Tissue Fate Prediction in Acute Ischemic Stroke Using Cuboid Models
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.
Keywords
Acute Ischemic Stroke Cerebral Blood Volume Mean Transit Time Flair Image Perfusion Weight ImagePreview
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