Dementing neurodegenerative disorders have more and more prominent public health implications with the most frequent cause being the currently non reversible Alzheimer's Disease (AD). However, over the past 15 years, an abundant literature has demonstrated the particular value of structural MRI from which brain morphological changes can be detected using automated volumetry and morphometry. These techniques offer promise to diagnose AD in early potentially-reversible stages as regional atrophy is currently "the" structural hallmark. Nevertheless, an essential question arises: to what extent are these quantitative measures dependent upon image quality? MRI quality can be mainly affected by machine-specific or patient-related artifacts, with motion being the most significant source of degradations. As we are currently not aware of any technique that reliably captures and corrects patient motion particularly for long high-resolution anatomical scans, rigorous quality assessment is hence of great importance to reliably derive qualitative or quantitative diagnostic information and consists in the first challenge addressed in this thesis. We developed a fully-automated method to classify the image quality based on a single MR image. Quality measures are derived by analyzing the air background of magnitude image and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring and ghosting. The method has been validated on 1670 multi-slice 2D, 3D T1- T2- PD- weighted 1.5T and 3T head scans acquired on Siemens and GE scanners operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the Alzheimer's Disease Neuroimaging Initiative (ADNI) quality control center (taken as the reference standard). The derived quality indices are independent of the MR system used and agree with the reference standard with sensitivity and specificity higher than 90 %. With acceptable computational performance (less than 10 seconds), we incorporated our algorithm in the Siemens scanner software for routine clinical use. As for many other domains, quality criterion does not exhibit all-or-none properties (i.e., a unique quality cutoff value cannot discriminate high- versus low-quality scans) but is modulated by two main aspects addressed in this thesis: acquisition parameters (what kind of image?) and application (what for?). On the one hand, depending on acquisition parameters, more or less strong signal in the object is generated inducing more or less visible artifacts. Therefore, our quality criterion has to be adjusted accordingly. On the other hand, it appears quite natural that different quality criteria are required for algorithms segmenting global gray matter contrary to small brain structure such as hippocampus. Hence comes the second challenge addressed in this thesis: developing models that allow customizing quality criterion according to the acquisition parameters and the required performance of a target application. Taking up the first part of such challenge, we investigated certain acquisition parameters effects (i.e., echo time, bandwidth, signal suppression, field strength) and proposed an exploratory model with reasonable predictive performance (86.6 % sensitivity 92 % specificity). In order to customize quality cutoff levels according to the required performance of a target application, we developed a framework based on motion simulations. The idea is to progressively and realistically corrupt an image with motion and analyze the influence of such degradations (i.e., measured by means of our quality assessment technique) on the accuracy of a brain morphometry algorithm. To be faithful to the challenging research context of early diagnosis of AD, we targeted the automated morphometry of gray matter (GM) tissue (i.e., important imaging-based marker of pathological atrophy). Our framework reveals that 2 % error on the global volume of GM can be induced by a 1 mm patient motion halfway through the acquisition (critical low-frequency sampling of rectilinear k-space). However, this small movement is reliably caught by our quality index whose variation can be further characterized as an exponential function of the error. This model thus allows determining whether an image is of sufficient quality to warrant further quantitative analysis. In conclusion, this thesis underscores the importance of a rigorous quality assessment of MR images to support high-quality outputs of quantitative morphometry tools, ultimately increasing probability for higher diagnostic accuracy. Overall, we envision that such quality assessment could greatly improve clinical workflow through its ability to rule-out the need for a repeat scan while the patient is still in the magnet bore. Just as importantly, it would be an important step forward for incorporating morphometry analysis into regular brain-imaging protocols and thus, further exploit the clinical potential of MRI.