We aim to provide a next generation Magnetic Resonance Imaging (MRI) technology with an integrated solution for reducing motion artifacts in brain imaging applications. New developments in the field of MRI are revolutionizing the diagnostic capabilities e.g. of functional (fMRI) of the technique. Unfortunately, motion artifacts are eminent problems in cerebral MRI images, especially in difficult patient populations (e.g. chronic pain, children, neonates). Patient motion artifacts are present in 2D sequences, but are extremely detrimental in multi-slice 3D sequences often employed in fMRI. The problem of motion compensation in MRI technology deals with:

• Identification of the source as well as pattern of motion.

• Obtaining a mathematical model of motion that can be used to identify and then compensate the motion effects.

• Optimizing the image acquisition sequence in order to minimize, or even eliminate, the effect of motion.

We propose a method to obtain a quantitative measure of the movement of the head between different data acquisition points in both MRI, and functional MRI examination.