Algoritmo do gradiente aplicado a amostragem compressiva em imagens de ressonância magnética para tratamento de epilepsia

Abstract

Epilepsies are serious and common diseases of the brain that, in general, can be treated with medicine intake. Modern medicine and its imaging equipment are useful for determining the kind of epilepsy and ways to deal with it. Focal epilepsies are focused on an area of the brain, and the equipment used for prognosis of the area is magnetic resonance imaging. MRI exams are done when it is not possible to treat epilepsy with medication, and so surgery is needed to deal with the area where the disease happens. MRI has limits in its measurement speed to get an image. Those limits are related to the necessary hardware for magnetic resonance and the body’s response to changing magnetic fields. Nonetheless, one can use software (and maybe another implementation of hardware) to speed up the measurement process so the exam time is lower. This possibility comes from a branch in information theory called compressive sensing, where the goal is to directly measure the information’s compacted structure and recover the sparse signal. The study focus of this paper is the compressive sensing’s reconstruction algorithm of the sparse signal, such that it processes magnetic resonance images, and shows another possibility for a reconstruction algorithm for compressive sensing.