296 research outputs found

    Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging

    Full text link
    We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an alternative reconstruction method guaranteeing a nonnegative EAP that integrates to unity. The reconstruction is validated on data simulated from two Gaussians at various crossing angles. Moreover, we demonstrate on non-uniformly sampled in vivo data that the method is far superior to linear interpolation, and allows a drastic undersampling of the data with only a minor loss of accuracy. We envision the method as a potential replacement for standard diffusion spectrum imaging, in particular when acquistion time is limited.Comment: 5 page

    Micro-structure diffusion scalar measures from reduced MRI acquisitions

    Get PDF
    In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space. Due to the huge amount of samples needed for an accurate reconstruction, more efficient alternative techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values. In order to use the EAP in practical studies, scalar measures must be directly derived, being the most common the return-to-origin probability (RTOP) and the return-to-plane and return-to-axis probabilities (RTPP, RTAP). In this work, we propose the so-called “Apparent Measures Using Reduced Acquisitions” (AMURA) to drastically reduce the number of samples needed for the estimation of diffusion properties. AMURA avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, and as opposed to common multi-shell procedures based on iterative optimization, we achieve closed-form expressions for the measures using information from one single shell. This way, the new methodology remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). We report extensive results showing the potential of AMURA to reveal microstructural properties of the tissues compared to state of the art EAP estimators, and is well above that of Diffusion Tensor techniques. At the same time, the closed forms provided for RTOP, RTPP, and RTAP-like magnitudes make AMURA both computationally efficient and robust

    Moment-based representation of the diffusion inside the brain from reduced DMRI acquisitions: Generalized AMURA

    Get PDF
    Producción CientíficaAMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain.Ministerio de Ciencia, Innovación y Universidades (grant RTI2018-094569-B-I00)Polish National Agency for Academic Exchange (grant PN/BEK/2019/1/00421)Ministry of Science and Higher Education of Poland (scholarship 692/STYP/13/2018)Junta de Castilla y León - Fondo Social Europeo (ID: 376062

    Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI

    Full text link
    We develop a general analytical and numerical framework for estimating intra- and extra-neurite water fractions and diffusion coefficients, as well as neurite orientational dispersion, in each imaging voxel. By employing a set of rotational invariants and their expansion in the powers of diffusion weighting, we analytically uncover the nontrivial topology of the parameter estimation landscape, showing that multiple branches of parameters describe the measurement almost equally well, with only one of them corresponding to the biophysical reality. A comprehensive acquisition shows that the branch choice varies across the brain. Our framework reveals hidden degeneracies in MRI parameter estimation for neuronal tissue, provides microstructural and orientational maps in the whole brain without constraints or priors, and connects modern biophysical modeling with clinical MRI.Comment: 25 pages, 12 figures, elsarticle two-colum

    Extraction of Structural Metrics from Crossing Fiber Models

    Get PDF
    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

    Efficient estimation of propagator anisotropy and non‐Gaussianity in multishell diffusion MRI with micro‐structure adaptive convolution kernels and dual Fourier integral transforms

    Get PDF
    Producción CientíficaPurpose:We seek to reformulate the so-called Propagator Anisotropy (PA) andNon-Gaussianity (NG), originally conceived for the Mean Apparent Propagatordiffusion MRI (MAP-MRI), to the Micro-Structure adaptive convolution ker-nels and dual Fourier Integral Transforms (MiSFIT). These measures describerelevant normalized features of the Ensemble Average Propagator (EAP).Theory and Methods:First, the indices, which are defined as the EAP’sdissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analyticallyreformulated within the MiSFIT framework. Then a comparison between theresulting maps is drawn by means of a visual analysis, a quantitative assess-ment via numerical simulations, a test-retest study across the MICRA dataset (6subjects scanned five times) and, finally, a computational time evaluation.Results:Findings illustrate the visual similarity between the indices computedwith either technique. Evaluation against synthetic ground truth data, however,demonstrates MiSFIT’s improved accuracy. In addition, the test–retest studyreveals MiSFIT’s higher degree of reliability in most of white matter regions.Finally, the computational time evaluation shows MiSFIT’s time reduction upto two orders of magnitude.Conclusions:Despite being a direct development on the MAP-MRI represen-tation, the PA and the NG can be reliably and efficiently computed withinMiSFIT’s framework. This, together with the previous findings in the originalMiSFIT’s article, could mean the difference that definitely qualifies diffusionMRI to be incorporated into regular clinical settings.Ministerio de Educación, Junta de Castilla y León y Fondo Social Europeo, (Grant/Award Number: OrdenEDU/1100/2017 12/12)Ministerio de Ciencia e Innovación, Grant/AwardNumbers: (RTI2018-094569-B-I00),(PID2021-124407NB-I00)Ministry of Science and Higher Education of Poland,(Grant/Award Number:692/STYP/13/2018)Narodowa Agencja Wymiany Akademickiej, (Grant/AwardNumber: PPN/BEK/2019/1/00421

    Apparent propagator anisotropy from single-shell diffusion MRI acquisitions

    Get PDF
    Purpose The apparent propagator anisotropy (APA) is a new diffusion MRI metric that, while drawing on the benefits of the ensemble averaged propagator anisotropy (PA) compared to the fractional anisotropy (FA), can be estimated from single‐shell data. Theory and Methods Computation of the full PA requires acquisition of large datasets with many diffusion directions and different b‐values, and results in extremely long processing times. This has hindered adoption of the PA by the community, despite evidence that it provides meaningful information beyond the FA. Calculation of the complete propagator can be avoided under the hypothesis that a similar sensitivity/specificity may be achieved from apparent measurements at a given shell. Assuming that diffusion anisotropy (DiA) is nondependent on the b‐value, a closed‐form expression using information from one single shell (ie, b‐value) is reported. Results Publicly available databases with healthy and diseased subjects are used to compare the APA against other anisotropy measures. The structural information provided by the APA correlates with that provided by the PA for healthy subjects, while it also reveals statistically relevant differences in white matter regions for two pathologies, with a higher reliability than the FA. Additionally, APA has a computational complexity similar to the FA, with processing‐times several orders of magnitude below the PA. Conclusions The APA can extract more relevant white matter information than the FA, without any additional demands on data acquisition. This makes APA an attractive option for adoption into existing diffusion MRI analysis pipelines
    corecore