  The evaluation and treatment of acute cerebral ischemia requires a technique
that can determine the total area of tissue at risk for infarction using
diagnostic magnetic resonance imaging (MRI) sequences. Typical MRI data sets
consist of T1- and T2-weighted imaging (T1WI, T2WI) along with advanced MRI
parameters of diffusion-weighted imaging (DWI) and perfusion weighted imaging
(PWI) methods. Each of these parameters has distinct radiological-pathological
meaning. For example, DWI interrogates the movement of water in the tissue and
PWI gives an estimate of the blood flow, both are critical measures during the
evolution of stroke. In order to integrate these data and give an estimate of
the tissue at risk or damaged, we have developed advanced machine learning
methods based on unsupervised non-linear dimensionality reduction (NLDR)
techniques. NLDR methods are a class of algorithms that uses mathematically
defined manifolds for statistical sampling of multidimensional classes to
generate a discrimination rule of guaranteed statistical accuracy and they can
generate a two- or three-dimensional map, which represents the prominent
structures of the data and provides an embedded image of meaningful
low-dimensional structures hidden in their high-dimensional observations. In
this manuscript, we develop NLDR methods on high dimensional MRI data sets of
preclinical animals and clinical patients with stroke. On analyzing the
performance of these methods, we observed that there was a high of similarity
between multiparametric embedded images from NLDR methods and the ADC map and
perfusion map. It was also observed that embedded scattergram of abnormal
(infarcted or at risk) tissue can be visualized and provides a mechanism for
automatic methods to delineate potential stroke volumes and early tissue at
risk.
