Biology subjects

Viergever, M. A.

Publications and source records attributed to Viergever, M. A..

6 recordsLinked to original sources

bioRxiv2019

Harmonization of diffusion MRI datasets with adaptive dictionary learning

Diffusion magnetic resonance imaging can indirectly infer the microstructure of tissues and provide metrics subject to normal variability in a population. Potentially abnormal values may yield essential information to support analysis of controls and patients cohorts, but subtle confounds could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability of the data. Our harmonization algorithm does not require paired training datasets, nor spatial registration or matching spatial resolution. Overcomplete dictionaries are learned iteratively from all datasets at the same time with an adaptive regularization criterion. removing variability attributable to the scanners in the process. The obtained mapping is applied directly in the native space of each subject towards a scanner-space. The method is evaluated with a public database which consists of two different protocols acquired on three different scanners. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner. Experiments with alterations using a free water compartment, which is not simulated in the training data, shows that the modifications applied to the diffusion weighted images are preserved in the diffusion metrics after harmonization, while still reducing global variability at the same time. The algorithm could help multicenter studies pooling their data by removing scanner specific confounds, and increase statistical power in the process.

neuroscience↗

bioRxiv2019

Effects of Inaccurate Response Function Calibration on Characteristics of the Fiber Orientation Distribution in Diffusion MRI

Diffusion MRI of the brain enables to quantify white matter fiber orientations noninvasively. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Constrained spherical deconvolution requires to define - or derive from the data - a response function, which is used to compute the fiber orientation distribution (FOD). This definition or derivation is not unequivocal and can thus result in different characteristics of the response function which are expected to affect the FOD computation and the subsequent fiber tracking. In this work, we explored the effects of inaccuracies in the shape and scaling factors of the response function on the FOD characteristics. With simulations, we show that underestimation of the shape factor in the response functions has a larger effect on the FOD peaks than overestimation of the shape factor, whereas the latter will cause more spurious peaks. Moreover, crossing fiber populations with a smaller separation angle were more sensitive to the response function inaccuracy than fiber populations with more orthogonal separation angles. Furthermore, the FOD characteristics show deviations as a result of modified shape and scaling factors of the response function. Results with the in vivo data demonstrate that the deviations of the FODs and spurious peaks can further deviate the termination of propagation in fiber tracking. This work highlights the importance of proper definition of the response function and how specific calibration factors can affect the FOD and fiber tractography results.

neuroscience↗

bioRxiv2019

Statistical significance in DTI group analyses: How the choice of the estimator can inflate effect sizes

Diffusion magnetic resonance imaging (dMRI) is one of the most prevalent methods to investigate the micro- and macrostructure of the human brain in vivo. Prior to any group analysis, dMRI data are generally processed to alleviate adverse effects of known artefacts such as signal drift, data noise and outliers, subject motion, and geometric distortions. These dMRI data processing steps are often combined in automated pipelines, such as the one of the Human Connectome Project (HCP). While improving the performance of processing tools has clearly shown its benefits at each individual step along the pipeline, it remains unclear whether - and to what degree - choices for specific user-defined parameter settings can affect the final outcome of group analyses. In this work, we demonstrate how making such a choice for a particular processing step of the pipeline drives the final outcome of a group study. More specifically, we performed a dMRI group analysis on gender using HCP data sets and compared the results obtained with two diffusion tensor imaging estimation methods: the widely used ordinary linear least squares (OLLS) and the more reliable iterative weighted linear least squares (IWLLS). Our results show that the effect sizes for group analyses are significantly smaller with IWLLS than with OLLS. While previous literature has demonstrated higher estimation reliability with IWLLS than with OLLS using simulations, this work now also shows how OLLS can produce a larger number of false positives than IWLLS in a typical group study. We therefore highly recommend using the IWLLS method. By raising awareness of how the choice of estimator can artificially inflate effect size and thus alter the final outcome, this work may contribute to improvement of the reliability and validity of dMRI group studies.

neuroscience↗

bioRxiv2019

Automated characterization of noise distributions in diffusion MRI data

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g., coils sensitivity maps or reconstruction coeffcients), which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired on a 3T Philips scanner along with noise-only measurements. Finally, experiments on freely available datasets from a single subject acquired on a 3T GE scanner are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset acquired on a 3T Siemens scanner. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results for the bias correction and denoising task show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.

neuroscience↗

bioRxiv2019

Reducing variability in along-tract analysis with diffusion profile realignment

Diffusion weighted magnetic resonance imaging (dMRI) provides a non invasive virtual reconstruction of the brains white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analyses with this along-tract strategy requires correspondence between points of tract pathways across subjects. This is usually achieved by creating a new common space where the representative streamlines from every subject are resampled to the same number of points. If the underlying anatomy of some subjects was altered due to, e.g., disease or developmental changes, such information might be lost by resampling to a fixed number of points. In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR). Experiments on synthetic datasets show that DPR reduces the coefficient of variation for the mean diffusivity, fractional anisotropy and apparent fiber density when compared to the unaligned case. Using 100 in vivo datasets from the human connectome project, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Pairwise Students t-tests between these altered subjects and the original subjects indicate that regional changes are identified after realignment with the DPR algorithm, while preserving differences previously detected in the unaligned case. This new correction strategy contributes to revealing effects of interest which might be hidden by misalignment and has the potential to improve the specificity in longitudinal population studies beyond the traditional region of interest based analysis and along-tract analysis workflows.

neuroscience↗

bioRxiv2018

Superoanterior Fasciculus (SAF): Novel fiber tract revealed by diffusion MRI fiber tractography

Substantial progress in acquisition, processing, and analysis boosted the reliability of diffusion-weighted MRI and increased the accuracy of mapping white matter pathways with fiber tractography. Since the introduction of region of interest (ROI) based virtual dissection by Conturo et al. in 1999, researchers have used tractography to identify white matter pathways, which faithfully represented previously known structures revealed by dyeing studies or post-mortem descriptions. The reconstructed streamlines are subjects of bundle-specific in vivo investigations to show differences between groups (e.g., comparing fractional anisotropy (FA) between patients and healthy controls) or to describe the relation between diffusion scalars and metrics of interest (e.g.: normal aging or changes due to learning). By applying a reverse strategy in using diffusion-weighted MRI tractography first, then supporting the findings with other techniques, we have identified a bilateral tract in the frontal cortex - the superoanterior fasciculus (SAF). The tract resembles the anterior shape of the cingulum bundle, but is located more frontally. To erase the chance that our findings are confounded by acquisition, processing or modeling artifacts, we analyzed a total of 421 subjects from four cohorts with different acquisition schemes and diverse processing pipelines. The findings were also completed with other non-MRI techniques, such as polarized light microscopy and dissection. Tractography results demonstrate a long pathway and are consistent among cohorts, while dissection indicates a series of U-shaped fibers connecting adjacent gyri. In conclusion, we hypothesize that these consecutive U-shaped fibers emerge to form a pathway, thereby resulting in a multicomponent bundle.

neuroscience↗