Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series.
We performed neural network clustering on dynamic contrast-enhanced perfusion magnetic resonance imaging time-series in patients with and without stroke. Minimal-free-energy vector quantization, self-organizing maps, and fuzzy c-means clustering enabled self-organized data-driven segmentation with respect to fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.
Wismüller
A
A
Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310-6046, USA.
Meyer-Baese
A
A
Lange
O
O
Reiser
M F
MF
Leinsinger
G
G
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000098:blood supply* / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation; D016000:Cluster Analysis; D003287:Contrast Media; D017352:Echo-Planar Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020521:Stroke / Q000175:diagnosis*; D013997:Time Factors
D003287:Contrast Media