Integration of multi-shell diffusion imaging derived metrics in tractography reconstructions of clinical data

Tese de mestrado integrado Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2019Nos últimos anos, com o rápido avanço das técnicas imagiológicas, a oportunidade de mapear o cérebro humano in vivo com uma resolução sem precedentes tornou-se realidade, permanecendo ainda hoje como uma das áreas de maior interesse da neurociência. Sabendo que o movimento natural das moléculas de água nos tecidos biológicos é altamente influenciado pelo ambiente microestrutural envolvente, e que a anisotropia que este processo aleatório assume na matéria branca pode ser explorada com o intuito de inferir características importantes associadas ao tecido neuronal, a ressonância magnética ponderada por difusão (dMRI, do inglês “Diffusion-Weighted Magnetic Resonance Imaging") afirmou-se como a técnica de imagem mais amplamente utilizada para a investigação in vivo e não invasiva da conectividade cerebral. A primeira técnica padrão de dMRI foi a imagiologia por tensor de difusão (DTI, do inglês "Diffusion Tensor Imaging"). Implementada com a capacidade de fornecer sensibilidade à microestrutura do tecido, esta técnica permite extrair informação acerca da tridimensionalidade da distribuição da difusão de moléculas de água através da aplicação de seis gradientes de difusão não colineares entre si. Além da difusividade média (MD, do inglês "Mean Diffusivity"), é também possível extrair outros índices microestruturais, como a anisotropia fraccional (FA, do inglês "Fractional Anisotropy"), que fornece informação acerca da percentagem de difusão anisotrópica num determinado voxel. Ambas as métricas são amplamente utilizadas como medidas de alterações microestruturais, todavia, apesar da sua sensibilidade, estes marcadores não são específicos quanto às características individuais da microestrutura tecidual. Regiões com reduzida FA podem camuflar regiões de conformação de cruzamento de fibras, ou fibras muito anguladas, que a DTI não consegue resolver. A razão para esta limitação reside no número reduzido de diferentes direções de difusão que são exploradas, assim como no pressuposto de que a distribuição das moléculas de água é gaussiana, o que não é necessariamente verdade. De forma alternativa e com o intuito de tais limitações serem ultrapassadas, é possível implementar uma representação matemática do sinal adquirido de forma a explorar o propagador de difusão, da qual a imagiologia por ressonância magnética do propagador aparente médio (MAP-MRI, do inglês “Mean Apparent Propagator Magnetic Resonance Imaging”) é exemplo. Esta técnica analítica caracteriza-se pelo cálculo da função de densidade de probabilidade associada ao deslocamento de spin, o que permite descrever o caráter não-gaussiano do processo de difusão tridimensional e quantificar índices escalares inerentes ao processo de difusão, os quais sublinham as características complexas intrínsecas à microestrutura do tecido. Estes parâmetros incluem o deslocamento médio quadrático (MSD, em inglês “mean square displacement”), a probabilidade de retorno à origem (RTOP, do inglês “return-to-the origin probability”) e suas variantes de difusão em uma e duas dimensões – a probabilidade de retorno ao plano (RTPP, do inglês “return-to-the plane probability”) e a probabilidade de retorno ao eixo (RTAP, do inglês “return-to-the axis probability”), respetivamente. Em resposta às limitações da DTI associadas à falta de especificidade para distinguir características microestruturais dos tecidos, surgiu ainda o modelo de Dispersão de Orientação de Neurite e Imagem de Densidade (NODDI, do inglês “Neurite Orientation Dispersion and Density Imaging”), o qual utiliza o processo de difusão para estimar a morfologia das neurites. Tendo como premissa subjacente que o sinal de difusão pode ser definido pela soma da contribuição dos sinais de diferentes compartimentos, este modelo biofísico diferencia o espaço intra e extracelular o que, por sua vez, permite quantificar a dispersão e densidade das neurites. Deste modo, dois parâmetros intrínsecos à microestrutura envolvente podem ser calculados: a densidade neurítica e o índice de dispersão da orientação das neurites. No entanto, de forma a garantir a viabilidade clínica do modelo, este pode ser aplicado por meio do método AMICO (do inglês “Accelerated Microstructure Imaging via Convex Optimization”) através do seu ajuste linear, o que permite o cálculo do índice de dispersão da orientação das neurites (ODI, do inglês “Orientation Dispersion Index”), da fração de volume intracelular (ICVF do inglês, “Intracellular Volume Fraction”), e da fração de volume isotrópico (ISOVF, do inglês “Isotropic Volume Fraction”). O estudo da configuração arquitetural das estruturas cerebrais in vivo, por meio da dMRI associada aos métodos de tractografia, permitiu a reconstrução não invasiva das fibras neuronais e a exploração da informação direcional inerente às mesmas, sendo que o seu estudo tem revelado uma enorme expansão por meio do estabelecimento de marcadores biológicos perante a presença de diversas condições patológicas. O objetivo principal desta dissertação prende-se com existência de uma variação proeminentenas métricas de difusão ao longo dos tratos de matéria branca no cérebro humano. Atualmente, a maioriados estudos de tractografia tem por base uma abordagem que se resume à análise do valor escalar médio da métrica de difusão para a estrutura cerebral em estudo, pelo que se tem verificado um crescente interesse na utilização de métodos que considerem a extensão da variabilidade nas métricas de difusão ao longo dos tratos de modo a providenciarem um maior nível de detalhe ao nível do processo de difusão, evitando interpretações erróneas dos parâmetros microestruturais. Desta forma, em primeiro lugar, foi desenvolvido uma análise ao longo dos tratos de matéria branca, tendo por base a variação dos valores assumidos pelos parâmetros microestruturais acima mencionados. No presente estudo foi possível demonstrar a eficácia de tal abordagem ao longo de três tratos de matéria de branca (fascículo arqueado, trato corticoespinhal, e corpo caloso), para além de permitir, através da variância assumida pelos diversos parâmetros microestruturais, o estudo detalhado de regiões anatómicas que assumem uma distribuição complexa de múltiplos conjuntos populacionais de fibras, como é o caso do centro semioval, o qual constitui uma região de cruzamento de fibras provenientes dos três tratos de matéria branca em estudo. De seguida, esta técnica foi utilizada com sucesso na identificação de diferenças microestruturais por meio do estudo dos diversos parâmetros de difusão em pacientes com diagnóstico prévio de epilepsia no lobo temporal (TLE, do inglês “Temporal Lobe Epilepsy”), com foco epiléptico localizado no hemisfério esquerdo, e controlos. O estudo do ambiente microestrutural por meio dos múltiplos mapas escalares permitiu averiguar a alteração do processo de difusão e/ou anisotropia, associadas ao efeito fisiopatológico da TLE na organização da matéria branca. Os resultados revelaram diferenças localizadas, as quais se traduziram num aumento da difusividade e redução da anisotropia do processo de difusão ao longo dos tratos em estudo dos pacientes com TLE, sugerindo deste modo uma perda na organização das diversas estruturas anatómicas e a expansão do espaço extracelular face aos controlos. Verificou-se ainda que pacientes com esta condição neurológica sofrem de alterações microestruturais que afetam redes cerebrais em grande escala, envolvendo regiões temporais e extratemporais de ambos os hemisférios. Adicionalmente, aplicada como técnica capaz de investigar padrões de mudança na matéria branca, procedeu-se à realização de um estudo assente na estatística espacial baseada no trato (TBSS, do inglês “Tract-Based Spatial Statistics”). Após a exploração das diversas métricas de difusão, os pacientes com TLE (com lateralização à esquerda) demonstraram alterações no processo de difusão, ilustradas pelos diversos padrões de mudança microestrutural de cada métrica em estudo, concordantes com os resultados anteriormente aferidos pela análise ao longo do trato. Por fim, uma análise baseada em fixel (FBA, do inglês “Fixel-Based Analysis”) foi realizada, a qual permitiu uma análise estatística abrangente de medidas quantitativas da matéria branca, com o intuito de detetar alterações no volume intra-axonal por variação na densidade intra-voxel e/ou reorganização da morfologia macroscópica. Para identificar tais diferenças entre pacientes e controlos, três parâmetros foram considerados: densidade das fibras (FD, do inglês “Fibre Density”), seção transversal do feixe de fibras (FC, do inglês “Fibre-bundle Cross-section”), e densidade de fibras e seção transversal (FDC, do inglês “Fibre Density and Cross-section). Reduções na FD, FC e FDC foram identificadas em pacientes com TLE (com lateralização à esquerda) em comparação com os controlos, o que está de acordo com as mudanças microestruturais que resultam do processo de degeneração que afeta as estruturas de matéria branca com a perda de axónios na presença de uma condição neuropatológica como a TLE. Apesar do resultado final positivo, tendo em conta a meta previamente estabelecida, está aberto o caminho para o seu aperfeiçoamento, tendo em vista as direções futuras que emergem naturalmente desta dissertação. Como exemplo disso, poder-se-á recorrer ao estudo pormenorizado das metodologias técnicas associadas à abordagem apresentada que tem por base a análise das métricas de difusão ao longo dos tratos de matéria branca, uma vez que o desvio padrão associado a cada valor atribuído pelas diversas métricas foi significativo, o que de alguma forma poderá ter influenciado os resultados e, consequentemente, as conclusões deles extraídas, tendo em vista a sua viabilidade enquanto aplicação clínica. Como nota final, gostaria apenas de salientar que a imagiologia por difusão e, em particular, a tractografia têm ainda muito espaço para progredir. A veracidade desta afirmação traduz-se pela existência de uma grande variedade de modelos e algoritmos implementados, bem como de técnicas e metodologias de análise à informação microestrutural retida tendo por base o perfil de difusão que carateriza cada trato em estudo, sem que no entanto, exista consenso na comunidade científica acerca da melhor abordagem a seguir.Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging method which has been successfully applied to study white matter (WM) in order to determine physiological information and infer tissue microstructure. The human body is filled with barriers affecting the mobility of molecules and preventing it from being constant in different directions (anisotropic diffusion). In the brain, the sources for this anisotropy arise from dense packing axons and from the myelin sheath that surrounds them. Diffusion Tensor Imaging (DTI) is widely used to extract fibre directions from diffusion data, but it fails in regions containing multiple fibre orientations. The constrained spherical deconvolution technique had been proposed to address this limitation. It provides an estimate of the fibre orientation distribution that is robust to noise whilst preserving angular resolution. As a noninvasive technique that generates a three-dimensional reconstruction of neuronal fibres, tractography is able to map in vivo the human WM based on the reconstruct of the fibre orientations from the diffusion profile. Most of the tractography studies use a “tract-averaged” approach to analysis, however it is well known that there is a prominent variation in diffusion metrics within WM tracts. In this study we address the challenge of defining a microstructural signature taking into account the potentially rich anatomical variation in diffusion metrics along the tracts. Therefore, a workflow to conduct along-tract analysis of WM tracts (namely, arcuate fasciculus, corticospinal and corpus callosum) and integrate not only DTI derived measures, but also more advanced parameters from Mean Apparent Propagator-Magnetic Resonance Imaging (MAP-MRI) and Neurite Orientation Dispersion and Density Imaging (NODDI) model, was developed across healthy controls and patients with Temporal Lobe Epilepsy (TLE). Beyond the true biological variation in diffusion properties along tracts, this technique was applied to show that it allows a more detailed analysis of small regions-of-interest extracted from the tract in order to avoid fibres from WM pathways in the neighbourhood, which might lead to equivocal biological interpretations of the microstructural parameters. Consequently, the along-tract streamline distribution from the centrum semiovale, which is known to be a complex fibre geometry with multiple fibres populations from arcuate fasciculus, corticospinal and corpus callosum, was investigated. Finally, to validate our approach and highlight the strength of this extensible framework, two other methods were implemented in order to support the conclusions derived from the along-tract analysis computed between-groups. Firstly, a tract-based spatial statistics (TBSS) analysis was performed to study the WM change patterns across the whole brain in patients with TLE, and explore the alteration of multiple diffusion metrics. This voxel-based technique provides a powerful and objective method to perform multi-subject comparison, based on voxel-wise statistics of diffusion metrics but simultaneous aiming to minimize the effects of misalignment using a conventional voxel-based analysis method. With this in mind, the results showed increased diffusivity and reduced diffusion anisotropy, suggesting a loss of structural organization and expansion of the extracellular space in the presence of neuropathological condition as TLE. Secondly, the fixel-based analysis (FBA) was performed allowing a comprehensive statistical analysis of WM quantitative measures in order to have access to changes that may result within WM tracts in the presence of TLE. The microstructural/macrostructural changes in WM tracts of TLE patients were observed in temporal and extratemporal regions of both hemispheres, which agrees with the concept that epilepsy is a network disorder

Double diffusion encoding and applications for biomedical imaging

Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application

Associations between diffusion MRI microstructure and cerebrospinal fluid markers of Alzheimer's disease pathology and neurodegeneration along the Alzheimer's disease continuum

INTRODUCTION: White matter (WM) degeneration is a critical component of early Alzheimer's disease (AD) pathophysiology. Diffusion-weighted imaging (DWI) models, including diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAP-MRI), have the potential to identify early neurodegenerative WM changes associated with AD. METHODS: We imaged 213 (198 cognitively unimpaired) aging adults with DWI and used tract-based spatial statistics to compare 15 DWI metrics of WM microstructure to 9 cerebrospinal fluid (CSF) markers of AD pathology and neurodegeneration treated as continuous variables. RESULTS: We found widespread WM injury in AD, as indexed by robust associations between DWI metrics and CSF biomarkers. MAP-MRI had more spatially diffuse relationships with Aβ42/40 and pTau, compared with NODDI and DTI. DISCUSSION: Our results suggest that WM degeneration may be more pervasive in AD than is commonly appreciated and that innovative DWI models such as MAP-MRI may provide clinically viable biomarkers of AD-related neurodegeneration in the earliest stages of AD progression

On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke

Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain “in vivo”, and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications

Brain Microstructure: Impact of the Permeability on Diffusion MRI

Diffusion Magnetic Resonance Imaging (dMRI) enables a non invasive in-vivo characterization of the brain tissue. The disentanglement of each microstructural property reflected on the total dMRI signal is one of the hottest topics in the field. The dMRI reconstruction techniques ground on assumptions on the signal model and consider the neurons axons as impermeable cylinders. Nevertheless, interactions with the environment is characteristic of the biological life and diffusional water exchange takes place through cell membranes. Myelin wraps axons with multiple layers constitute a barrier modulating exchange between the axon and the extracellular tissue. Due to the short transverse relaxation time (T2) of water trapped between sheets, myelin contribution to the diffusion signal is often neglected. This thesis aims to explore how the exchange influences the dMRI signal and how this can be informative on myelin structure. We also aimed to explore how recent dMRI signal reconstruction techniques could be applied in clinics proposing a strategy for investigating the potential as biomarkers of the derived tissue descriptors. The first goal of the thesis was addressed performing Monte Carlo simulations of a system with three compartments: intra-axonal, spiraling myelin and extra-axonal. The experiments showed that the exchange time between intra- and extra-axonal compartments was on the sub-second level (and thus possibly observable) for geometries with small axon diameter and low number of wraps such as in the infant brain and in demyelinating diseases. The second goal of the thesis was reached by assessing the indices derived from three dimensional simple harmonics oscillator-based reconstruction and estimation (3D-SHORE) in stroke disease. The tract-based analysis involving motor networks and the region-based analysis in grey matter (GM) were performed. 3D-SHORE indices proved to be sensitive to plasticity in both white matter (WM) and GM, highlighting their viability as biomarkers in ischemic stroke. The overall study could be considered the starting point for a future investigation of the interdependence of different phenomena like exchange and relaxation related to the established dMRI indices. This is valuable for the accurate dMRI data interpretation in heterogeneous tissues and different physiological conditions

Magnetic Resonance Characterization of Porous Media Using Diffusion through Internal Magnetic Fields

When a porous material is inserted into a uniform magnetic field, spatially varying fields typically arise inside the pore space due to susceptibility contrast between the solid matrix and the surrounding fluid. As a result, direct measurement of the field variation may provide a unique opportunity to characterize the pore geometry. The sensitivity of nuclear magnetic resonance (NMR) to inhomogeneous field variations through their dephasing effects on diffusing spins is unique and powerful. Recent theoretical and experimental research sheds new light on how to utilize susceptibility-induced internal field gradients to quantitatively probe the microstructure of porous materials. This article reviews ongoing developments based on the stimulated echo-pulse sequence to extend the characterization of porous media using both spatially resolved and unresolved susceptibility-induced internal gradients that operate on a diffusing-spin ensemble.open

Extraction of Structural Metrics from Crossing Fiber Models

Diffusion MRI (dMRI) measurements allow us to infer the microstructural properties of white matter and to reconstruct fiber pathways in-vivo. High angular diffusion imaging (HARDI) allows for the creation of more and more complex local models connecting the microstructure to the measured signal. One of the challenges is the derivation of meaningful metrics describing the underlying structure from the local models. The aim hereby is to increase the specificity of the widely used metric fractional anisotropy (FA) by using the additional information contained within the HARDI data. A local model which is connected directly to the underlying microstructure through the model of a single fiber population is spherical deconvolution. It produces a fiber orientation density function (fODF), which can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle). Parameterizing these peaks one is able to disentangle and characterize these bundles. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second order statistics of the fiber orientations, from which metrics for the parametric quantification of fiber bundles are derived. Meaningful relationships between these measures and the underlying microstructural properties are proposed. The focus lies on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. These metrics are compared to the conventionally used fractional anisotropy (FA) and it is shown how they may help to increase the specificity of the characterization of microstructural properties. Visualization of the micro-structural arrangement is another application of dMRI. This is done by using tractography to propagate the fiber layout, extracted from the local model, in each voxel. In practice most tractography algorithms use little of the additional information gained from HARDI based local models aside from the reconstructed fiber bundle directions. In this work an approach to tractography based on the Bingham parameterization of the fODF is introduced. For each of the fiber populations present in a voxel the diffusion signal and tensor are computed. Then tensor deflection tractography is performed. This allows incorporating the complete bundle information, performing local interpolation as well as using multiple directions per voxel for generating tracts. Another aspect of this work is the investigation of the spherical harmonic representation which is used most commonly for the fODF by means of the parameters derived from the Bingham distribution fit. Here a strong connection between the approximation errors in the spherical representation of the Dirac delta function and the distribution of crossing angles recovered from the fODF was discovered. The final aspect of this work is the application of the metrics derived from the Bingham fit to a number of fetal datasets for quantifying the brain’s development. This is done by introducing the Gini-coefficient as a metric describing the brain’s age

Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis

Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response