scholarly journals A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis

2019 ◽  
Vol 26 (7) ◽  
pp. 774-785 ◽  
Author(s):  
Carmen Tur ◽  
Francesco Grussu ◽  
Ferran Prados ◽  
Thalis Charalambous ◽  
Sara Collorone ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Brain Connectivity ◽  
Diffusion Imaging ◽  
Orientation Distribution ◽  
Distribution Functions ◽  
Network Modularity ◽  
Clinical Measures ◽  
Network Metrics ◽  
Better Than ◽  
Single Shell

Background: The potential of multi-shell diffusion imaging to produce accurate brain connectivity metrics able to unravel key pathophysiological processes in multiple sclerosis (MS) has scarcely been investigated. Objective: To test, in patients with a clinically isolated syndrome (CIS), whether multi-shell imaging-derived connectivity metrics can differentiate patients from controls, correlate with clinical measures, and perform better than metrics obtained with conventional single-shell protocols. Methods: Nineteen patients within 3 months from the CIS and 12 healthy controls underwent anatomical and 53-direction multi-shell diffusion-weighted 3T images. Patients were cognitively assessed. Voxel-wise fibre orientation distribution functions were estimated and used to obtain network metrics. These were also calculated using a conventional single-shell diffusion protocol. Through linear regression, we obtained effect sizes and standardised regression coefficients. Results: Patients had lower mean nodal strength ( p = 0.003) and greater network modularity than controls ( p = 0.045). Greater modularity was associated with worse cognitive performance in patients, even after accounting for lesion load ( p = 0.002). Multi-shell-derived metrics outperformed single-shell-derived ones. Conclusion: Connectivity-based nodal strength and network modularity are abnormal in the CIS. Furthermore, the increased network modularity observed in patients, indicating microstructural damage, is clinically relevant. Connectivity analyses based on multi-shell imaging can detect potentially relevant network changes in early MS.

scholarly journals White Matter Fiber Tractography Using Nonuniform Rational B-Splines Curve Fitting

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Zhanxiong Wu ◽  
Dongnan Wu ◽  
Dong Xu
Keyword(s):  
Curve Fitting ◽  
Diffusion Imaging ◽  
Orientation Distribution ◽  
High Angular Resolution ◽  
Fiber Tractography ◽  
Nurbs Curve ◽  
B Splines ◽  
Neural Fiber ◽  
Clinical Measures ◽  
Anatomical Connection

The study of neural connectivity has grown rapidly in the past decade. Revealing brain anatomical connection improves not only clinical measures but also cognition understanding. In order to achieve this goal, we have to track neural fiber pathways first. Aiming to estimate 3D fiber pathways more accurately from orientation distribution function (ODF) fields, we presented a novel tracking method based on nonuniform rational B-splines (NURBS) curve fitting. First, we constructed ODF fields from high angular resolution diffusion imaging (HARDI) datasets using diffusion orientation transform (DOT) method. Second, under the angular and length constraints, the consecutive diffusion directions were extracted along each fiber pathway starting from a seed voxel. Finally, after the coordinates of the control points and their corresponding weights were determined, NURBS curve fitting was employed to track fiber pathways. The performance of the proposal has been evaluated on the tractometer phantom and real brain datasets. Based on several measure metrics, the resulting fiber pathways show promising anatomic consistency.

scholarly journals Diffusion MRI abnormalities detection with orientation distribution functions: A multiple sclerosis longitudinal study

2015 ◽  
Vol 22 (1) ◽  
pp. 114-123 ◽  
Author(s):  
Olivier Commowick ◽  
Adil Maarouf ◽  
Jean-Christophe Ferré ◽  
Jean-Philippe Ranjeva ◽  
Gilles Edan ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Longitudinal Study ◽  
Diffusion Mri ◽  
Orientation Distribution ◽  
Distribution Functions ◽  
Orientation Distribution Functions

scholarly journals Validating atlas-based lesion disconnectomics in multiple sclerosis: a retrospective multi-centric study

2021 ◽  
Author(s):  
Veronica Ravano ◽  
Michaela Andelova ◽  
Mario Joao Fartaria ◽  
Mazen Fouad A-Wali Mahdi ◽  
Benedicte Marechal ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Symptoms ◽  
Brain Connectivity ◽  
Diffusion Imaging ◽  
White Matter Lesions ◽  
Acquisition Time ◽  
Lesion Volume ◽  
Topological Graph ◽  
Embedding Technique ◽  
Graph Properties

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision. Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5T and 3T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. A graph embedding technique followed by dimensionality reduction found a topological organization that mirrored disability. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.

Textures Applied to Mechanical Processing Technology Assessment in Ancient Bronze

2005 ◽  
Vol 495-497 ◽  
pp. 719-724
Author(s):  
R.E. Bolmaro ◽  
B. Molinas ◽  
E. Sentimenti ◽  
A.L. Fourty
Keyword(s):  
Orientation Distribution ◽  
Distribution Functions ◽  
Archaeological Sites ◽  
Mechanical Processing ◽  
Post Processing ◽  
X Ray Diffraction ◽  
Mechanical Process ◽  
X Ray ◽  
Pole Figures ◽  
Microscopic Inspection

Some ancient metallic art craft, utensils, silverware and weapons are externally undistinguishable from modern ones. Not only the general aspect and shape but also some uses have not changed through the ages. Moreover, when just some small pieces can be recovered from archaeological sites, the samples can not easily be ascribed to any known use and consequently identified. It is clear that mechanical processing has changed along history but frequently only a "microscopic" inspection can distinguish among different techniques. Some bronze samples have been collected from the Quarto d’Altino (Veneto) archaeological area in Italy (paleovenetian culture) and some model samples have been prepared by a modern artisan. The sample textures have been measured by X-ray Diffraction techniques. (111), (200) and (220) pole figures were used to calculate Orientation Distribution Functions and further recalculate pole figures and inverse pole figures. The results were compared with modern forging technology results. Textures are able to discern between hammering ancient techniques for sheet production and modern industrial rolling procedures. However, as it is demonstrated in the present work, forgery becomes difficult to detect if the goldsmith, properly warned, proceeds to erase the texture history with some hammering post-processing. The results of this contribution can offer to the archaeologists the opportunity to take into consideration the texture techniques in order to discuss the origin (culture) of the pieces and the characteristic mechanical process developed by the ancient artisan. Texture can also help the experts when discussing the originality of a certain piece keeping however in mind the cautions indicated in this publication.

scholarly journals Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Shui-Hua Wang ◽  
Xianwei Jiang ◽  
Yu-Dong Zhang
Keyword(s):  
Multiple Sclerosis ◽  
Genetic Algorithm ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Adaptive Genetic Algorithm ◽  
Biorthogonal Wavelet ◽  
Wide Range ◽  
Wavelet Features ◽  
The Brain ◽  
Better Than

Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method.Methods: First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA.Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876.Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods.

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