Multiparametric MRI tissue characterization in clinical stroke with correlation to clinical outcome: part 2.
OBJECTIVE
Multiparametric MRI generates different zones within the lesion that may reflect heterogeneity of tissue damage in cerebral ischemia. This study presents the application of a novel model of tissue characterization based on an angular separation between tissues obtained with the use of an objective (unsupervised) computer segmentation algorithm implementing a modified version of the Iterative Self-Organizing Data Analysis Technique (ISODATA). We test the utility of this model to identify ischemic tissue in clinical stroke.


METHODS
MR parameters diffusion-, T2-, and T1-weighted imaging (DWI, T2WI, and T1WI, respectively) were obtained from 10 patients at 3 time points (30 studies) after stroke: acute (</=12 hours), subacute (3 to 5 days), and chronic (3 months). The National Institutes of Health Stroke Scale (NIHSS) was measured, and volumes were obtained from the ISODATA, DWI, and T2WI maps on patients at each time point.


RESULTS
The acute (</=12 hours) multiparametric ISODATA volume was significantly correlated with the acute (</=12 hours) DWI (r=0.96, P<0.05; n=10) and chronic (3 months) T2WI volume (r=0.69, P<0.05; n=10). The ISODATA-defined tissue regions exhibited MR indices consistent with ischemic and/or infarcted tissue at each time point. The acute (</=12 hours) multiparametric ISODATA volumes were significantly correlated (r=0.82, P<0.009; n=10) with the final NIHSS score. In comparison, the acute (</=12 hours) DWI volumes were less correlated (r=0.77, P<0.05; n=10) and T2WI volume (</=12h) exhibited a marginal correlation (r=0.66, P<0.05; n=10) with the final NIHSS score.


CONCLUSIONS
The integrated ISODATA approach to tissue segmentation and classification discriminated abnormal from normal tissue at each time point. The ISODATA volume was significantly correlated with the current MR standards used in the clinical setting and the 3-month clinical status of the patient.
Jacobs
M A
MA
Departments of Neurology, Medical Image Analysis Research, Henry Ford Health Sciences Center, Detroit, Michigan, USA.
Mitsias
P
P
Soltanian-Zadeh
H
H
Santhakumar
S
S
Ghanei
A
A
Hammond
R
R
Peck
D J
DJ
Chopp
M
M
Patel
S
S
D000328:Adult; D000368:Aged; D000465:Algorithms; D018450:Disease Progression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D020521:Stroke / Q000145:classification* / Q000175:diagnosis*