Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing.
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ABSTRACT:
SUBMITTER: Chen G
PROVIDER: S-EPMC5124958 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Diffusion magnetic resonance imaging is an imaging technology designed to probe anatomical architectures of biological samples in an in vivo and noninvasive manner through measuring water diffusion. The contribution of this paper is threefold. First, it proposes a new method to identify and estimate multiple diffusion directions within a voxel through a new and identifiable parametrization of the widely used multi-tensor model. Unlike many existing methods, this method focuses on the estimation of diffusion directions rather than the diffusion tensors. Second, this paper proposes a novel direction smoothing method which greatly improves direction estimation in regions with crossing fibers. This smoothing method is shown to have excellent theoretical and empirical properties. Last, this paper develops a fiber tracking algorithm that can handle multiple directions within a voxel. The overall methodology is illustrated with simulated data and a data set collected for the study of Alzheimer's disease by the Alzheimer's Disease Neuroimaging Initiative (ADNI).
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Project description:PurposeDiffusion MRI has recently been used with detailed models to probe tissue microstructure. Much of this work has been performed ex vivo with powerful scanner hardware, to gain sensitivity to parameters such as axon radius. By contrast, performing microstructure imaging on clinical scanners is extremely challenging.MethodsWe use an optimized dual spin-echo diffusion protocol, and a Bayesian fitting approach, to obtain reproducible contrast (histogram overlap of up to 92%) in estimated maps of axon radius index in healthy adults at a modest, widely-available gradient strength (35 mT m(-1)). A key innovation is the use of influential priors.ResultsWe demonstrate that our priors can improve precision in axon radius estimates--a 7-fold reduction in voxelwise coefficient of variation in vivo--without significant bias. Our results may reflect true axon radius differences between white matter regions, but this interpretation should be treated with caution due to the complexity of the tissue relative to our model.ConclusionsSome sensitivity to relatively large axons (3-15 μm) may be available at clinical field and gradient strengths. Future applications at higher gradient strength will benefit from the favorable eddy current properties of the dual spin-echo sequence, and greater precision available with suitable priors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.
| S-EPMC4791093 | biostudies-literature
Project description:Axon diameter mapping using high-gradient diffusion MRI has generated great interest as a noninvasive tool for studying trends in axonal size in the human brain. One of the main barriers to mapping axon diameter across the whole brain is accounting for complex white matter fiber configurations (e.g., crossings and fanning), which are prevalent throughout the brain. Here, we present a framework for generalizing axon diameter index estimation to the whole brain independent of the underlying fiber orientation distribution using the spherical mean technique (SMT). This approach is shown to significantly benefit from the use of real-valued diffusion data with Gaussian noise, which reduces the systematic bias in the estimated parameters resulting from the elevation of the noise floor when using magnitude data with Rician noise. We demonstrate the feasibility of obtaining whole-brain orientationally invariant estimates of axon diameter index and relative volume fractions in six healthy human volunteers using real-valued diffusion data acquired on a dedicated high-gradient 3-Tesla human MRI scanner with 300 mT/m maximum gradient strength. The trends in axon diameter index are consistent with known variations in axon diameter from histology and demonstrate the potential of this generalized framework for revealing coherent patterns in axonal structure throughout the living human brain. The use of real-valued diffusion data provides a viable solution for eliminating the Rician noise floor and should be considered for all spherical mean approaches to microstructural parameter estimation.
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Project description:Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.
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Project description:While diffusion MRI (dMRI) is currently the method of choice to non-invasively probe tissue microstructure and study structural connectivity in the brain, its spatial resolution is limited and its results need structural validation. Current ex vivo methods employed to provide 3D fiber orientations have limitations, including tissue-distorting sample preparation, small field of view or inability to quantify 3D fiber orientation distributions. 3D fiber orientation in tissue sections can be obtained from 3D scanning small-angle X-ray scattering (3D sSAXS) by analyzing the anisotropy of scattering signals. Here we adapt the 3D sSAXS method for use in brain tissue, exploiting the high sensitivity of the SAXS signal to the ordered molecular structure of myelin. We extend the characterization of anisotropy from vectors to tensors, employ the Funk-Radon-Transform for converting scattering information to real space fiber orientations, and demonstrate the feasibility of the method in thin sections of mouse brain with minimal sample preparation. We obtain a second rank tensor representing the fiber orientation distribution function (fODF) for every voxel, thereby generating fODF maps. Finally, we illustrate the potential of 3D sSAXS by comparing the result with diffusion MRI fiber orientations in the same mouse brain. We show a remarkably good correspondence, considering the orthogonality of the two methods, i.e. the different physical processes underlying the two signals. 3D sSAXS can serve as validation method for microstructural MRI, and can provide novel microstructural insights for the nervous system, given the method's orthogonality to dMRI, high sensitivity to myelin sheath's orientation and abundance, and the possibility to extract myelin-specific signal and to perform micrometer-resolution scanning.
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Project description:Coordination of services used by palliative care patients across care contexts is essential in providing patient centred care. In Lothian, Edinburgh, a baseline audit in 2008 of patients known to all four specialist palliative care teams revealed only 49% had information available to out of hours (OOH) general practitioners (GPs). This highlighted the poor handover and sharing of information, which are essential for providing quality care for palliative patients in accordance with their wishes, and for reducing inappropriate hospital admissions. A number of quality improvement measures have been introduced, some nationally in Scotland, such as the roll out of Electronic Palliative Care Summaries (ePCS) - an electronic register containing up to date information including patient wishes and latest treatment decisions. In addition there have been changes to the GP Quality Outcomes Framework encouraging the use of electronic records. Locally, at Marie Curie Hospice, drivers were implemented including hosting GP education evenings promoting ePCS use, alteration of hospice discharge letter format to complement ePCS completion, in addition to offering specialist nurse support and presence at GP practice palliative care register meetings. A re-audit in 2012 revealed that 75% of specialist palliative care patients had electronic information available to OOH services, and in 2013, 71% of patients. This represents a significant improvement in electronic information sharing across care contexts. Building on the progress of ePCS, a new Electronic Key Information Summary is currently being rolled out across Scotland. This quality improvement report reflects on the positive measures taken to address the important clinical need of effective electronic handover for specialist palliative care patients in Lothian. Furthermore, it highlights the ongoing requirement to continue to improve the quality and availability of electronically shared information for every patient known to palliative care services across care contexts.
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Project description:Registration of subject and control brains to a common anatomical space or template is the basis for quantitatively delineating regions of abnormality in an individual brain. Normally, a brain atlas is chosen as the template. Limitations in the registration process result in persistent differences between individual subject brains and template, which can be a source of error in an analysis. We propose a new approach to the registration process where the subject of interest is the registration template. Through this change, we eliminate errors due to differences between a brain template and a subject's brain. We applied this method to the analysis of FA values derived from DTI data of 20 individual mTBI patients as compared to 48 healthy controls. Subject-centered analysis resulted in identification of significantly fewer regions of abnormally low FA compared to two separate atlas-centered analyses, with subject-centered abnormalities essentially representing the common subset of abnormal low FA regions detected by the two atlas-centered methods. Whereas each atlas-centered approach demonstrated abnormalities in nearly every subject (19/20 and 20/20), the subject-centered approach demonstrated abnormalities in fewer than half the subjects (9/20). This reduction of diffusion abnormalities observed using the subject-centered approach is due to elimination of misregistration errors that occur when registering the subject of interest to a template. Evaluation of atlas-centered analyses demonstrated that 9.8% to 13.3% of subject GM and CSF was misregistered onto the WM of the brain atlas, resulting in the observation of additional low FA clusters compared to the subject-centered approach. Without careful evaluation, these misregistrations could be misinterpreted as pathology. An additional benefit of the subject-centered approach is that diffusion abnormalities can now be visualized directly in the subject's anatomical space, rather than interpolating results from the brain atlas space, and can thereby enhance correlation with other components of an imaging protocol.
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Project description:BackgroundThe purpose of this study was to develop a DTI-based method to quantitatively assess fiber angles and changes therein in leg muscles in order to facilitate longitudinal studies on muscle fiber architectural adaptations in healthy subjects.MethodsThe upper legs of five volunteers were scanned twice on the same day. The right lower legs of five volunteers were scanned twice with the ankle in three positions, i.e. -15° dorsiflexion, 0° neutral position, and 30° plantarflexion. The MRI protocols consisted of a noise scan, a 3-point mDixon scan and a DTI scan. Fiber-angle color maps were generated for four muscles in the upper legs and two muscles in the lower leg. Voxel-wise fiber angles (θ) were calculated from the angle between the principal eigenvector of the diffusion tensor and a reference line defined between the origo and insertion points of each muscle. Bland-Altman analysis, intraclass correlation coefficient (ICC), coefficient of variation (CV%), minimal detectable change (MDC), standard error (SE) and Friedman test were used for assessing the feasibility of this method and in order to have an indication of the repeatability and the sensitivity.ResultsBland-Altman analysis showed good repeatability (CV%<10 and 0.7≤ICC≤0.9) with exception of the Tibialis Anterior (TA) muscle in dorsiflexion position(CV%: 12.2) and the Semitendinosus (ST) muscle (left leg) (CV%: 11.4). The best repeatability metrics were found for the SOL muscle in neutral position (CV%: 2.6). Changes in average θ in TA and SOL with ankle positions were observed in accordance with expected agonist and antagonist functions of both muscles. For example, for the anterior left compartment the change in fiber angle Δθ with respect to the neutral position Δθ = -1.6° ± 0.8° and 2.2° ± 2.8° (p = 0.008), for dorsiflexion and plantarflexion, respectively.ConclusionOur method facilitates fast inspection and quantification of muscle fiber angles in the lower and upper leg muscles in rest and detection of changes in lower-leg muscle fiber angles with varying ankle angles.
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Project description:BackgroundTo develop and evaluate a diffusion MRI-based apparent muscle fiber diameter (AFD) method in patients with muscle denervation. It was hypothesized that AFD differences between denervated, non-denervated and control muscles would be greater than those from standard diffusion metrics.MethodsA spin-echo diffusion acquisition with multi-b-valued diffusion sampling was used. An orientation-invariant dictionary approach utilized a cylinder-based forward model and multi-compartment model for obtaining restricted and free fractions. Simulations were performed to determine precision, bias, and optimize dictionary parameters. In all, 18 exams of patients with muscle denervation and 8 exams of healthy subjects were performed at 3T. Six regions of interests (ROIs) within separate shoulder muscles were selected, yielding three groups consisting 47 control (healthy), 36 non-denervated (patients), and 68 denervated (patients) muscle ROIs. Two-sample t-tests (α=0.05) between groups were performed with Holm-Bonferroni correction. T2- and fat fraction (FF)-mapping were acquired for comparison.ResultsMean AFD was 89.7±13.6 µm in control, 71.6±15.3 µm in non-denervated, and 60.7±15.9 µm in denervated muscles and were significantly different (P<0.001) in paired comparisons and in 10/12 individual muscle region comparisons. Correlation between AFD and FF (-0.331, P<0.001) was low, but correlation between FA and FF was negligible (0.197, P=0.016). Correlation was low between AFD and T2 (-0.395, P<0.001) and between FA and T2 (0.359, P<0.001).ConclusionsDiffusion MRI-based AFD complements T2- and FF-mapping techniques to non-invasively assess muscle denervation.
| S-EPMC8666740 | biostudies-literature
Project description:In recent years, several new diffusion MRI approaches have been proposed to explore microstructural properties of the white matter, such as Q-ball imaging and spherical deconvolution-based techniques to estimate the orientation distribution function. These methods can describe the estimated diffusion profile with a higher accuracy than the more conventional second-rank diffusion tensor imaging technique. Despite many important advances, there are still inconsistent findings between different models that investigate the "crossing fibers" issue. Due to the high information content and the complex nature of the data, it becomes virtually impossible to interpret and compare results in a consistent manner. In this work, we present novel fiber tractography visualization approaches that provide a more complete picture of the microstructural architecture of fiber pathways: multi-fiber hyperstreamlines and streamribbons. By visualizing, for instance, the estimated fiber orientation distribution along the reconstructed tract in a continuous way, information of the local fiber architecture is combined with the global anatomical information derived from tractography. Facilitating the interpretation of diffusion MRI data, this approach can be useful for comparing different diffusion reconstruction techniques and may improve our understanding of the intricate white matter network.
| S-EPMC3839966 | biostudies-literature