A comparison of methods for the registration of tractographic fibre images

Diffusion tensor imaging (DTI) and tractography have opened up new avenues in neuroscience. As most applications require precise spatial localization of the fibre images, image registration is an important area of research. Registration is usually performed prior to tractography. However more reliable images could be produced if a viable registration can be performed post tractography. This study shows two available techniques for direct registration of fibre images and explores novel adaptations of these. The methods register volume images derived from the fibres, and reapply the transformation from these registrations to the fibre images. The first method is a local affine registration and the second is a global affine registration. The local affine method produced superior results

Axon diameter measurements using diffusion MRI are infeasible

The feasibility of non-invasive axonal diameter quantification with diffusion MRI is a strongly debated topic due to the neuroscientific potential of such information and its relevance for the axonal signal transmission speed. It has been shown that under ideal conditions, the minimal diameter producing detectable signal decay is bigger than most human axons in the brain, even using the strongest currently available MRI systems. We show that resolving the simplest situations including multiple diameters is unfeasible even with diameters much bigger than the diameter limit. Additionally, the recently proposed effective diameter resulting from fitting a single value over a distribution is almost exclusively influenced by the biggest axons. We show how impractical this metric is for comparing different distributions. Overall, axon diameters currently cannot be quantified by diffusion MRI in any relevant way

Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS)

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both space and diffusion direction. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric and group operations, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. The position-orientation adaptive smoothing preserves the edges of the observed fine and anisotropic structures. The POAS-algorithm is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting

Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS)

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both space and diffusion direction. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric and group operations, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. The position-orientation adaptive smoothing preserves the edges of the observed fine and anisotropic structures. The POAS-algorithm is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting

Influence of anisotropic conductivity of the white matter tissue on EEG source reconstruction a FEM simulation study

The aim of this study was to quantify the influence of the inclusion of anisotropic conductivity on EEG source reconstruction. We applied high-resolution finite element modeling and performed forward and inverse simulation with over 4000 single dipoles placed around an anisotropic volume block (with an anisotropic ratio of 1:10) in a rabbit brain. We investigated three different orientation of the dipoles with respect to the anisotropy in the white matter block. We found a weak influence of the anisotropy in the forward simulation on the electric potential. The relative difference measure (RDM) between the potentials simulated with and without taking into account anisotropy was less than 0.009. The changes in magnitude (MAG) ranged from 0.944 to 1.036. Using the potentials of the forward simulation derived with the anisotropic model and performing source reconstruction by employing the isotropic model led to dipole shifts of up to 2 mm, however the mean shift over all dipoles and orientations of 0.05 mm was smaller than the grid size of the FEM model (0.6 mm). However, we found the source strength estimation to be more influenced by the anisotropy (up to 7-times magnified dipole strength)

Same brain, different look? The impact of scanner, sequence and preprocessing on diffusion imaging outcome parameters

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability

Mapping the human lateral geniculate nucleus and its cytoarchitectonic subdivisions using quantitative MRI

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