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

Development of Advanced, Clinically Feasible Neuroimaging Methodology with Diffusional Kurtosis Imaging

Diffusion MRI (dMRI) is a powerful, non-invasive tool for probing the structural organization of the human brain. Quantitative dMRI analyses provide unique capabilities for the characterization of tissue microstructure as well as imaging contrast that is not available to other modalities. White matter tractography relies on dMRI and is currently the only non-invasive technique for mapping structural connections in the human brain. In this chapter, we will describe diffusional kurtosis imaging, an effective and versatile dMRI technique, and discuss a clinical problem in temporal lobe epilepsy (TLE) which is insurmountable with current diagnostic approaches. Subsequent chapters will further develop the capabilities of DKI and demonstrate how it may be particularly well suited to overcome current barriers to care in the clinical management of TLE

Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI

Age-related microstructural differences have been detected using diffusion tensor imaging (DTI). Although DTI is sensitive to the effects of aging, it is not specific to any underlying biological mechanism, including demyelination. Combining multiexponential T2 relaxation (MET2) and multishell diffusion MRI (dMRI) techniques may elucidate such processes. Multishell dMRI and MET2 data were acquired from 59 healthy participants aged 17-70 years. Whole-brain and regional age-associated correlations of measures related to multiple dMRI models (DTI, diffusion kurtosis imaging [DKI], neurite orientation dispersion and density imaging [NODDI]) and myelin-sensitive MET2 metrics were assessed. DTI and NODDI revealed widespread increases in isotropic diffusivity with increasing age. In frontal white matter, fractional anisotropy linearly decreased with age, paralleled by increased "neurite" dispersion and no difference in myelin water fraction. DKI measures and neurite density correlated well with myelin water fraction and intracellular and extracellular water fraction. DTI estimates remain among the most sensitive markers for age-related alterations in white matter. NODDI, DKI, and MET2 indicate that the initial decrease in frontal fractional anisotropy may be due to increased axonal dispersion rather than demyelination

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project.

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience

Advanced Application of Diffusion Kurtosis Imaging

Diffusion tensor imaging (DTI) has become a standard procedure in clinical routine as well as research as it enables the reconstruction and visualization of fiber tracts in the human brain. Due to the simplified assumption the tensor model – a Gaussian distribution of the diffusion – it typically fails to provide neither accurate spatial mapping nor quantification of crossing or kissing fibers. A clinically feasible development might be diffusion kurtosis imaging (DKI), an extension of DTI also integrating non-Gaussian distribution diffusion processes and thereby shall overcome some of its limitations. The potential DKI will be evaluated in case of the detection of the interhemispheric asymmetry of the white matter in healthy volunteers (n = 20), as well as the analysis of tumor-related impairments of fiber tracts and their correlation with neurological deficits in patients (n = 13) diagnosed with glioma. In order to analyze interhemispheric asymmetry across the whole brain, especially of nine large fiber tracts, tract-based spatial statistics (TBSS) analysis was performed using DTI- and DKI-based parameters, a laterality index was calculated for asymmetries and DTI- and DKI-based results were compared. With regard to fractional anisotropy as marker of integrity, asymmetry was found for all nine fiber tracts based on DTI and seven tracts based on DKI. For mean diffusivity, asymmetries were found for three (DTI) and two (DKI) fiber tracts. Regarding mean kurtosis, asymmetry was found in one tract. The interhemispheric asymmetry thereby varied in anatomical location as well as in cluster size. Only small parts of the tracts were affected. A comparison of DTI and DKI showed significantly higher fractional anisotropy and mean diffusivity based on DKI compared to DTI. Gender and handedness did not seem to have any influence. For the assessment of tumor-related changes of fiber tracts in patients diagnosed with glioma, especially in relation to pre-existing and postoperative neurological deficits (hemiparesis, aphasia), templates for the corticospinal tract and the arcuate fasciculus were created based on DTI- and DKI-derived parameters, respectively. The corticospinal tract and the arcuate fasciculus were reconstructed for each patient and the associated parametric maps were projected onto the templates. Based on this, alterations along the tracts could be identified and quantified. Alterations were found on fiber tracts regardless of the spatial proximity to the lesion. There was a correlation between alterations based on fractional anisotropy, mean diffusivity and mean kurtosis. Increased mean diffusivity was associated with alteration in mean kurtosis, a decreased fractional anisotropy was found concurrent with a likewise decreased mean kurtosis. In the case of pre-existing neurological deficits (hemiparesis, aphasia) with regard to the changes along the fiber tracts (corticospinal tract, left arcuate fasciculus), most often increased mean diffusivity and altered mean kurtosis was found. Applying this pattern for prediction of corresponding postoperative neurological deficits a sensitivity of 75.0% and a specificity of 87.5% was achieved. DKI seems to more precisely estimated and depict the underlying microstructure in comparison to DTI. Thereby, in pathological cases especially the mean kurtosis seems to be of special interest. A combination of DTI- and DKI based parameters, particularly with regard to its clinical usability and value, offers great potential in clinical routine

Imaging diffusional variance by MRI [public] : The role of tensor-valued diffusion encoding and tissue heterogeneity

Diffusion MRI provides a non-invasive probe of tissue microstructure. We recently proposed a novel method for diffusion-weighted imaging, so-called q-space trajectory encoding, that facilitates tensor-valued diffusion encoding. This method grants access to b-tensors with multiple shapes and enables us to probe previously unexplored aspects of the tissue microstructure. Specifically, we can disentangle diffusional heterogeneity that originates from isotropic and anisotropic tissue structures; we call this diffusional variance decomposition (DIVIDE).In Paper I, we investigated the statistical uncertainty of the total diffusional variance in the healthy brain. We found that the statistical power was heterogeneous between brain regions which needs to be taken into account when interpreting results.In Paper II, we showed how spherical tensor encoding can be used to separate the total diffusional variance into its isotropic and anisotropic components. We also performed initial validation of the parameters in phantoms, and demonstrated that the imaging sequence could be implemented on a high-performance clinical MRI system. In Paper III and V, we explored DIVIDE parameters in healthy brain tissue and tumor tissue. In healthy tissue, we found that diffusion anisotropy can be probed on the microscopic scale, and that metrics of anisotropy on the voxel scale are confounded by the orientation coherence of the microscopic structures. In meningioma and glioma tumors, we found a strong association between anisotropic variance and cell eccentricity, and between isotropic variance and variable cell density. In Paper IV, we developed a method to optimize waveforms for tensor-valued diffusion encoding, and in Paper VI we demonstrated that whole-brain DIVIDE is technically feasible at most MRI systems in clinically feasible scan times

Structural connectivity based on diffusion Kurtosis imaging

Structural connectivity models based on Diffusion Tensor Imaging (DTI) are strongly affected by the technique’s inability to resolve crossing fibres, either intra- or inter-hemispherical connections. Several models have been proposed to address this issue, including an algorithm aiming to resolve crossing fibres which is based on Diffusion Kurtosis Imaging (DKI). This technique is clinically feasible, even when multi-band acquisitions are not available, and compatible with multi-shell acquisition schemes. DKI is an extension of DTI enabling the estimation of diffusion tensor and diffusion kurtosis metrics. In this study we compare the performance of DKI and DTI in performing structural brain connectivity. Six healthy subjects were recruited, aged between 25 and 35 (three females). The MRI experiments were performed using a 3T Siemens Trio with a 32-channel head coil. The scans included a T1-weighted sequence (1mm3), and a DWI with b-values 0, 1000 and 2000 s:mm2. For each b-value, 64 equally spaced gradient directions were sampled. For DTI fitting only images with b-value of 0 and 1000 s:mm2 were considered, whereas for the DKI fitting, the whole cohort of images were considered. To fit both DTI and DKI tensors, extract the metrics and perform tract reconstructions, the toolbox DKIu was used, and the structural connectivity analysis was accomplished using the MIBCA toolbox. Tractography results revealed, as expected, that DKI-based tractography models can resolve crossing fibres within the same voxel, which posed a limitation to the DTI-based tractography models. Structural connectivity analysis showed DKI-based networks’ ability to establish both more inter-hemisphere and intra-hemisphere connections, when compared to DTI-based networks. This may be a direct consequence of the inability to resolve crossing fibres when using the DTI model. The DKI model ability to resolve crossing fibres may provide increased sensitivity to both inter- and intra-hemispherical connections. DTI-based modularity connectograms show a distinct intra-hemispherical configuration, whereas DKI-based connectograms show an increased number of inter-hemispherical connections, with several clusters extending over both hemispheres. Global and local connectivity metrics were also studied, but yielded no conclusive results. This may be due to a lack of reproducibility of the metrics or of the small cohort of subjects considered. DKI seems to provide additional insights into structural brain connectivity by resolving crossing fibres, otherwise undetected by DTI

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

Novel approaches to MRI of glioma

Gliomas are extremely heterogeneous, both morphologically and biologically, which contributes to a very poor prognosis. Current imaging of glioma is insufficient for a thorough diagnosis, therapy assessment and prognosis prediction. Moreover, refined and more sophisticated imaging technique could help in furthering our knowledge of gliomas. In order to facilitate proliferation, cancer cells undergo a change in structure and an increase in metabolism that results in distortion and disruption of tissue architecture. Gliomas are characterised by an increase in cells of variable sizes, as well as changes in the tissue microstructure. Diffusion-Weighted Imaging (DWI) and the apparent diffusion coefficient (ADC), have been extensively studied as potential imaging biomarkers for cellularity and tissue architecture. However, several studies have shown partial overlap in the measured values between tumour subtypes. Moreover, ADC is influenced by several factors and does not provide detailed information on the tissue microstructure. The Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT) is a novel diffusion model that infers tissue microstructure compartment from conventional DWI measurements. This model derives metrics for the intracellular, intravascular and extracellular– extravascular spaces providing a more detailed interpretation of the tissue microstructure. To date, VERDICT has been applied to xenograft models of colorectal cancer, patient studies of prostate cancer and recently its feasibility in glioma has been shown. In this PhD I have applied a shortened version of the VERDICT method to image intratumoral and intertumoral heterogeneity in glioma. The results have also been validated with histology as part of a prospective study. Gliomas also exhibit a significant increase in mitotic activity within the tumour. The increased number of mitosis alters cell density which, in turn, affects the total concentration of tissue sodium as the concentration of tissue sodium is approximately ten-fold higher in the extracellular compared to the intracellular space. In addition, there is a decrease in Na+/K+-ATPase activity in tumours due to ATP depletion, which contributes to disturb sodium homeostasis. Non-invasive detection of 23Na with MRI has the potential to quantify sodium concentration and therefore could be an imaging probe of cell morphology and membrane function within the tumour microenvironment, as well as a method of probing tissue heterogeneity. During my PhD, a novel 23Na-MRI technique has been used to evaluate sodium distribution within glioma and in the surrounding tissue. Metabolic reprogramming is one of the major driving forces for determining glioma growth and invasion. Therefore, the non-invasive characterization of metabolic intratumoral, peritumoral and intertumoral heterogeneity in vivo could help to better stratify patients and to develop novel therapeutic strategies targeting cancer-specific metabolic pathways. 13C magnetic resonance imaging (MRI) using dynamic nuclear polarization (DNP) is a novel technique that allows non-invasive assessment of the metabolism of hyperpolarized (HP) 13C-labelled molecules in vivo, such as the exchange of [1-13C]pyruvate to [1-13C]lactate in tumours (Warburg effect). Part of my PhD has focused on developing and translating HP [1-13C]pyruvate MRI to explore metabolic reprogramming in glioma and the surrounding microenvironment. The overall aim of my PhD has been to develop novel approaches to imaging glioma with MRI to probe both the architectural and metabolic changes of Glioma. The preliminary evidence suggests that these tools can more deeply phenotype tumours than conventional imaging approaches. Although the main focus of this work has been gliomas, the techniques developed and presented here may be applied to study other pathological conditions within the brain, which raises the possibility of other potential clinical applications for this work

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic