Application of Quantitative MRI Methods in Central Nervous System Diseases

Abstract

During the past couple of decades, conventional magnetic resonance imaging (MRI) techniques have been increasingly used to assess alterations in the central nervous system (CNS). Nowadays, a variety of conventional MRI protocols are also routinely used to detect therapeutic effects of different treatment strategies. These offer several important advantages, such as the definition of disability level, the association of blood brain barrier damage, spatial and temporal dissemination of brain lesions. In the past few years, a host of non-conventional quantitative MRI techniques have been introduced for the assessment of CNS diseases. These MRI techniques appear to be reliable markers in monitoring pathologic processes related to disease activity and clinical progression. They are able to reveal a range of tissue changes that include oedema, inflammation, demyelination, axonal loss, and degeneration. Therefore, in a disease with a high degree of longitudinal variability of clinical signs and with no current adequate biological markers of disease progression, non-conventional quantitative MRI techniques provide a powerful tool to non-invasively investigate not only the pathological substrates of overt lesions but also subtle global changes that may affect the entire brain. Additionally, conventional MR imaging gives only a cross-sectional qualitative information of different tissues, while quantitative approaches offer the advantage of absolute rather than relative characterization of the underlying biochemical composition of the tissue. The determination of quantitative MRI data requires more detailed approaches and a good understanding of basic MR phenomena. Generally, it is performed by using and analysing a set of qualitative images, where the signal intensity is controlled by the change of an MR imaging parameter: inversion time, flip angle or repetition time for T1 relaxometry; echo time for T2 relaxometry and b-value for diffusionweighted data. Then quantitative MR data can be calculated by mono- or multi-exponentially fitting the signal change against these parameters. In clinical perspective, T1 and T2 relaxation times depend on structural characteristics such as local tissue density (i.e. water content), while quantitative diffusion data provides an indirect measure of tissue structure on a microscopic scale.