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DT-MRI denoising and neuronal fiber tracking

T. McGraw, B.C. Vemuri, Y. Chen, M. Rao, T. Mareci
2004 Medical Image Analysis  
Neuronal fibers are then traced by calculating the integral curves of this vector field.  ...  The automatic fiber tract mapping problem will be solved in two phases, namely a data smoothing phase and a fiber tract mapping phase.  ...  In Section 4 we present the process of tracking neuronal fiber bundles through the restored DT-MRI data. Previous methods of fiber tracking will be discussed.  ... 
doi:10.1016/j.media.2003.12.001 pmid:15063860 fatcat:f55moj2dn5hs3hjehz6aes2oqy

Variational frameworks for DT-MRI estimation, regularization and visualization

Tschumperle, Deriche
2003 Proceedings Ninth IEEE International Conference on Computer Vision  
We illustrate how our complete DT-MRI processing pipeline can be successfully used to construct and draw fiber bundles in the white matter of the brain, from a set of noisy raw MRI images.  ...  We address three crucial issues encountered in DT-MRI (Diffusion Tensor Magnetic Resonance Imaging) : diffusion tensor Estimation, Regularization and fiber bundle Visualization.  ...  DT-MRI is then particularly well adapted to study the neuron connectivities within white matter, by tracking the fiber directions given pointwise by the principal eigenvector u(x, y, z) of the tensor T  ... 
doi:10.1109/iccv.2003.1238323 dblp:conf/iccv/TschumperleD03 fatcat:zjwyzjika5c53gy62inr3iebci

Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain

Celine Delettre, Arnaud Messé, Leigh-Anne Dell, Ophélie Foubet, Katja Heuer, Benoit Larrat, Sebastien Meriaux, Jean-Francois Mangin, Isabel Reillo, Camino de Juan Romero, Victor Borrell, Roberto Toro (+1 others)
2019 Network Neuroscience  
Diffusion MRI tractography offers the unique opportunity to investigate the brain fiber architecture in vivo and noninvasively. However, its reliability is still highly debated.  ...  These results are comparable to findings reported in mouse and monkey, increasing the confidence in diffusion MRI tractography results.  ...  performance of three diffusion tractography algorithms, namely DT, CSD, and msmt CSD, using both deterministic and probabilistic tracking.  ... 
doi:10.1162/netn_a_00098 pmid:31637337 pmcid:PMC6777980 fatcat:gmdeum7unzhmlhq3rdqdua4rci

Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain [article]

Celine Delettre, Arnaud Messe, Leigh-Anne Dell, Ophelie Foubet, Katja Heuer, Benoit Larrat, Sebastien Meriaux, Jean-Francois Mangin, Isabel Reillo, Camino de Juan Romero, Victor Borrell, Roberto Toro (+1 others)
2019 bioRxiv   pre-print
Diffusion MRI tractography offers the unique opportunity to investigate the brain fiber architecture in vivo and non invasively. However, its reliability is still highly debated.  ...  These results are comparable to findings reported in mouse and monkey, increasing the confidence in diffusion MRI tractography results.  ...  Acknowledgements We gratefully acknowledge financial support by grants from the Deutsche Forschungsgemeinschaft (DFG): SFB 936/A1/Z3 and SPP 2041 / HI 1286/6-1, the Human Brain Project HBP-SGA2 (785907  ... 
doi:10.1101/517136 fatcat:afwxrn56afakdccqmjp7sf4p2e

Regularization of DT-MRI Using 3D Median Filtering Methods

Soondong Kwon, Dongyoun Kim, Bongsoo Han, Kiwoon Kwon
2014 Journal of Applied Mathematics  
DT-MRI (diffusion tensor magnetic resonance imaging) tractography is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of the principal eigenvectors  ...  Tractography based on DT-MRI is known to need many computations and is highly sensitive to noise. Hence, adequate regularization methods, such as image processing techniques, are in demand.  ...  Introduction DT-MRI tractography is a method of noninvasively tracing neuronal fiber bundles.  ... 
doi:10.1155/2014/285367 fatcat:4bcad4xb6rf7lf6jonyyq5lkae

Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom

Pierre Fillard, Maxime Descoteaux, Alvina Goh, Sylvain Gouttard, Ben Jeurissen, James Malcolm, Alonso Ramirez-Manzanares, Marco Reisert, Ken Sakaie, Fatima Tensaouti, Ting Yo, Jean-François Mangin (+1 others)
2011 NeuroImage  
As it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research.  ...  For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography,  ...  The MRI acquisitions were realized under the supervision of Cyril Poupon and Irina Kezele. The organizers would like to thank Dr.  ... 
doi:10.1016/j.neuroimage.2011.01.032 pmid:21256221 fatcat:jhmx3n4nmbeivc7h4b2pyiybbu

Quantifying nerve decussation abnormalities in the optic chiasm

Robert J. Puzniak, Khazar Ahmadi, Jörn Kaufmann, Andre Gouws, Antony B. Morland, Franco Pestilli, Michael B. Hoffmann
2019 NeuroImage: Clinical  
Here we used diffusion MRI (dMRI) for the in-vivo identification of the abnormally high proportion of crossing fibers found in the optic chiasm of people with albinism.  ...  We detected significant group differences in chiasmal crossing for both unfiltered DT (p = 0.014) and CSD tractograms (p = 0.0009) also reflected by AUC measures (for DT and CSD: 0.61 and 0.75, respectively  ...  A common tool for this purpose is diffusion MRI (dMRI), which uses random thermal motion of water molecules (Stejskal and Tanner, 1965) to identify markers of the neuronal tissue organization.  ... 
doi:10.1016/j.nicl.2019.102055 pmid:31722288 pmcid:PMC6849426 fatcat:d43tqj4ksfh3jlvjjjfklfvff4

Quantifying nerve decussation abnormalities in the optic chiasm [article]

Robert J. Puźniak, Khazar Ahmadi, Jörn Kaufmann, Andre Gouws, Antony B. Morland, Franco Pestilli, Michael B. Hoffmann
2019 biorxiv/medrxiv   pre-print
ROC analyses indicated good performance of dMRI-based discrimination (AUC for DT and CSD: 0.81 and 0.96, respectively).  ...  Results: We detected significant group differences in chiasmal crossing for both DT (p=0.018) and CSD (p=0.0003).  ...  A common tool for this purpose is diffusion MRI (dMRI), which uses random thermal motion of water molecules (Stejskal and Tanner, 1965) to identify markers of the neuronal tissue organization.  ... 
doi:10.1101/633347 fatcat:wmds246porgyhhigrylm3gfwte

A detailed analysis of anatomical plausibility of crossed and uncrossed streamline rendition of the dentato-rubro-thalamic tract (DRT(T)) in a commercial stereotactic planning system

Volker A. Coenen, Bastian E. Sajonz, Peter C. Reinacher, Christoph P. Kaller, Horst Urbach, M. Reisert
2021 Acta Neurochirurgica  
Six subjects had repeated MRI scans and therefore entered the analysis twice.  ...  Methods Distinct dMRI and anatomical magnetic resonance imaging (MRI) data of high and low quality from 9 subjects were used.  ...  Global tracking As opposed to a local walker-based tractography, global fiber tracking has a more concrete objective: It creates a fiber configuration that fits to the acquired diffusionweighted MRI data  ... 
doi:10.1007/s00701-021-04890-4 pmid:34181083 pmcid:PMC8437929 fatcat:b5afmfb2sbfc5omuynprxtwwly

Early micro‐ and macrostructure of sensorimotor tracts and development of cerebral palsy in high risk infants

Rahul Chandwani, Julia E. Kline, Karen Harpster, Jean Tkach, Nehal A. Parikh, The Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS) Group
2021 Human Brain Mapping  
We used the recently developed fixel-based (FB) analysis to compute fiber density (FD), fiber-bundle cross-section (FC), and combined fiber density and cross-section (FDC) for each tract.  ...  Diffusion and structural MRI were acquired at term.  ...  ACKNOWLEDGMENTS The authors sincerely thank the parents of infants that participated in our study and the Cincinnati Infant Neurodevelopment Early Predic-  ... 
doi:10.1002/hbm.25579 pmid:34322949 pmcid:PMC8410533 fatcat:klgnzmzfwbbe3c332zfwivsqyu

Tracking the visual system—from the optic chiasm to primary visual cortex

Robert J. Puzniak, Gokulraj T. Prabhakaran, Lars Buentjen, Friedhelm C. Schmitt, Michael B. Hoffmann
2020 Zeitschrift für Epileptologie  
As a result, the reader will become familiar both with the ideas and challenges of imaging the visual system using dMRI and their relevance for planning the intervention.  ...  Such risk can be minimized by the identification and localization of visual pathways using diffusion magnetic resonance imaging (dMRI).  ...  Overview of required MRI data The optimal MRI dataset acquired for the purpose of tracking of the visual system should comprise (a) T1-weighted (.  ... 
doi:10.1007/s10309-020-00384-y fatcat:rmjip6h34jhefd53oldzkx36tu

Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

Alberto Santamaría-Pang, Paul Hernandez-Herrera, Manos Papadakis, Peter Saggau, Ioannis A. Kakadiaris
2015 Neuroinformatics  
The challenges faced in analyzing optical imaging data from neurons include a low signal-to-noise ratio of the acquired images and the multiscale nature of the tubular structures that range in size from  ...  The key aspects of this approach are: (i) detection of neuronal dendrites through learning 3D tubular models, and (ii) skeletonization by a new algorithm using a morphology-guided deformable model for  ...  Acknowledgments We wish to thank all of the members of the ORION team (Computational Biomedicine Lab 2014) and especially Costa M. Colbert, Yong Liang, and Bradley E. Losavio.  ... 
doi:10.1007/s12021-014-9253-2 pmid:25631538 fatcat:u2hcifna2vgc5ivgktr7gmdtlq

Variational multiple-tensor fitting of fiber-ambiguous diffusion-weighted magnetic resonance imaging voxels

Ofer Pasternak, Yaniv Assaf, Nathan Intrator, Nir Sochen
2008 Magnetic Resonance Imaging  
The framework is designed to address fiber ambiguity caused by a number of neuronal fiber compartments residing in the same voxel.  ...  We analyze fiber compartment separation capabilities on a synthetic model of crossing fibers and on brain areas known to have crossing fibers.  ...  for Brain Research of Tel Aviv University; The Israel Academy of Sciences; Israel Ministry of Science; and the Tel Aviv University Research Authority.  ... 
doi:10.1016/j.mri.2008.01.006 pmid:18524529 fatcat:nxhhgdi2ergllpgfqvq6nyshie

Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation

Ying-Qiu Zheng, Yu Zhang, Yvonne Yau, Yahar Zeighami, Kevin Larcher, Bratislav Misic, Alain Dagher, Henry Kennedy
2019 PLoS Biology  
The model also demonstrates that SNCA and GBA transcription influence α-synuclein concentration and local regional vulnerability.  ...  The dynamic model replicates the spatial and temporal patterning of empirical atrophy in PD and implicates the substantia nigra as the disease epicenter.  ...  The goal of this normalization is to correct for the bias toward large regions and long fibers inherent in the fiber tracking algorithms.  ... 
doi:10.1371/journal.pbio.3000495 pmid:31751329 pmcid:PMC6894889 fatcat:72yvdmizxfflvlupudtllga3cq

Towards HCP-Style Macaque Connectomes: 24-Channel 3T Multi-Array Coil, MRI Sequences and Preprocessing

Joonas A. Autio, Matthew F. Glasser, Takayuki Ose, Chad J. Donahue, Matteo Bastiani, Masahiro Ohno, Yoshihiko Kawabata, Yuta Urushibata, Katsutoshi Murata, Kantaro Nishigori, Masataka Yamaguchi, Yuki Hori (+9 others)
2020 NeuroImage  
In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI).  ...  However, the tools and methods for noninvasive image acquisition (e.g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans.  ...  of neurite 856 fiber orientations, a ratio between tangential and radial fibers (Fukutomi et al., 2018) .  ... 
doi:10.1016/j.neuroimage.2020.116800 pmid:32276072 pmcid:PMC7116593 fatcat:nnp5sjz7onabpklejcev6mprji
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