Abstract #0016
Prediction of Tissue Recovery in Chronic Stroke Using Adaptive Models and Acute MR Information
Hassan Bagher-Ebadian 1,2 , Marie Luby 3 , James R Ewing 2,4 , Panayiotis Mitsias 4 , and Hamid Soltanian-Zadeh 1,5
1
Radiology, Henry Ford Hospital, Detroit, MI,
United States,
2
Physics,
Oakland University, Rochester, MI, United States,
3
National
Institute of Neurological Disorders and Stroke, MD,
United States,
4
Neurology,
Henry Ford Hospital, Detroit, MI, United States,
5
CIPCE,
ECE Dept., University of Tehran, Tehran, Iran
This pilot study introduces four different
adaptive-models (the inelastic-collision (IC) model, the
Kohonen-Multi-Parametric-Self-Organizing-Map (KMP-SOM),
the Generalized-Linear-Model (GLM) and an
Artificial-Neural-Network) for multi-parametric
analysis. These models are applied on acute MR
information of eleven treatment-nave patients to
predict tissue recovery in chronic stroke. All patients
presenting with acute neurological deficit consistent
with stroke, and had MRI studies done within 24h of
onset. Results imply that adaptive models are capable of
identifying the ischemic growth (in pattern and size),
and may describe tissue viability. Thus adaptive models
can play important role in the assessment of acute and
sub-acute therapeutic interventions of stroke.
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