BACKGROUND AND PURPOSE: The quantitative nature of CT should make it amenab
le to semiautomated analysis using modern neuroimaging methods. The purpose
of this study was to begin to develop automated methods of analysis of CT
scans to identify putative hypodensity within the lentiform nucleus and ins
ula in patients with acute middle cerebral artery stroke.
METHODS: Thirty-five CT scans were retrospectively selected from our CT arc
hive (scans of 20 normal control participants and 15 patients presenting wi
th acute middle cerebral artery stroke symptoms). The DICOM data for each p
articipant were interpolated to a single volume, scalp stripped, normalized
to a standard atlas, and segmented into anatomic regions. Voxel densities
in the lentiform nucleus and insula were compared with the contralateral si
de at P < .01 using the Wilcoxon two-sample rank sum statistic, corrected f
or spatial autocorrelation.
RESULTS: The quality of the registration for the anatomic regions was excel
lent. The control group had two false-positive results. The patient group h
ad two false-negative results in the lentiform nucleus, two false-negative
results in the insular cortex, and one false-positive finding for the insul
ar cortex. The remainder of the infarcts were correctly identified. The ori
ginal clinical reading, performed at the time of presentation, produced fiv
e false-negative interpretations for the patient group, all of which were c
orrectly identified by the automated algorithm.
CONCLUSION: We present an automated method for identifying potential areas
of acute ischemia on CT scans. This approach can be extended to other brain
regions and vascular territories and may aid in the interpretation of CT s
cans in cases of hyperacute stroke.