English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The evaluation and treatment of acute cerebral ischemia requires a techniquethat can determine the total area of tissue at risk for infarction usingdiagnostic magnetic resonance imaging (MRI) sequences. Typical MRI data setsconsist of T1- and T2-weighted imaging (T1WI, T2WI) along with advanced MRIparameters of diffusion-weighted imaging (DWI) and perfusion weighted imaging(PWI) methods. Each of these parameters has distinct radiological-pathologicalmeaning. For example, DWI interrogates the movement of water in the tissue andPWI gives an estimate of the blood flow, both are critical measures during theevolution of stroke. In order to integrate these data and give an estimate ofthe tissue at risk or damaged, we have developed advanced machine learningmethods based on unsupervised non-linear dimensionality reduction (NLDR)techniques. NLDR methods are a class of algorithms that uses mathematicallydefined manifolds for statistical sampling of multidimensional classes togenerate a discrimination rule of guaranteed statistical accuracy and they cangenerate a two- or three-dimensional map, which represents the prominentstructures of the data and provides an embedded image of meaningfullow-dimensional structures hidden in their high-dimensional observations. Inthis manuscript, we develop NLDR methods on high dimensional MRI data sets ofpreclinical animals and clinical patients with stroke. On analyzing theperformance of these methods, we observed that there was a high of similaritybetween multiparametric embedded images from NLDR methods and the ADC map andperfusion map. It was also observed that embedded scattergram of abnormal(infarcted or at risk) tissue can be visualized and provides a mechanism forautomatic methods to delineate potential stroke volumes and early tissue atrisk.

Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results