Evaluating Fiber Orientation Dispersion Measures Computed From Single-Shell Diffusion MRI.

Fiber orientation dispersion is one of the fundamental features that can be estimated from diffusion magnetic resonance imaging (dMRI) of the brain. Several approaches have been proposed to estimate dispersion from single- and multi-shell dMRI acquisitions. Here, we derive solutions to bring these proposed methods to a standard orientation dispersion index (ODI) with the goal of making them comparable across different dMRI acquisitions. To illustrate the utility of the measures in studying brain aging, we further examined the age-dependent trajectory of the different single- and multi-shell ODI estimates in the white matter across the lifespan.Clinical Relevance- This work computes metrics of brain microstructure that can be adapted for large neuroimaging initiatives that aim to study the brain's development and aging, and to identify deviations that may serve as biomarkers of brain disease.

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner.

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-Gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to diffusion anisotropy (Kaniso), and microscopic kurtosis (µK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, µK is typically ignored in diffusion MRI signal modelling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible µK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of µK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that µK significantly contributes to total diffusional kurtosis both in grey and white matter tissue but, as expected, not in the ventricles. The first µK maps of the human brain are presented, revealing that the spatial distribution of µK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring µK and assuming the multiple Gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.

Correlation Tensor MRI deciphers underlying kurtosis sources in stroke.

Noninvasively detecting and characterizing modulations in cellular scale micro-architecture remains a desideratum for contemporary neuroimaging. Diffusion MRI (dMRI) has become the mainstay methodology for probing microstructure, and, in ischemia, its contrasts have revolutionized stroke management. Diffusion kurtosis imaging (DKI) has been shown to significantly enhance the sensitivity of stroke detection compared to its diffusion tensor imaging (DTI) counterparts. However, the interpretation of DKI remains ambiguous as its contrast may arise from competing kurtosis sources related to the anisotropy of tissue components, diffusivity variance across components, and microscopic kurtosis (e.g., arising from cross-sectional variance, structural disorder, and restriction). Resolving these sources may be fundamental for developing more specific imaging techniques for stroke management, prognosis, and understanding its pathophysiology. In this study, we apply Correlation Tensor MRI (CTI) - a double diffusion encoding (DDE) methodology recently introduced for deciphering kurtosis sources based on the unique information captured in DDE's diffusion correlation tensors - to investigate the underpinnings of kurtosis measurements in acute ischemic lesions. Simulations for the different kurtosis sources revealed specific signatures for cross-sectional variance (representing neurite beading), edema, and cell swelling. Ex vivo CTI experiments at 16.4 T were then performed in an experimental photothrombotic stroke model 3 h post-stroke (N = 10), and successfully separated anisotropic, isotropic, and microscopic non-Gaussian diffusion sources in the ischemic lesions. Each of these kurtosis sources provided unique contrasts in the stroked area. Particularly, microscopic kurtosis was shown to be a primary "driver" of total kurtosis upon ischemia; its large increases, coupled with decreases in anisotropic kurtosis, are consistent with the expected elevation in cross-sectional variance, likely linked to beading effects in small objects such as neurites. In vivo experiments at 9.4 T at the same time point (3 h post ischemia, N = 5) demonstrated the stability and relevance of the findings and showed that fixation is not a dominant confounder in our findings. In future studies, the different CTI contrasts may be useful to address current limitations of stroke imaging, e.g., penumbra characterization, distinguishing lesion progression form tissue recovery, and elucidating pathophysiological correlates.

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.

Evidence for microscopic kurtosis in neural tissue revealed by correlation tensor MRI.

PURPOSE: The impact of microscopic diffusional kurtosis (µK), arising from restricted diffusion and/or structural disorder, remains a controversial issue in contemporary diffusion MRI (dMRI). Recently, correlation tensor imaging (CTI) was introduced to disentangle the sources contributing to diffusional kurtosis, without relying on a-priori multi-gaussian component (MGC) or other microstructural assumptions. Here, we investigated µK in in vivo rat brains and assessed its impact on state-of-the-art methods ignoring µK. THEORY AND METHODS: CTI harnesses double diffusion encoding (DDE) experiments, which were here improved for speed and minimal bias using four different sets of acquisition parameters. The robustness of the improved CTI protocol was assessed via simulations. In vivo CTI acquisitions were performed in healthy rat brains using a 9.4T pre-clinical scanner equipped with a cryogenic coil, and targeted the estimation of µK, anisotropic kurtosis, and isotropic kurtosis. RESULTS: The improved CTI acquisition scheme substantially reduces scan time and importantly, also minimizes higher-order-term biases, thus enabling robust µK estimation, alongside Kaniso and Kiso metrics. Our CTI experiments revealed positive µK both in white and gray matter of the rat brain in vivo; µK is the dominant kurtosis source in healthy gray matter tissue. The non-negligible µK substantially were found to bias prior MGC analyses of Kiso and Kaniso . CONCLUSIONS: Correlation Tensor MRI offers a more accurate and robust characterization of kurtosis sources than its predecessors. µK is non-negligible in vivo in healthy white and gray matter tissues and could be an important biomarker for future studies. Our findings thus have both theoretical and practical implications for future dMRI research.

High-Resolution 3D in vivo Brain Diffusion Tensor Imaging at Ultrahigh Fields: Following Maturation on Juvenile and Adult Mice.

Diffusion tensor imaging (DTI) is a well-established technique for mapping brain microstructure and white matter tracts in vivo. High resolution DTI, however, is usually associated with low intrinsic sensitivity and therefore long acquisition times. By increasing sensitivity, high magnetic fields can alleviate these demands, yet high fields are also typically associated with significant susceptibility-induced image distortions. This study explores the potential arising from employing new pulse sequences and emerging hardware at ultrahigh fields, to overcome these limitations. To this end, a 15.2 T MRI instrument equipped with a cryocooled surface transceiver coil was employed, and DTI experiments were compared between SPatiotemporal ENcoding (SPEN), a technique that tolerates well susceptibility-induced image distortions, and double-sampled Spin-Echo Echo-Planar Imaging (SE-EPI) methods. Following optimization, SE-EPI afforded whole brain DTI maps at 135 µm isotropic resolution that possessed higher signal-to-noise ratios (SNRs) than SPEN counterparts. SPEN, however, was a better alternative to SE-EPI when focusing on challenging regions of the mouse brain -including the olfactory bulb and the cerebellum. In these instances, the higher robustness of fully refocused SPEN acquisitions coupled to its built-in zooming abilities, provided in vivo DTI maps with 75 µm nominal isotropic spatial resolution. These DTI maps, and in particular the mean diffusion direction (MDD) details, exhibited variations that matched very well the anatomical features known from histological brain Atlases. Using these capabilities, the development of the olfactory bulb (OB) in live mice was followed from week 1 post-partum, until adulthood. The diffusivity of this organ showed a systematic decrease in its overall isotropic value and increase in its fractional anisotropy with age; this maturation was observed for all regions used in the OB's segmentation but was most evident for the lobules' centers, in particular for the granular cell layer. The complexity of the OB neuronal connections also increased during maturation, as evidenced by the growth in directionalities arising in the mean diffusivity direction maps.

Correlation tensor magnetic resonance imaging.

Diffusional Kurtosis Magnetic Resonance Imaging (DKI) quantifies the extent of non-Gaussian water diffusion, which has been shown to be a sensitive biomarker for microstructure in health and disease. However, DKI is not specific to any microstructural property per se since kurtosis may emerge from several different sources. Q-space trajectory encoding schemes have been proposed for decoupling kurtosis arising from the variance of mean diffusivities (isotropic kurtosis) from kurtosis driven by microscopic anisotropy (anisotropic kurtosis). Still, these methods assume that the system is comprised of multiple Gaussian diffusion components with vanishing intra-compartmental kurtosis (associated with restricted diffusion). Here, we develop a more general framework for resolving the underlying kurtosis sources without relying on the multiple Gaussian diffusion approximation. We introduce Correlation Tensor MRI (CTI) - an approach harnessing the versatility of double diffusion encoding (DDE) and its sensitivity to displacement correlation tensors capable of explicitly decoupling isotropic and anisotropic kurtosis components from intra-compartmental kurtosis effects arising from restricted (and time-dependent) diffusion. Additionally, we show that, by subtracting these isotropic and anisotropic kurtosis components from the total diffusional kurtosis, CTI provides an index that is potentially sensitive to intra-compartmental kurtosis. The theoretical foundations of CTI, as well as the first proof-of-concept CTI experiments in ex vivo mouse brains at ultrahigh field of 16.4 T, are presented. We find that anisotropic and isotropic kurtosis can decouple microscopic anisotropy from substantial partial volume effects between tissue and free water. Our intra-compartmental kurtosis index exhibited positive values in both white and grey matter tissues. Simulations in different synthetic microenvironments show, however, that our current CTI protocol for estimating intra-compartmental kurtosis is limited by higher order terms that were not taken into account in this study. CTI measurements were then extended to in vivo settings and used to map heathy rat brains at 9.4 T. These in vivo CTI results were found to be consistent with our ex vivo findings. Although future studies are still required to assess and mitigate the higher order effects on the intra-compartmental kurtosis index, our results show that CTI's more general estimates of anisotropic and isotropic kurtosis contributions are already ripe for future in vivo studies, which can have significant impact our understanding of the mechanisms underlying diffusion metrics extracted in health and disease.

Validation and noise robustness assessment of microscopic anisotropy estimation with clinically feasible double diffusion encoding MRI.

PURPOSE: Double diffusion encoding (DDE) MRI enables the estimation of microscopic diffusion anisotropy, yielding valuable information on tissue microstructure. A recent study proposed that the acquisition of rotationally invariant DDE metrics, typically obtained using a spherical "5-design," could be greatly simplified by assuming Gaussian diffusion, facilitating reduced acquisition times that are more compatible with clinical settings. Here, we aim to validate the new minimal acquisition scheme against the standard DDE 5-design, and to quantify the proposed method's noise robustness to facilitate future clinical use. THEORY AND METHODS: DDE MRI experiments were performed on both ex vivo and in vivo rat brains at 9.4 T using the 5-design and the proposed minimal design and taking into account the difference in the number of acquisitions. The ensuing microscopic fractional anisotropy (µFA) maps were compared over a range of b-values up to 5000 s/mm2 . Noise robustness was studied using analytical calculations and numerical simulations. RESULTS: The minimal protocol quantified µFA at an accuracy comparable to the estimates obtained by means of the more theoretically robust DDE 5-design. µFA's sensitivity to noise was found to strongly depend on compartment anisotropy and tensor magnitude in a nonlinear manner. When µFA < 0.75 or when mean diffusivity is particularly low, very high signal-to-noise ratio is required for precise quantification of µFA. CONCLUSION: Our work supports using DDE for quantifying microscopic diffusion anisotropy in clinical settings but raises hitherto overlooked precision issues when measuring µFA with DDE and typical clinical signal-to-noise ratio.

Applying microstructural models to understand the role of white matter in cognitive development.

Diffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a typical multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular modeling techniques to examine individual differences in white matter structure. We first assess scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon water fraction in poor readers. Both models support this interpretation, highlighting the utility of these approaches for testing specific hypotheses about cognitive development.

Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI.

PURPOSE: Microscopic fractional anisotropy (µFA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate µFA, it has only recently been proposed that powder-averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for µFA estimation. This study aims to evaluate µFA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder-averaged SM signals. METHODS: SDE experiments were performed at 16.4 T on an ex vivo mouse brain (Δ/δ = 12/1.5 ms). The µFA maps obtained from powder-averaged SDE signals were then compared to maps obtained from DDE-MRI experiments (Δ/τ/δ = 12/12/1.5 ms), which allow a model-free estimation of µFA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings. RESULTS: µFA, as well as other estimates derived from powder-averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis). CONCLUSION: Powder-averaged SMT and (2-component) SM are unable to accurately report µFA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research.