Application of neural networks in detection of abnormal brain perfusion regions

• We proposed an algorithm for computed tomography perfusion maps analysis.
• Application of self-organizing maps in early process of image processing has increased recognition rate.
• The new approach reduced false positive rate by nearly 10%.
• The use of neural network enables not only to recognize the localization and type but also to quantify the uncertainty of automatic diagnosis.

In this paper we modify the existing image processing schema for computed tomography perfusion (CTP) maps analysis in order to increase its efficiency in detection and classification of perfusion abnormalities by adding multilayer perceptron neural network diagnostic module and new neural network based image processing procedure. The main new contribution of this paper is description of self-organizing Kohonen's map (SOM) architecture that is used in the image processing step. CTP is based on generation of time series images that shows the flow of contrast material in vascular system of the brain. Despite the fact that SOM was previously reported as a reliable tool for time series classification we decided to utilize it as the extension of our existing methodology. We also present methodology of CTP – based diagnostic system for brain stroke diagnosis updated with our latest results and never before presented validation results. Our proposition is consisted of two steps: initial image processing after which potential asymmetry regions between hemispheres are detected and the second step during which position, type and prognostic map for potentially infarcted tissues are generated. We also made comparison of efficiency of our new approach with existing one. The test set for our algorithm validation was consisted of 75 CTP images triplets (one cerebral blood flow, cerebral blood volume and CT in each) from 30 patients (both man and woman) with suspicion of ischemia/stroke. The maps were previously diagnosed by an expert. Forty two CTP maps were described as normal (no abnormalities), 33 maps showed syndromes of ischemic strokes with various severities. Finally over 82% of CTP maps from our test set were rightly segmented and classified.