Abstract
Early and accurate prediction of tissue outcome is essential to the clinical decision-making process in acute ischemic stroke. We present a quantitative predictive model that combines tissue information available immediately after onset, measured using fluid attenuated inversion recovery (FLAIR), with multi-modal perfusion features (Tmax, MTT, and TTP) to infer the likely outcome of the tissue. A key component is the use of randomly extracted, overlapping, cuboids (i.e. rectangular volumes) whose size is automatically determined during learning. The prediction problem is formalized into a nonlinear spectral regression framework where the inputs are the local, multi-modal cuboids extracted from FLAIR and perfusion images at onset, and where the output is the local FLAIR intensity of the tissue 4 days after intervention. Experiments on 7 stroke patients demonstrate the effectiveness of our approach in predicting tissue fate and its superiority to linear models that are conventionally used.
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Scalzo, F., Hao, Q., Alger, J.R., Hu, X., Liebeskind, D.S. (2010). Tissue Fate Prediction in Acute Ischemic Stroke Using Cuboid Models. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_29
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DOI: https://doi.org/10.1007/978-3-642-17274-8_29
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