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Functional Magnetic Resonance Imaging (fMRI) has become one of the most important techniques to understand the brain activity related to a specific task or a sensory process. fMRI is a medical imaging modality that makes it possible to identify the active areas in the brain, regions with increased neuronal activity as a result of a sensorial stimulus or due to a task predefined in the paradigm of the functional experiment. The most important feature of this medical imaging modality is to map cerebral functions without necessity of radioactive isotopes injection or use of ionising radiation. Due to low signal to noise ratio, elaborate processing techniques are necessary to analyse functional images. Statistical techniques are usually applied on the temporal series obtained from fMRI, resulting in a Statistical Parametric Map (SPM), a 3-D image that makes it possible to determine the state of a voxel, as activated or not activated, and the statistical significance of the result. In this work we study the main statistical methods to analyse fMRI temporal series to detect activated regions and to characterise the error involved in this decision. We proposed a new, simple an elegant method based on Robust Anisotropic Diffusion (RAD) to exploit the spatial correlation of activated regions of the human brain. The new method, named Robust Anisotropic Diffusion of Statistical Parametric Maps (RADSPM), allows one to obtain statistical maps that improve the determination of activated areas from noisy fMRI data. The new parametric statistical maps, based on the voxel spatial correlation of the fMRI image, reduce the classification error thus improving the significance of the results. We have tested the new technique in both simulated and real fMRI, obtaining sharp and noiseless SPMs with increased statistical significance. We compare results of the new RADSPM method with those of a classic method, the conventional correlation method. We calculate the involved errors and we present Receiver Operating Characteristics (ROC) curves for both methods, comparing the most important parameters for simulated fMRI data. We also evaluate the new method on real data of a visual fMRI experiment.

Ressonância magnética funcional com filtragem pela difusão anisotrópica robusta.