Description: Development, tuning and testing of a software tool that calculates MRI relaxation rates and proton density voxel-wise (QMCI images), and segments most of the brain tissues, including multiple sclerosis lesions. The current version, validated at 1 and 1. 5 Tesla, segments brain studies into the following 18 compartments: Gray Matter (GM); White Matter (WM); demyelinized/abnormal WM (AWM); Cerebrospinal Fluid (CSF); Globus Pallidus; Putamen; Thalamus; Caudate Nucleus; Substantia Nigra; Red Nucleus; Dentate Nucleus; Muscle, Fat; Vitreous humor; Intra-Cranial and Extra-Cranial Connective tissues; Extra-Cranial Fluid; and Low Proton Density tissue. The software produces volumetric and relaxometric information of each compartment. This model based segmentation uses relaxation rates, proton density and anatomical information. The software is configurable and can be optimized in various environmental situations as different scanner characteristics and different anatomical regions (see also Relaxometry based MRI limb segmentation). A module for automatic follow-up evaluation was also developed: it aligns basal and follow-up studies with rigid coregistration (normalized mutual information based of the SPM software), cuts out tissues not common to all studies and perform volumes and rates Pearson's analisys. Application to neurological research: Aging; Alzheimer Disease; Schizophrenia; Degenerative disorders; Partial volume effect correction for PET and SPECT. The improvements currently in testing phase include: 3T brain studies segmentation; Receiving and Transmission MRI inhomogeneity correction (critical for 3T studies); Automatic real flip angle estimation; Registration of misaligned semi-series. Affine registration of basal and follow-up to take into account MR gradient derive; Improved anatomical and signal model for brain studies. Planned future improvements are: Fast Spin Echo support; T1w-like 3D FFE support; Other 3D fast sequences for rates maps; Further improved anatomical and signal model for brain studies; Iterative segmentation.