes. Thus,
averaging the values in the total lesion volume obscured
heterogeneously distributed regions of pseudonormal or high
ADCw. The ISODATA pixel-by-pixel cluster
analysis and segmentation routine therefore appear critical in
discriminating normal tissue from abnormal ischemic regions and
their heterogeneous distribution, especially in detecting
pseudonormalized ADCw values. Also, accepting
that pseudonormalized and high ADCw values
reflect later stages in the evolution of ischemic cellular
damage, our images reveal that the conventional notion of an infarcted
core with potentially viable ischemic tissue in a penumbral
location does not always pertain to human strokes.
To stage cerebral ischemic damage in clinical stroke
independent of time, we proposed a MR signature model in which
ADCw was combined with T2 and related to the
histopathology of experimental ischemic
infarction.3 The utility of this
multiparametric model was questioned because of perceived
differences in the time course of changes in ADCw
between animal ischemia models and human
strokes.4 Our present results suggest that
the previously reported differences in ADCw
values during the evolution of human ischemic strokes in part
can be explained by difficulty in identifying accurately the
heterogeneous nature of ADCw
abnormalities on DWI, and they should not prohibit the validation of
models that use MR signatures to stage, quantify, and predict
histopathologic damage in ischemic infarcts.
Received February 2, 1998;
revision received May 19, 1998;
accepted June 5, 1998.
© 1998 American Heart Association, Inc.
Original Contributions
Time Course of ADCw Changes in Ischemic Stroke: Beyond the Human Eye!
Abstract
Top
Abstract
Introduction
Subjects and Methods
Results
Discussion
References
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.
Key Words: cerebral ischemia, focal • magnetic resonance imaging • signal processing, computer assisted • stroke, acute
Introduction
Top
Abstract
Introduction
Subjects and Methods
Results
Discussion
References
Recent studies have highlighted the clinical value of
diffusion-weighted imaging (DWI) in stroke
diagnosis1 2 and the potential of the apparent
diffusion coefficient of water (ADCw) measurement
to stage, quantify, and predict histopathologic damage in
ischemic brain infarcts.3 The precise
staging of ischemic stroke evolution and prediction of cell
death at very early times of clinical study with DWI remain
challenging. Using computer-assisted image postprocessing and cluster
analysis, we observed regions of elevated
ADCw in human ischemic stroke within the
first 10 hours after the symptom onset. Other investigators, who
visually identified the ischemic focus and calculated an
average ADCw, reported persistence of low
ADCw values for 4 to 8 days after the onset of
symptoms.1 2 4 These disparate results may be
explained in part by differences in the methods of diffusion imaging,
direction of applied diffusion gradients, and differences in image
processing.5 6 Accordingly, we performed DWI
using methods and equipment that more closely replicated the
experimental measurement conditions of other centers and measured the
orientation-independent trace ADCw. Again, using
image postprocessing and an Iterative Self-Organizing Data
Analysis Technique (ISODATA) clustering
algorithm,7 we report pseudonormalized and
elevated ADCw in regions of acute stroke studied
within 10 hours of the onset. We conclude that visually evaluating
diffusion-weighted (DW) intensity changes may not accurately detect the
heterogeneous nature of ADCw
abnormalities in an acute ischemic stroke, or, accordingly, the
changes in ADCw over time.
Subjects and Methods
Top
Abstract
Introduction
Subjects and Methods
Results
Discussion
References
Clinical Patients
We studied the first 9 patients who presented to
us with acute onset of neurological symptoms and signs of focal
cerebral ischemia and successfully completed the imaging
protocol within 10 hours of onset. Their ages ranged from 45 to 83
years. Six were women and 3 were men. Clinically, 7 patients had middle
cerebral artery (MCA) distribution infarct, 1 patient had anterior
cerebral artery distribution infarct, and 1 had ischemia in the
right inferior cerebellar artery. Four patients had MR
angiography that confirmed occlusion of the MCA, 2 of whom had
associated internal carotid artery occlusion as well. Two patients with
MCA branch territory ischemia were diagnosed with embolic
stroke on the basis of clinical as well as imaging information. In the
other 7, the mechanism of arterial occlusion was considered
thromboembolic, caused by atheromatous
arterial disease with established risk factors. The
patients were studied between 5.5 and 9.25 hours after stroke onset.
All patients or appropriate family member or guardian signed informed
consent. The study was approved by the Institutional Review Board of
the Henry Ford Health Sciences Center.
Diagnostic DW images were acquired on a 1.5-T
GE Signa whole-body scanner, coincident with routine MRI and MR
angiography diagnostic imaging. Transaxial trace DW images
were obtained with a single-shot, spin-echo, echo-planar imaging
sequence. The FDA-approved echo-planar DWI sequence contained
second-order eddy current compensation, which eliminated geometric
distortions from the diffusion gradients. This was verified by
overlaying the outlines (determined from the b=0
s/mm2 image) of both the edges and the internal
structures of the brain onto the DW images from the different b values.
There was no mismatch in the images, thus indicating that eddy currents
did not contribute to any distortion. Four sets (b=900, 600, 300, and 0
s/mm2) of trace DW images were obtained for all
patients. Contiguous 6-mm-thick slices with a 230-mm field of view,
with echo time of 99 ms and 128x128 matrix of the whole brain, were
acquired. The single-shot DW images were obtained sequentially with X,
Y, and Z diffusion weighting and then averaged. The scan time for trace
DWI of the entire head per b value was 32 seconds, and the total scan
time for the 3 sets of b values was 96 seconds. In addition, spin-echo
T2-weighted images were obtained with echo time of 90 ms, repetition
time of 2500 ms, 256x192 matrix, and other imaging
parameters identical to those of DW images.
Fourier-transformed, multiple-source DW MR images were
processed for multispectral segmentation. Before segmentation, all
image data sets were 3-dimensionally coregistered with a head and hat
approach to compensate for patient motion between multiple
scans.8 Next, the intracranial volume was
segmented from the image background and skull by thresholding the
signal intensity from recognized anatomic structures. The image
background was discarded. Subsequently, white noise in the images was
suppressed by multidimensional restoration filtering. For this, we used
a nonlinear edge-preserving filter that maintained average partial
volume information.9 
).
Shape analysis is of interest in itself but beyond the scope of
this report. A flowchart of the algorithm and its explanation are
detailed in Reference 77 . Calculation of ADCw maps
is unnecessary for the ISODATA clustering algorithm; thus, a major
advantage of ISODATA is that it segments the image using all the
discriminating information implicitly available in the data.
View larger version (105K):
[in a new window]
Figure 1. Two representative patients with
acute stroke are illustrated, in whom the hyperintense signal in the DW
images indicates an ischemic lesion. a, DWI (b=900
s/mm2) obtained from a patient with right MCA territory
embolic stroke studied 6.5 hours after ictus. b, A magnified view of
the 2 zones in the ischemic lesion are superimposed on the
outline of the brain that correspond to the image displayed in panel a.
These zones are both characterized by low ADCw. The blue
area is reduced by 50% and the red zone is reduced by 25% compared
with contralateral noninvolved tissue (N), suggesting that injury of
the tissues is evolving at different rates. The trace ADCw
map corresponding to the same region is displayed in panel c. d, Zones
generated by the ISODATA algorithm showing normal tissue (white and
gray matter, Z1 and Z2), zones of stroke (Z3 and Z4), cerebrospinal
fluid (Z5), partial volumes (Z6 and Z7), and noise (Z8). e, DWI (b=900
s/mm2) of a patient with right temporal lobe embolic stroke
studied 5.5 hours after ictus. f, Magnified view of results from the
ISODATA algorithm that uniquely identified a cluster corresponding to
pseudonormal ADCw, shown in red. The
ADCw value in this zone was 102% of noninvolved tissue.
There were also 3 zones of low ADCw in this stroke, shown
in blue, green, and yellow, corresponding to a reduction of 50%, 68%,
and 72%, respectively. g, Trace ADCw map of the region
shown in panel f. 
). Furthermore, the classification
is based on both spatial and feature domain properties of MRI. The
spatial domain properties included the relationship between a region
and its neighbors, ie, size of connected pixels in a region and
connectivity of the regions in 3 dimensions. The feature domain
properties included similarity of the signature vectors associated with
segmented regions (ie, the cluster centers) and the gradient and
texture of the region. Clusters consisting of merely sparse pixels were
assumed to be generated as a result of noise and thus were not
classified as meaningful tissue types. Anatomic knowledge of the human
brain was used to avoid misclassification.
where SI0 is the pixel signal
intensity from image acquired with no diffusion gradients, ie, b=0
s/mm2 image; SI1 is the
pixel signal intensity with diffusion gradients on; and b is the
diffusion sensitive factor that is dependent on the diffusion gradient
strength, the gradient duration, and the diffusion time. The logarithm
of intensity values for each pixel was used in a linear least-squares
fit to obtain the map. The volume of each cluster was determined from
the number of pixels. Regions of interest, corresponding to clusters
comprising the ischemic tissue in each slice, were
projected onto the map to obtain the mean and SD of the
ADCw. The ADCw value of
each cluster was compared and normalized to that of the contralateral
noninvolved homologous region. Normalization eliminated the need for
comparisons with a control group, avoiding intersubject variability,
and minimized variability due to gradient eddy currents. It also
avoided the use of absolute ADCw values because
the accuracy in ADCw determination is inherently
dependent on the range of b values used in the calculation.
The ADCw of contralateral
nonischemic tissue (N) and SD were used to classify the
ISODATA segmented regions of the ischemic foci into 3 groups,
namely, low (L), pseudonormalized (P), and high (H)
ADCw. Clusters of pixels in the ischemic
region were classified as L if the ADCw was
<N-SD, P if >N-SD but <N+SD, and H if >N+SD. Classification was
also performed with the use of 1.5 and 2 SD to define the ranges of L,
P, and H. Only 1 SD of the ADCw of normal tissue
was used in the classification of the ischemic zones so as not
to skew the data in favor of P and H (see Results). The percentage of
L, P, and H of the total lesion volume was calculated. Finally, the
average ADCw of the entire lesion volume was
calculated.
The average ADCw values of normal
and ischemic tissue are presented as mean±SD.
Statistically significant differences in ADCw of
the ischemic tissue were tested against the noninvolved
contralateral tissue with a paired t test; the significance
level was set at P<0.05.
Results
Top
Abstract
Introduction
Subjects and Methods
Results
Discussion
References
In each patient, DW images (b=900 s/mm2) of
the entire brain were reviewed visually. In all 9 patients, the
ischemic region was conspicuous in 
1 of the contiguous slices
of the trace DW images. In the total patient group, the mean of the
average ADCw in ischemic tissue
(626.2±76.8 x10-6
mm2/s) was significantly different
(P<0.0001) from contralateral homologous noninvolved tissue
(842.9±60.4 x10-6
mm2/s) (Table). The
numbers of pixels with high values according to the 1-, 1.5-, and 2-SD
classification were on average within 20% of each other, but there was
an 
50% decrease in the number of pixels with low values (these
pixels shifted to pseudonormal) when changing from the 1-SD to 2-SD
classification. Thus, to avoid skewing the data toward pseudonormal and
high values, the 1-SD classification was used in the analysis.
The table also shows that the percentage of pixels with low
ADCw in individual patients ranged from 56 to 91
with a mean of 72.2, the percentage of pixels with pseudonormal
ADCw ranged from 4 to 33 with a mean of 20.3, and
the percentage of pixels with high ADCw ranged
from 0 to 17 with a mean of 7.5. These results show the
heterogeneity of ADCw values in
the ischemic focus.
View this table:
[in a new window]
Table 1. ADCw Values of Noninvolved Tissue, Volume of
Stroke, and Average ADCw of Stroke
(n=9) to 1d shows
results from a patient with 2 separate zones of low but different
ADCw values within the lesion that presumably
reflected tissue at different stages of ischemic injury. One or
more segmented zones of low but different ADCw
was typical of each patient studied. Figure 1e and 1g are from the
ischemic focus of a different patient and illustrate how image
postprocessing is important to detecting pseudonormalized
ADCw. Figure 1f shows 3 (blue, green, and yellow)
zones of low ADCw and an outer zone (red) of
pseudonormalized ADCw. The zone of pseudonormal
ADCw was uniquely segmented from other brain
regions. Despite having normal ADCw values, the
signal strength of the pseudonormal zone compared with the signal
strength of the contralateral noninvolved normal tissue was not the
same for the 4 sets of DWI of this slice. Thus, this zone had a unique
vector feature in the 4-dimensional feature space that uniquely
distinguished it from normal tissue due to elevated T2 and that enabled
its designation as pseudonormal. The pseudonormal zone was coincident
with regions of hyperintensity on the T2 image (data not shown). provides images from 2
patients, 1 of whom (Figure 2a, 2b, and 2c) exhibited high
ADCw in marginal zones that surrounded a low
ADCw core. In the other patient (Figure 2d, 2e, and 2f), the lateral zone had a low ADCw and the
medial a region of high ADCw. Figure 3 illustrates that low, pseudonormal, and
high ADCw zones often were not confined to a set
pattern but were "jigsawlike" in composition and distribution. For
example, zones of high ADCw were not confined to
the periphery or to the interior of a lesion but randomly distributed
in a pattern not attributable to partial volume effects. As shown in
Figure 3b (the b=0 s/mm2 DWI can be considered a
T2-weighted image), the hyperintense areas were coincident with
pseudonormal and high ADCw values of the
lesion.
View larger version (104K):
[in a new window]
Figure 2. The ADCw
heterogeneity in ischemic brain, not visible to
the human eye, is detected with the use of the ISODATA clustering
algorithm. In general, during the acute stage of ischemia,
regions of low ADCw constitute a major proportion of the
lesion. Nevertheless, there are zones of high ADCw in the
ischemic focus. DW images are displayed in the left panels,
magnified views of ISODATA segmented zones of the lesion superimposed
on the outline of the brain are displayed in center panels, and the
corresponding ADCw maps are shown in the right panels. a,
DWI of a patient with right MCA stroke studied 6.75 hours after ictus.
b, The ischemic region shown in the right panel comprises two
zones; the blue zone confined to the central area of the lesion has an
ADCw that is reduced by 43%, but the red zone shows a 47%
increase. c, Corresponding ADCw map. d, DWI of a
patient with right MCA branch occlusion imaged 8.5 hours after symptoms
began. e, The blue area corresponds to tissue with ADCw
reduction by 21%; the red area has a 68% increase in
ADCw. Accepting that pseudonormalized and high
ADCw values reflect later stages in the evolution
of ischemic cellular damage, images b and e reveal that the
conventional notion of an infarcted core with potentially viable
ischemic tissue in a penumbral location does not always pertain
to human strokes. f, Corresponding ADCw map.
View larger version (126K):
[in a new window]
Figure 3. The study is from a patient with left MCA
occlusion studied 5.5 hours after onset of symptoms. a, DWI (b=900
s/mm2); b, DWI (b=0 s/mm2); c, an enlarged view
of the 3 zones that were segmented corresponding to the lesion. This
typifies lesions in which different zones have low (blue region
constituting 63% of total lesion), pseudonormal (yellow region
constituting 32% of total lesion), and high (red region constituting
5% of total lesion) ADCw. Note the jigsawlike pattern of
segmentation. Zones of pseudonormal and high ADCw were not
confined to the periphery or to the interior of a lesion but were
randomly distributed in a pattern not attributable to partial volume
effects. As shown in panel b (the b=0 s/mm2 DWI can be
considered a T2-weighted image), the hyperintense areas (indicated by
the yellow arrows) were coincident with pseudonormal and high
ADCw values of the lesion. d, Trace ADCw map of
the region corresponding to the area shown in panel c.
Discussion
Top
Abstract
Introduction
Subjects and Methods
Results
Discussion
References
Reports of the time course of ADCw
changes in ischemic brain have differed between
laboratories.1 2 3 4 This may be explained in part
by the differences in the methods of the diffusion imaging and in the
direction of applied diffusion gradients.5
Therefore, in the present study we measured the
orientation-independent trace ADCw with imaging
methods and equipment similar to that used by
others.1 2 For example, we used an echo-planar
imaging sequence and a 1.5-T scanner as opposed to a 3-T magnet. The
mean ADCw value of contralateral noninvolved
tissue obtained from our present study is in good agreement with
the ADCw for control (normal) human brain
(825±170 x10-6
mm2/s).12 Another important
explanation for the discrepancies may lie in visually identifying
ischemic strokes by intensity increases on the DW images and in
calculating its average ADCw and sampling regions
of maximum intensity. Such visual selection may miss regions of
pseudonormalization and be insensitive to regions of low intensity and
correspondingly high ADCw within or outside the
intense focus. Averaging the ADCw values may also
average out pseudonormal or high values. To overcome these limitations,
in past and present studies we performed computer analysis
and segmentation of the total lesion.
Acknowledgments
This research was supported in part by National Institutes of
Health grant PO1 NS23393. The authors thank Ilene Beninson and the
neurology residents of Henry Ford Health System for patient
recruitment, and the radiology department technicians for MR image
acquisition.
References
Top
Abstract
Introduction
Subjects and Methods
Results
Discussion
References
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S. L. Keir and J. M. Wardlaw Systematic Review of Diffusion and Perfusion Imaging in Acute Ischemic Stroke Stroke, November 1, 2000; 31(11): 2723 - 2731. [Abstract] [Full Text] [PDF]
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D. C. Tong, A. Adami, M. E. Moseley, and M. P. Marks Relationship Between Apparent Diffusion Coefficient and Subsequent Hemorrhagic Transformation Following Acute Ischemic Stroke Stroke, October 1, 2000; 31(10): 2378 - 2384. [Abstract] [Full Text] [PDF]
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 T. Brott and J. Bogousslavsky Treatment of Acute Ischemic Stroke N. Engl. J. Med., September 7, 2000; 343(10): 710 - 722. [Full Text] [PDF]
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N. Miyasaka, T. Kuroiwa, F. Y. Zhao, T. Nagaoka, H. Akimoto, I. Yamada, T. Kubota, and T. Aso Cerebral Ischemic Hypoxia: Discrepancy between Apparent Diffusion Coefficients and Histologic Changes in Rats Radiology, April 1, 2000; 215(1): 199 - 204. [Abstract] [Full Text]
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P. Mukherjee, M. M. Bahn, R. C. McKinstry, J. S. Shimony, T. S. Cull, E. Akbudak, A. Z. Snyder, and T. E. Conturo Differences between Gray Matter and White Matter Water Diffusion in Stroke: Diffusion-Tensor MR Imaging in 12 Patients Radiology, April 1, 2000; 215(1): 211 - 220. [Abstract] [Full Text]
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J. R. Carhuapoma, P. Y. Wang, N. J. Beauchamp, P. M. Keyl, D. F. Hanley, and P. B. Barker Diffusion-Weighted MRI and Proton MR Spectroscopic Imaging in the Study of Secondary Neuronal Injury After Intracerebral Hemorrhage Stroke, March 1, 2000; 31(3): 726 - 732. [Abstract] [Full Text] [PDF]
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Q. Yang, B. M. Tress, P. A. Barber, P. M. Desmond, D. G. Darby, R. P. Gerraty, T. Li, and S. M. Davis Serial Study of Apparent Diffusion Coefficient and Anisotropy in Patients With Acute Stroke Stroke, November 1, 1999; 30(11): 2382 - 2390. [Abstract] [Full Text] [PDF]
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G. C. Newman, V. Nagesh, and K.M.A. Welch Time Course of ADCW Changes in Ischemic Stroke • Response Stroke, January 1, 1999; 30(1): 185 - 185. [Full Text] [PDF]
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H.-J. Wittsack, A. Ritzl, G. R. Fink, F. Wenserski, M. Siebler, R. J. Seitz, U. Modder, and H.-J. Freund MR Imaging in Acute Stroke: Diffusion-weighted and Perfusion Imaging Parameters for Predicting Infarct Size Radiology, February 1, 2002; 222(2): 397 - 403. [Abstract] [Full Text] [PDF]
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