المؤلفون: Warrington, S, Bryant, KL, Khrapitchev, AA, Sallet, J, Charquero-Ballester, M, Douaud, G, Jbabdi, S, Mars, RB, Sotiropoulos, SN

الوصف: We present a new toolbox and library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=sharebioRxiv::9705540ef3968980ec7e4dcb06bebc67Test

المؤلفون: Uğurbil, K1, Xu, J, Auerbach, Ej, Moeller, S, Vu, At, Duarte Carvajalino JM, Lenglet, C, Wu, X, Schmitter, S, Van de Moortele PF, Strupp, J, Sapiro, G, De Martino, F, Wang, D, Harel, N, Garwood, M, Chen, L, Feinberg, Da, Smith, Sm, Miller, Kl, Sotiropoulos, Sn, Jbabdi, S, Andersson, Jl, Behrens, Te, Glasser, Mf, Van Essen DC, Yacoub, E, DE PASQUALE, Francesco, WU Minn HCP Consortium

المساهمون: Cognitive Neuroscience, RS: FPN CN 2

المصدر: Neuroimage, 80, 80-104. Elsevier Science

مصطلحات موضوعية: Models, Anatomic, Computer science, Cognitive Neuroscience, Models, Neurological, Article, Spatio-Temporal Analysis, Models, medicine, Connectome, Functional mr, Animals, Humans, Computer vision, Human Connectome Project, Resting state fMRI, medicine.diagnostic_test, business.industry, Functional connectivity, Brain, Diffusion Magnetic Resonance Imaging, Image Enhancement, Nerve Net, Neurology, Anatomic, Magnetic resonance imaging, Human brain, Mr imaging, Functional imaging, medicine.anatomical_structure, Temporal resolution, Neurological, Artificial intelligence, business, Diffusion MRI, Tractography

الوصف: The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce '. functional connectivity'; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3. T, leading to whole brain coverage with 2. mm isotropic resolution in 0.7. s for fMRI, and 1.25. mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7. T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure. © 2013 Elsevier Inc.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8af9f69f18413eb64ad94b6400dd526fTest
https://doi.org/10.1016/j.neuroimage.2013.05.012Test

المؤلفون: Hernández, M, Guerrero, GD, Cecilia, JM, García, JM, Inuggi, A, Sotiropoulos, SN, Stotzka, R, Schiffers, M, Cotronis, Y

المساهمون: Stotzka, R, Schiffers, M, Cotronis, Y

المصدر: PDP

مصطلحات موضوعية: medicine.diagnostic_test, Computer science, Central nervous system, Magnetic resonance imaging, Bioinformatics, computer.software_genre, medicine.anatomical_structure, Kernel (image processing), Voxel, medicine, computer, Algorithm, Diffusion MRI, Tractography

الوصف: Diffusion Weighted Magnetic Resonance Imaging (DWMRI) and tractography approaches are the only tools that can be utilized to estimate structural connections between different brain areas, non-invasively and in-vivo. A first step that is commonly utilized in these techniques includes the estimation of the underlying fibre orientations and their uncertainty in each voxel of the image. A popular method to achieve that is implemented in the FSL software, provided by the FMRIB Centre at University of Oxford, and is based on a Bayesian inference framework. Despite its popularity, the approach has high computational demands, taking normally more than 24 hours for analyzing a single subject. In this paper, we present a GPU-optimized version of the FSL tool that estimates fibre orientations. We report up to 85x of speed-up factor between the GPU and its sequential counterpart CPU-based version. © 2012 IEEE.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e1fe6e13a208d3ee8e166ca6d169d2cTest
https://doi.org/10.1109/pdp.2012.46Test

المؤلفون: Canales-Rodríguez, E, Daducci, A, Sotiropoulos, SN, Caruyer, E, Aja-Fernández, S, Radua, J, Mendizabal, Y, Iturria-Medina, Y, Melie-García, L, Alemán-Gómez, Y, Thiran, J, Sarró, S, Pomarol-Clotet, E, Salvador, R

الوصف: Due to a higher capability in resolving white matter fiber crossings, Spherical Deconvolution (SD) methods have become very popular in brain fiber-tracking applications. However, while some of these estimation algorithms assume a central Gaussian distribution for the MRI noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique intended to deal with realistic MRI noise. The algorithm relies on a maximum a posteriori formulation based on Rician and noncentral Chi likelihood models and includes a total variation (TV) spatial regularization term. By means of a synthetic phantom contaminated with noise mimicking patterns generated by data processing in multichannel scanners, the performance of RUMBA-SD is compared to that of other well-established SD methods (i.e., CSD and dRL-SD). The inclusion of proper likelihood models and TV regularization in RUMBA-SD leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and an increased robustness to noise. Finally, the proposed method is also validated in human brain data, producing the most stable fiber reconstructions in front of differing noise types and diffusion schemes based on a small number of gradient directions.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::97dccbd09e92c1be827928cceffad98eTest
http://ora.ox.ac.uk/objects/uuid:9f4185ae-4432-47c7-abc1-d8a3a48837aaTest

المؤلفون: Sotiropoulos, SN, Moeller, S, Jbabdi, S, Xu, J, Andersson, J, Auerbach, E, Yacoub, E, Feinberg, D, Setsompop, K, Wald, L, Behrens, T, Ugurbil, K, Lenglet, C

مصطلحات موضوعية: Sum of Squares, SENSE, Non-central-X, Rician, Magnitude Image, Tractography

الوصف: PURPOSE: To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation. THEORY AND METHODS: It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on the properties of the reconstructed magnitude image. Using a root-sum-of-squares approach results in a magnitude signal that follows an effective noncentral-χ distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest. RESULTS: In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fiber orientations, both for model-based and model-free approaches, when modern 32-channel coils are used. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, root-sum-of-squares can cause excessive overfitting and reduced precision in orientation estimation compared with the SENSE-based approach. CONCLUSION: These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition.

وصف الملف: PDF

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::cac30616565915c7884ebe476ab49875Test
https://nottingham-repository.worktribe.com/file/719347/1/nihms428785.pdfTest