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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 412638, 8 pages
http://dx.doi.org/10.1155/2013/412638
Research Article

Brain Stroke Detection by Microwaves Using Prior Information from Clinical Databases

1Instituto Gregorio Millán, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain
2Departamento de Ciencia e Ingeniería de Materiales e Ingeniería Química, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain

Received 18 December 2012; Revised 22 April 2013; Accepted 24 April 2013

Academic Editor: Bashir Ahmad

Copyright © 2013 Natalia Irishina and Aurora Torrente. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Microwave tomographic imaging is an inexpensive, noninvasive modality of media dielectric properties reconstruction which can be utilized as a screening method in clinical applications such as breast cancer and brain stroke detection. For breast cancer detection, the iterative algorithm of structural inversion with level sets provides well-defined boundaries and incorporates an intrinsic regularization, which permits to discover small lesions. However, in case of brain lesion, the inverse problem is much more difficult due to the skull, which causes low microwave penetration and highly noisy data. In addition, cerebral liquid has dielectric properties similar to those of blood, which makes the inversion more complicated. Nevertheless, the contrast in the conductivity and permittivity values in this situation is significant due to blood high dielectric values compared to those of surrounding grey and white matter tissues. We show that using brain MRI images as prior information about brain's configuration, along with known brain dielectric properties, and the intrinsic regularization by structural inversion, allows successful and rapid stroke detection even in difficult cases. The method has been applied to 2D slices created from a database of 3D real MRI phantom images to effectively detect lesions larger than 2.5 × 10−2 m diameter.