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Abstract: MR tractography of the lumbosacral plexus (LSP) is challenging due to the difficulty of acquiring high quality data and accurately estimating the neuronal tracts. We proposed an algorithm for an accurate visualization and assessment of the major LSP bundles using the segmentation of the cauda equina as seed points for the initial starting area for the fiber tracking algorithm.Twenty-six healthy volunteers underwent MRI examinations on a 3T MR scanner using the phased array coils with optimized measurement protocols for diffusion-weighted images and coronal T2 weighted 3D short-term inversion recovery sampling perfection with application optimized contrast using varying flip angle evaluation sequences used for LSP fiber reconstruction and MR neurography (MRN).The fiber bundles reconstruction was optimized in terms of eliminating the muscle fibers contamination using the segmentation of cauda equina, the effects of the normalized quantitative anisotropy (NQA) and angular threshold on reconstruction of the LSP. In this study, the NQA parameter has been used for fiber tracking instead of fractional anisotropy (FA) and the regions of interest positioning was precisely adjusted bilaterally and symmetrically in each individual subject.The diffusion data were processed in individual L3-S2 nerve fibers using the generalized Q-sampling imaging algorithm. Data (mean FA, mean diffusivity, axial diffusivity and radial diffusivity, and normalized quantitative anisotropy) were statistically analyzed using the linear mixed-effects model. The MR neurography was performed in MedINRIA and post-processed using the maximum intensity projection method to demonstrate LSP tracts in multiple planes.FA values significantly decreased towards the sacral region (P < .001); by contrast, mean diffusivity, axial diffusivity, radial diffusivity and NQA values significantly increased towards the sacral region (P < .001).Fiber tractography of the LSP was feasible in all examined subjects and closely corresponded with the nerves visible in the maximum intensity projection images of MR neurography. Usage of NQA instead of FA in the proposed algorithm enabled better separation of muscle and nerve fibers.The presented algorithm yields a high quality reconstruction of the LSP bundles that may be helpful both in research and clinical practice., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Purpose: To characterize the relationship between superparamagnetic ferritin-bound iron and diffusion tensor scalars in vitro, and validate the results in vivo., Materials and Methods: The in vitro model consisted of a series of 40-mL 1.1% agarose gels doped with ferritin covering and exceeding those concentrations normally found within healthy human gray matter. Additionally, regions of interest were placed in the caudate, putamen, and globus pallidus of 29 healthy volunteer subjects 19-80 years of age. Carr-Purcell-Meiboom-Gill sequence (CPMG) and diffusion tensor imaging (DTI) data were collected at 1.5 Tesla (T) and 3T in vitro, and at 1.5T in vivo., Results: In vitro, linear relationships were observed between ferritin-bound iron concentration, R2 (1/T2 ) and 1/SNR. Eigenvalue repulsion with increasing R2 (decreasing SNR) was reflected in an artifactual increase of fractional anisotropy. In vivo, similar relationships were observed, with mean diffusivity also decreasing linearly with increasing R2 . Lambda 3 showed the strongest correlation with R2 both in vitro and in vivo., Conclusion: The observation that DTI metrics correlate with ferritin-bound iron is an important consideration in the design and interpretation of studies exploring the diffusion characteristics of gray matter regions, especially in studies focused on adolescence as well as diseases associated with altered brain-iron load such as pantothenate kinase-associated neurodegeneration, Huntington disease and multiple system atrophy., (Copyright © 2013 Wiley Periodicals, Inc.)

The volume reduction of the gray matter structures in patients with Alzheimer's disease is often accompanied by an asymmetric increase in the number of white matter fibers located close to these structures. The present study aims to investigate the white matter structure changes in the motor basal ganglia in Alzheimer's disease patients compared to healthy controls using diffusion tensor imaging. The amounts of tracts, tract length, tract volume, quantitative anisotropy, and general fractional anisotropy were measured in ten patients with Alzheimer's disease and ten healthy controls. A significant decrease in the number of tracts and general fractional anisotropy was found in patients with Alzheimer's disease compared to controls in the right caudate nucleus, while an increase was found in the left and the right putamen. Further, a significant decrease in the structural volume of the left and the right putamen was observed. An increase in the white matter diffusion tensor imaging parameters in patients with Alzheimer's disease was observed only in the putamen bilaterally. The right caudate showed a decrease in both the diffusion tensor imaging parameters and the volume in Alzheimer's disease patients. The right pallidum showed an increase in the diffusion tensor imaging parameters but a decrease in volume in Alzheimer's disease patients. [ABSTRACT FROM AUTHOR]

The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects. Measurement(s) Diffusion Weighted Imaging Technology Type(s) Magnetic Resonance Imaging Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Czech Republic [ABSTRACT FROM AUTHOR]

Abstract: For the purpose of DT-MRI data visualization, an algorithm based on a numerical model of texture diffusion is proposed. As a prerequisite of entering clinical use, its parameters need to be adjusted properly so that the procedure gives satisfactory results with limited computational resources and time available. This contribution introduces the principles of the method and reports on the results of extensive computational studies aimed at finding optimal settings of the numerical scheme and model parameters with respect to visualization purposes. Total variation is used as a measure of visual quality of the produced images. Further, we provide evidence that using the algorithm is fully feasible on state of the art hardware. Finally, high resolution visualizations based on real data are demonstrated. [Copyright &y& Elsevier]