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High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity
Abstract
Magnetic resonance (MR) diffusion tensor imaging (DTI) can resolve the white matter fiber orientation within a voxel provided that the fibers are strongly aligned. However, a given voxel may contain a distribution of fiber orientations due to, for example, intravoxel fiber crossing. The present study sought to test whether a geodesic, high b-value diffusion gradient sampling scheme could resolve multiple fiber orientations within a single voxel. In regions of fiber crossing the diffusion signal exhibited multiple local maxima/minima as a function of diffusion gradient orientation, indicating the presence of multiple intravoxel fiber orientations. The multimodality of the observed diffusion signal precluded the standard tensor reconstruction, so instead the diffusion signal was modeled as arising from a discrete mixture of Gaussian diffusion processes in slow exchange, and the underlying mixture of tensors was solved for using a gradient descent scheme. The multitensor reconstruction resolved multiple intravoxel fiber populations corresponding to known fiber anatomy. Ma
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“The DTI model cannot differentiate these components or even crossing fibers (Tuch et al., 2002; Jbabdi et al., 2010), which are estimated to occur in up to 90% of WM voxels at the typical dMRI resolution (Descoteaux et al., 2009; Jeurissen et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Tuch et al., 2002
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Recent advances in diffusion MRI modeling: Angular and radial reconstruction
Assemlal, H.-E., Tschumperlé, D., Brun, L., & Kaleem Siddiqi, A. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.02.002 · Cited by 104
“, 2002), or is chosen by a statistical criterion (Parker and Alexander, 2003; Tuch et al., 2002; Behrens et al., 2007).”
BackgroundMethodInfluentialMakris, N., Preti, M. G., Wassermann, D., Yogesh Rathi, A., Papadimitriou, G., Yergatian, C., Dickerson, B. C., Shenton, M. E., & Kubicki, M. R. (2013) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9235-2 · Cited by 106
“2012) using DSI tractography (Tuch et al. 2002; Wedeen et al. 2008) in six subjects as well as the publicly available NTU-90 DSI atlas (Yeh and Tseng 2011) and complemented by fiber microdissection in four subjects, demonstrated the MdLF connection between TP, STG and SPL (BA 7), precuneus (BA 7) and cuneus (CN, BA 18…”
Self-citationDeprez, S., Billiet, T., Sunaert, S. G., & Leemans, A. (2013) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9220-1 · Cited by 109
“For voxels that suffer from " partial voluming " (PV)—also known as " partial volume effect " , " powder averaging " , or " intra-voxel heterogeneity " —the observed diffusion signal is a combination of the signals associated with each tissue type (Frank 2001; Tuch et al. 2002).”
BackgroundVisualizing the human connectome
Margulies, D. S., Böttger, J., Watanabe, A., & Gorgolewski, K. J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.111 · Cited by 102
“To achieve a more complete map of the probability density function within each voxel, new methods such as high angular resolution diffusion imaging (HARDI) (Tuch et al., 2002) or diffusion spectrum imaging (DSI) (Wedeen et al., 2005) were developed to sample the three-dimensional probability distribution using scans f…”
InfluentialCan increased spatial resolution solve the crossing fiber problem for diffusion MRI?
Schilling, K., Gao, Y., Janve, V., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3787 · Cited by 102
Zavaliangos-Petropulu, A., Nir, T. M., Thomopoulos, S. I., Reid, R. I., Bernstein, M. A., Borowski, B., Jack Jr., C. R., Weiner, M. W., Jahanshad, N., & Thompson, P. M. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00002 · Cited by 101
“The DTI model cannot differentiate these components or even crossing fibers (Tuch et al., 2002; Jbabdi et al., 2010), which are estimated to occur in up to 90% of WM voxels at the typical dMRI resolution (Descoteaux et al., 2009; Jeurissen et al., 2013).”
Wang, H., Black, A. J., Zhu, J., Stigen, T., Al-Qaisi, M. K., Netoff, T. I., Abosch, A., & Akkin, T. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.005 · Cited by 101
“Diffusion magnetic resonance imaging (MRI) techniques (diffusion tensor imaging and high angular resolution diffusion imaging) utilizing the anisotropic diffusion of water molecules along the fiber tracts have been developed recently to noninvasively study brain connectivity (Basser et al., 1994; Conturo et al., 1999;…”
Budinger, T. F., & Bird, M. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.067 · Cited by 102
“The complex fibrous structure of white matter can be further resolved using methods of high angular resolution diffusion weighted imaging (HARDI) to determine the water displacement probability function (Tuch et al., 2002).”
Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review
Moura, L. M., Luccas, R., de Paiva, J. P. Q., Amaro, E., Leemans, A., Leite, C. D. C., Otaduy, M. C. G., & Conforto, A. B. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00445 · Cited by 102
“Beyond DTI-Based Tractography: HARDI Models High angular resolution diffusion imaging (HARDI) uses a larger number of diffusion gradient directions, often in combination with multiple b-values, to measure the diffusion signal (68).”
BackgroundDiffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review
Zhang, Y., & Burock, M. A. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.531993 · Cited by 105
High-resolution diffusion tensor imaging and tractography of the human optic chiasm at 9.4 T
Roebroeck, A. F., Galuske, R. A. W., Formisano, E., Chiry, O., Bratzke, H., Ronen, I., Kim, D., & Goebel, R. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.015 · Cited by 100
“However, DTI tractography is known to have limitations primarily resulting from partial volume averaging of complex fiber architecture (such as sharp curves and crossings) into single voxels (Alexander et al., 2001; Tuch et al., 2002, 2003).”
BackgroundYogarajah, M., Powell, R., Parker, G. J. M., Alexander, D. C., Thompson, P. J., Symms, M. R., Boulby, P. A., Wheeler‐Kingshott, C. A. M. G., Barker, G. J., Koepp, M. J., et al. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.12.046 · Cited by 98
The tensor distribution function
Leow, A. D., Zhu, S., Zhan, L., McMahon, K., de Zubicaray, G. I., Meredith, M., Wright, M. J., Toga, A. W., & Thompson, P. M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21852 · Cited by 101
Diffusion MRI in the brain – Theory and concepts
Tournier, J. (2019) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/j.pnmrs.2019.03.001 · Cited by 110
Yamamoto, A., Miki, Y., Urayama, S., Fushimi, Y., Okada, T., Hanakawa, T., Fukuyama, H., & Togashi, K. (2007) · PubMed Central · Cited by 100
Wilkins, B., Lee, N., Gajawelli, N., Law, M., & Leporé, N. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.060 · Cited by 96
“However, DTI is limited tomodeling a single-fiber orientation per voxel and is therefore incapable of resolving complex intra-voxel geometry such as crossingfibers (Alexander et al., 2002; Frank, 2001; Tuch et al., 2002), which are thought to occur in at least one-third of voxels in white-matter (Behrens et al.”
BackgroundMethodInfluentialSpectral decomposition of a 4th-order covariance tensor: Applications to diffusion tensor MRI
Basser, P. J., & Pajevic, S. (2006) · Signal Processing · doi:10.1016/j.sigpro.2006.02.050 · Cited by 99
“In DTI, the angular distribution of T r ^ D̄r ^ is often plotted in 3-dimensions, exhibiting a profile that can be ‘‘peanut’’ or ‘‘discoid’’ shaped in brain white matter [34,35].”
Resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging
Stämpfli, P., Jaermann, T., Crelier, G. R., Kollias, S., Valavanis, A., & Boesiger, P. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.09.027 · Cited by 100
Schilling, K. G., Janve, V. A., Gao, Y., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.022 · Cited by 99
“, 2005), multi-tensor (Tuch et al., 2002) or ball-and-stick models (Behrens et al.”
InfluentialInferring White Matter Geometry from Diffusion Tensor MRI: Application to Connectivity Mapping
Lenglet, C., Rachid Deriche, B., & Faugeras, O. D. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24673-2_11 · Cited by 97
“This is a case of high angular diffusion weighted imaging [21], [22] where the variance of the signal could give important information on the multimodal aspect of diffusion.”
BackgroundQuan, M., Lee, S.-H., Kubicki, M., Kikinis, Z., Rathi, Y., Seidman, L. J., Mesholam-Gately, R. I., Goldstein, J. M., McCarley, R. W., Shenton, M. E., et al. (2013) · Schizophrenia Research · doi:10.1016/j.schres.2012.11.028 · Cited by 97
“However, it is limited by a single tensor model, which does not take into account complex fiber configurations such as crossings and branchings (Tuch et al., 2002; Kubicki et al., 2011).”
BackgroundThe influence of construction methodology on structural brain network measures: A review
Qi, S., Meesters, S., Nicolay, K., Romeny, B. M. T. H., & Ossenblok, P. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.06.016 · Cited by 98
“For HARDI, there is no specialized report on the effect of he number of gradient directions on the structural brain network easures.”
BackgroundInfluentialMinati, L., & Węglarz, W. P. (2007) · Concepts in Magnetic Resonance Part A · doi:10.1002/cmr.a.20094 · Cited by 94
“[53]”
Deep Brain Stimulation for Essential Tremor: Targeting the Dentato-Rubro-Thalamic Tract?
Schlaier, J. R., Anthofer, J., Steib, K., Fellner, C., Rothenfusser, E., Brawanski, A. T., & Lange, M. (2014) · Neuromodulation Technology at the Neural Interface · doi:10.1111/ner.12238 · Cited by 96
“To solve the problem of branching, kissing or crossing fibers algorithms have been developed, like the mixture of tensors (30), higher rank tensor (33,34) ball-and-stick (35), Q-ball imaging algorithm (36,37) and diffusion spectrum imaging (38) They try to estimate”
BackgroundMethodCarballedo, A., Amico, F., Ugwu, I., Fagan, A. J., Fahey, C., Morris, D., Meaney, J. F., Leemans, A., & Frodl, T. (2012) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32060 · Cited by 95
“Another possible limitation is that with basic deterministic tracking it is impossible to accurately reconstruct all the major pathways, especially where fibers branch and cross [Tuch et al., 2002].”
BackgroundTraumatic brain injury, major depression, and diffusion tensor imaging: Making connections
Maller, J. J., Thomson, R. H., Lewis, P., Rose, S., Pannek, K., & Fitzgerald, P. B. (2010) · Brain Research Reviews · doi:10.1016/j.brainresrev.2010.04.003 · Cited by 100
Spinal Cord Imaging in Amyotrophic Lateral Sclerosis: Historical Concepts—Novel Techniques
Mendili, M. M. E., Querin, G., Bede, P., & Pradat, P. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00350 · Cited by 102
Stämpfli, P., Reischauer, C., Jaermann, T., Valavanis, A., Kollias, S., & Boesiger, P. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.025 · Cited by 95
“Thereby, higher order mathematical tensor models, as proposed in different studies (Frank, 2001, 2002; Tuch, 2004; Tuch et al., 2002, 2003), could provide a possible basis.”
BackgroundJoint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter
Reddy, C. P., & Yogesh Rathi, A. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00166 · Cited by 94
“However, it is well known that most of the white matter tissue in the brain has crossing fibers (Tuch et al., 2002), which must be taken into account for proper analysis of the estimated microstructural parameters.”
BackgroundCaeyenberghs, K., Leemans, A., Decker, C., Heitger, M. H., Drijkoningen, D., Linden, C. V., Sunaert, S. G., & Swinnen, S. P. (2012) · NeuroImage Clinical · doi:10.1016/j.nicl.2012.09.011 · Cited by 97
“In these regions, where the diffusion tensor model has been shown to be inadequate (Frank, 2002; Tuch et al., 2002), partial volume effects exist (Alexander et al., 2001; Vos et al., 2011) that can adversely affect the estimation of diffusivity metrics (Wheeler-Kingshott and Cercignani, 2009; Vos…”
Zhang, H., Wang, Y., Lu, T., Qiu, B., Tang, Y., Ou, S., Tie, X., Sun, C., Xu, K., & Wang, Y. (2013) · Neurosurgery · doi:10.1227/neu.0000000000000146 · Cited by 94
Computational analysis in epilepsy neuroimaging: A survey of features and methods
Kini, L. G., Gee, J. C., & Litt, B. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.02.013 · Cited by 96
“High angular resolution diffusion imaging (HARDI-DTI), which resolves multiple intravoxel fiber populations (Tuch et al., 2002; Behrens et al., 2003), can more accurately perform fiber mapping and allow for the evaluation of white matter abnormalities near lesions.”
BackgroundTime‐efficient and flexible design of optimized multishell HARDI diffusion
Hutter, J. M., Tournier, J., Price, A. N., Cordero‐Grande, L., Hughes, E., Malik, S., Steinweg, J. K., Bastiani, M., Sotiropoulos, S. N., Jbabdi, S., et al. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26765 · Cited by 93
“f1ðd2; rÞ 1⁄4 jbð~h; d2; rÞ b̂j |fflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflffl} Residual between desired and achieved b-value [9]”
BackgroundMR Diffusion Imaging in Ischemic Stroke
Fung, S. H., Roccatagliata, L., González, R. G., & Schaefer, P. W. (2011) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2011.03.001 · Cited by 92
Structural and effective brain connectivity underlying biological motion detection
Sokolov, A. A., Zeidman, P., Erb, M., Ryvlin, P., Friston, K. J., & Pavlova, M. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812859115 · Cited by 94
“4A), these insights may be attributable to the improved signal-to-noise ratio of the present HARDI dataset, related to the number of gradient directions and b values (65, 66).”
Calamante, F. (2019) · Diagnostics · doi:10.3390/diagnostics9030115 · Cited by 98
“A number of such models have been proposed, including multi-tensor models [23], spherical deconvolution [24], Q-ball imaging [25], persistent angular structure MRI [26], and diffusion spectrum imaging [27], among others (There is yet no general consensus as to which of these advanced models has the best performance, a…”
BackgroundReijmer, Y. D., Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., & Biessels, G. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044074 · Cited by 91
“In the past decade, several advanced approaches for characterizing the intra-voxel diffusion profile have been developed to overcome the limitations of the second-rank diffusion tensor model [5–11].”
BackgroundUtility of MRI, PET, and ictal SPECT in presurgical evaluation of non-lesional pediatric epilepsy
Juhász, C., & John, F. (2019) · Seizure · doi:10.1016/j.seizure.2019.05.008 · Cited by 91
“, tract-based spatial statistics [TBSS]) [120], as well as advanced approaches such as high angular resolution (HARDI) [121].”
MethodInfluentialDoes diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls
O’Donnell, L. J., & Pasternak, O. (2014) · Schizophrenia Research · doi:10.1016/j.schres.2014.09.007 · Cited by 94
“The tensor model is able to represent just one fiber tract orientation per voxel, an anatomical implausibility in much, if not most of the brain (Tuch et al., 2002).”
Yeh, F.-C. F., Tang, P., & Tseng, W. I. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.06.014 · Cited by 94
“Studies have used high angular resolution diffusion imaging (HARDI) (Tuch et al., 2002) and diffusion spectrum imaging (DSI) (Wedeen et al., 2005) tomodel the diffusion characteristics of axonal fibers.”
InfluentialVisualization of Cranial Nerves Using High-Definition Fiber Tractography
Yoshino, M., Abhinav, K., Yeh, F.-C. F., Panesar, S. S., Fernandes, D., Pathak, S., Gardner, P. A., & Fernandez-Miranda, J. C. (2016) · Neurosurgery · doi:10.1227/neu.0000000000001241 · Cited by 91
Visual Computing for Medicine: Theory, Algorithms, and Applications
Preim, B., & Botha, C. P. (2007) · Cited by 91
3D fiber tractography with susceptibility tensor imaging
Liu, C., Li, W., Wu, B. H., Jiang, Y., & Johnson, G. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.096 · Cited by 89
“STI tractography can be further extended to resolve fiber crossings (Basser et al., 2000; Frank, 2001; Lin et al., 2003; Tuch et al., 2002) by incorporatingmultiple tensors andhigher order tensors (Liu et al., 2004, 2010).”
BackgroundAn Introduction to Visualization of Diffusion Tensor Imaging and Its Applications
Vilanova, A., Zhang, S., Kindlmann, G., & Laidlaw, D. H. (2006) · Mathematics and visualization · doi:10.1007/3-540-31272-2_7 · Cited by 89
“To resolve the uncertainty in these areas, q-space spectral imaging [16] and other high angular resolution diffusion imaging methods [17, 18] have been explored.”
MethodAggarwal, M., Nauen, D. W., Troncoso, J. C., & Mori, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.053 · Cited by 89
“Techniques including diffusion tensor imaging (Basser et al., 1994), high angular resolution diffusion imaging (HARDI) (Tuch et al., 2002), and diffusion spectrum imaging (Wedeen et al., 2005), have been developed to infer tissue microstructural properties from the diffusion-driven NMR signal decay.”
Gaige, T. A., Benner, T., Wang, R., Wedeen, V. J., & Gilbert, R. (2007) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21022 · Cited by 89
“Two distinct fiber populations intersecting at an angle within a voxel will produce a major eigenvector at an angle between the actual fiber orientation vectors (34).”
BackgroundSelf-citationMincic, A. M. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.08.007 · Cited by 96
Evaluating the Accuracy of Diffusion MRI Models in White Matter
Rokem, A., Yeatman, J. D., Pestilli, F., Kay, K. N., Mezer, A., van der Walt, S., & Wandell, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123272 · Cited by 91
“2004 [24] Human Field-strength = 3T Resolution = 3.”
A review of diffusion tensor magnetic resonance imaging computational methods and software tools
Hasan, K. M., Walimuni, I. S., Abid, H., & Hahn, K. R. (2010) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2010.10.008 · Cited by 96
“Datadriven approaches with high angular resolution diffusion (HARDI) measurements with two b-factors may use spherical harmonic decomposition (SHD) [58-63] which is based on the expansion of the measured apparent diffusion coefficient data in terms of a complete orthonormal set (e.”
DT-MRI denoising and neuronal fiber tracking
McGraw, T., Vemuri, B. C., Chen, Y., Rao, M., & Mareci, T. H. (2004) · Medical Image Analysis · doi:10.1016/j.media.2003.12.001 · Cited by 88
“Frank (2002) and Tuch et al. (2002) have used such high angular resolution images to resolve multiple fibers in a single voxel.”
Brain anatomical networks in world class gymnasts: A DTI tractography study
Wang, B., Fan, Y., Lü, M., Li, S., Song, Z., Peng, X., Zhang, R., Lin, Q., Yong He, A., Wang, J., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.007 · Cited by 91
“…has prompted the development of several different measurement streams including diffusion spectrum imaging (DSI) (Wedeen et al., 2008) and a variety of high angular resolution diffusion imaging (HARDI) methods such as Q-ball imaging and the spherical deconvolutionmethod (Tuch et al., 2002).”
BackgroundSomatotopic Organization of Thalamocortical Projection Fibers as Assessed with MR Tractography
Yamada, K., Nagakane, Y., Yoshikawa, K., Kizu, O., Ito, H., Kubota, T., Akazawa, K., Oouchi, H., Matsushima, S., Nakagawa, M., et al. (2007) · Radiology · doi:10.1148/radiol.2423060297 · Cited by 87
A Probabilistic Model-Based Approach to Consistent White Matter Tract Segmentation
Clayden, J. D., Storkey, A., & Bastin, M. E. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.905826 · Cited by 88
“, [4]–[13]), the principle is broadly common: it consists of integrating the local white matter orientation information provided by a dMRI data set across the brain [3].”
BackgroundFADTTS: Functional analysis of diffusion tensor tract statistics
Hongtu Zhu, B., Kong, L., Li, R., Styner, M. A., Gerig, G., Lin, W., & Gilmore, J. H. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.075 · Cited by 87
“We are in the process of extending FADTTS to the analysis of high angular resolution diffusion image (HARDI), which is important for resolving the issue of fiber crossing (Lenglet et al., 2009; Tuch et al., 2002).”
MethodPhysics, Techniques and Review of Neuroradiological Applications of Diffusion Kurtosis Imaging (DKI)
Marrale, M., Collura, G., Brai, M., Toschi, N., Midiri, M., Tona, G. L., Casto, A. L., & Gagliardo, C. (2015) · Clinical Neuroradiology · doi:10.1007/s00062-015-0469-9 · Cited by 88
“Even in WM, the DTI model can fail if the tissue contains substantial crossing or diverging fibers [11, 12].”
Summers, P. E., Stämpfli, P., Jaermann, T., Kwieciński, S., & Kollias, S. (2006) · PubMed Central · Cited by 87
Abhinav, K., Yeh, F.-C. F., Mansouri, A., Zadeh, G. M., & Fernandez-Miranda, J. C. (2015) · Neuro-Oncology · doi:10.1093/neuonc/nov113 · Cited by 89
“A tensor model cannot describe multiple fiber conditions, and therefore the estimation of the axonal direction is inaccurate in the crossing fiber regions.(22,23) As mentioned above, the diffusivity measured by diffusion MRI is an average measurement of the underlying diffusion environment.”
BackgroundNing, L., Laun, F. B., Gur, Y., DiBella, E., Deslauriers‐Gauthier, S., Megherbi, T., Ghosh, A., Zucchelli, M., Menegaz, G., Fick, R., et al. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.10.012 · Cited by 84
“…(single b-value), e.g., using a fixed diffusion time and gradient strength but with varying gradient directions, see (Campbell et al., 2005; CohenAdad et al., 2011; Descoteaux et al., 2008; Gur et al., 2012; Michailovich and Rathi, 2010; Tournier et al., 2004; Tuch et al., 2002; Tuch, 2004).”
MethodCharacterizing the role of the structural connectome in seizure dynamics
Shah, P., Ashourvan, A., Mikhail, F., Pines, A. R., Kini, L. G., Oechsel, K. F., Das, S. R., Stein, J. M., Shinohara, R. T., Bassett, D. S., et al. (2019) · Brain · doi:10.1093/brain/awz125 · Cited by 94
Budinger, T. F., Bird, M. D., Frydman, L., Long, J. R., Mareci, T. H., Rooney, W. D., Rosen, B. R., Schenck, J. F., Schepkin, V. D., Sherry, A. D., et al. (2016) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-016-0561-4 · Cited by 83
“The complex fibrous structure of white matter can be further resolved using methods of high angular-resolution diffusion-weighted imaging (HARDI) to determine the water displacement probability function [59].”
MethodDiffusion Tensor Imaging of the Spinal Cord
Maier, S. E., & Mamata, H. (2005) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1340.011 · Cited by 83
Gibbons, E. K., Hodgson, K. K., Chaudhari, A. S., Richards, L. G., Majersik, J. J., Adluru, G., & DiBella, E. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27568 · Cited by 83
“7,8 Common sampling approaches in DSI include sampling along high angular resolution diffusion imaging (HARDI) shells, 9 q‐ball imaging patterns, 10 or a cube on a Cartesian grid.”
MethodCharacterization of U-shape streamline fibers: Methods and applications
Zhang, T., Chen, H., Guo, L., Li, K., Li, L., Zhang, S., Shen, D. G., Hu, X. P., & Liu, T. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.04.005 · Cited by 81
“The former two imaging techniques (DSI and HARDI) were developed in order to better resolve crossing fibers at the scale of single MRI voxels (Tuch et al., 2002; Wedeen et al., 2005; Wedeen et al., 2012, Ozarslan et al., 2003).”
MethodClose, T., Tournier, J., Calamante, F., Johnston, L. A., Mareels, I., & Connelly, A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.077 · Cited by 82
“However, for acquisitions where the b value is constant, such as HARDI (Tuch et al., 2002) or single diffusion tensor analysis, the user-defined parameters, d (mean diffusivity) and FA, can be adjusted to suit the b value used for the acquisition (Yoshiura et al.”
MethodLin, Z., Gong, T., Wang, K., Li, Z., He, H., Tong, Q., Feng, Y., & Zhong, J. (2019) · Medical Physics · doi:10.1002/mp.13555 · Cited by 82
Disruption of the ascending arousal network in acute traumatic disorders of consciousness
Snider, S. B., Bodien, Y. G., Bianciardi, M., Brown, E. N., Wu, O., & Edlow, B. L. (2019) · Neurology · doi:10.1212/wnl.0000000000008163 · Cited by 81
“Unlike conventional MRI and diffusion tensor imaging, high-angular-resolution diffusion imaging (HARDI) meets these methodologic requirements by measuring water diffusion along axonal pathways using a multidirectional model designed to detect crossing and branching axons.(12) HARDI thus provides an opportunity to mode…”
BackgroundYang, J. Y.-M., Yeh, C.-H., Poupon, C., & Calamante, F. (2021) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac0d90 · Cited by 80
Diffusion-Tensor Fiber Tractography: Intraindividual Comparison of 3.0-T and 1.5-T MR Imaging
Okada, T., Miki, Y., Fushimi, Y., Hanakawa, T., Kanagaki, M., Yamamoto, A., Urayama, S.-I., Fukuyama, H., Hiraoka, M., & Togashi, K. (2006) · Radiology · doi:10.1148/radiol.2382042192 · Cited by 80
Mapping connectivity in the developing brain
Dennis, E. L., & Thompson, P. M. (2013) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2013.05.007 · Cited by 79
“For these reasons, HARDI is better at resolving crossing fibers (such as the corpus callosum and the long association fibers), a major issue for DTI (Tuch et al., 2002).”
BackgroundOn Approximation of Orientation Distributions by Means of Spherical Ridgelets
Michailovich, O., & Yogesh Rathi, A. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2035886 · Cited by 76
“One of such techniques is high angular resolution diffusion imaging (HARDI), which measures the diffusion directly by sampling the diffusion signal over a spherical shell in diffusion wavevector space [9], [10], [13], [14].”
Mikuni, N., Okada, T., Nishida, N., Taki, J., Enatsu, R., Ikeda, A., Miki, Y., Hanakawa, T., Fukuyama, H., & Hashimoto, N. (2007) · Journal of neurosurgery · doi:10.3171/jns.2007.106.1.128 · Cited by 77
Metzler-Baddeley, C., Foley, S., de Santis, S., Charron, C., Hampshire, A., Caeyenberghs, K., & Jones, D. K. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01127 · Cited by 78
“Diffusion data were collected with a spin-echo echo-planar high-angular resolution diffusion imaging (Tuch et al., 2002) sequence with diffusion encoded along 60 isotropically distributed orientations according to an optimized gradient vector scheme (Jones, Horsfield, & Simmons, 1999) Pierpaoli, &…”
MethodClustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data
Román, C., Guevara, M., Valenzuela, R., Figueroa, M. E., Houenou, J., Duclap, D., Poupon, C., Mangin, J., & Guevara, P. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00073 · Cited by 77
“These images feature better quality and, together with a high-order local diffusion model (Tuch et al., 2002; Descoteaux et al., 2007), enable the identification of fiber crossing within a voxel, thus improving tractography results.”
BackgroundTwo-Dimensional Correlation of Isotropic and Directional Diffusion Using NMR
Martins, J. P. D. A., & Topgaard, D. (2016) · Physical Review Letters · doi:10.1103/physrevlett.116.087601 · Cited by 76
&NA; (2012) · Neurosurgery · doi:10.1227/neu.0b013e318270d9fb · Cited by 76
DT-MRI Fiber Tracking: A Shortest Paths Approach
Zalesky, A. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.923644 · Cited by 76
“, [7], [22], [28], [29], [33], [35], [39], [44], [45]); and the introduction of higher -value [24] diffusion-weighted imaging (DWI) sequences [48] covering an increased number of gradient directions.”
BackgroundWatts, R., Liston, C. M., Niogi, S. N., & Uluğ, A. M. (2003) · Mental Retardation and Developmental Disabilities Research Reviews · doi:10.1002/mrdd.10077 · Cited by 76
“This high angular resolution diffusion imaging ( Tuch et al., 2002 ) holds the promise to be able to determine the directions of multiple fibers within a single voxel, but also leads to increased acquisition times.”
BackgroundAydogan, D. B., Jacobs, R., Dulawa, S., Thompson, S. L., Francois, M. C., Toga, A. W., Dong, H., Knowles, J. A., & Shi, Y. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1663-8 · Cited by 72
Jensen, J. H., Glenn, G. R., & Helpern, J. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.049 · Cited by 75
“For strong diffusion weightings (i.e., high b-values), the angular dependence of the diffusionMRI (dMRI) signal intensity inwhitematter is sensitive to the complex geometries associatedwith intersecting axonal fiber bundles (Tuch et al., 2002).”
Mathematical description of q‐space in spherical coordinates: Exact q‐ball imaging
Canales-Rodríguez, E. J., Melie‐García, L., & Iturria‐Medina, Y. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21917 · Cited by 74
Shepherd, T. M., Özarslan, E., King, M. A., Mareci, T. H., & Blackband, S. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.04.210 · Cited by 75
“Alternative methods of measuring diffusion anisotropy, such as high angular resolution diffusion imaging (Tuch et al., 2002), may be required to further improve our understanding of those rat hippocampal regions where multiple coherent fiber pathways exist at the length scales of particular image resolutions.”
MethodTranscallosal connectivity of the human cortical motor network
Ruddy, K. L., Leemans, A., & Carson, R. G. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1274-1 · Cited by 75
“The diffusion tensor model that is typically applied to DW imaging data in this context is, however, inadequate for regions that contain complex architectures such as crossing fibres (Alexander et al. 2001; Jeurissen et al. 2013; Tuch et al. 2002).”
MethodTian, Q., Wintermark, M., Elias, W. J., Ghanouni, P., Halpern, C. H., Henderson, J. M., Huss, D., Goubran, M., Thaler, C. P., Airan, R. D., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.05.010 · Cited by 75
“Diffusion MRI tractography is a non-invasive technique that reconstructs three-dimensional models of white matter fiber pathways based on voxel-wise diffusion patterns (Basser et al., 1994; Pierpaoli and Basser, 1996; Tuch et al., 2002; Behrens et al., 2003a; Tournier et al., 2004; Wedeen et al., 2005; Jbabdi et al.,…”
InfluentialWu, X., Schmitter, S., Auerbach, E. J., Moeller, S., Uǧurbil, K., & de Moortele, P. V. (2013) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24828 · Cited by 74
Bourbon‐Teles, J., Bells, S., Jones, D. K., Coulthard, E. J., Rosser, A., & Metzler‐Baddeley, C. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.05.042 · Cited by 74
“The present study investigated HD-related differences in WM microstructure by utilizing both high angular resolution diffusion imaging (HARDI) (Tuch et al., 2002) and quantitative magnetization transfer imaging (qMT) (Henkelman et al., 1993, 2001) which provides improved myelin specificity compared to diffusion MRI. qMT e…”
MethodInfluentialStructural Abnormalities in Bipolar Euthymia: A Multicontrast Molecular Diffusion Imaging Study
Canales-Rodríguez, E. J., Pomarol-Clotet, E., Radua, J., Sarró, S., Alonso-Lana, S., Del Mar Bonnín, C., Goikolea, J. M., Maristany, T., García-Álvarez, R., Vieta, E., et al. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.09.027 · Cited by 72
“It is well known that the DTI model, which accounts only for Gaussian processes, does not adequately describe diffusion in areas of kissing, branching, and crossing fibers (16), and FA alone does not explain all possible morphologic abnormalities.”
Riffert, T. W., Schreiber, J., Anwander, A., & Knösche, T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.015 · Cited by 74
“…the number of tensor components (eg. to two components, Parker et al., 2003, Caan et al., 2010), incorporating physiological constraints (Tuch et al., 2002), reducing the complexity of the model by only allowing identical prolate tensors (Tabelow et al., 2012), stabilizing the problem…”
Potential and limitations of diffusion MRI tractography for the study of language
Campbell, J. S. W., & Pike, G. B. (2013) · Brain and Language · doi:10.1016/j.bandl.2013.06.007 · Cited by 77
Diffusion-weighted magnetic resonance imaging fibre tracking using a front evolution algorithm
Tournier, J., Calamante, F., Gadian, D. G., & Connelly, A. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00236-2 · Cited by 73
Tractography for Surgical Neuro-Oncology Planning: Towards a Gold Standard
Panesar, S. S., Abhinav, K., Yeh, F.-C. F., Jacquesson, T., Collins, M. K., & Fernandez-Miranda, J. C. (2018) · Neurotherapeutics · doi:10.1007/s13311-018-00697-x · Cited by 71
Tractography segmentation using a hierarchical Dirichlet processes mixture model
Wang, X., Grimson, W. E. L., & Westin, C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.050 · Cited by 71
“There are more general models such as High-Angular-Resolution Diffusion-Image (HARDI) (Tuch et al., 2002), Q-ball (Tuch, 2004) and Diffusion Spectrum Imaging (DSI) (Wedeen et al., 2005) etc. Extracting connectivity information from DT-MRI, termed " tractography " (Basser et al., 2000; Conturo et…”
MethodCanales-Rodríguez, E. J., Daducci, A., Sotiropoulos, S. N., Caruyer, E., Aja‐Fernández, S., Raduà, J., Mendizabal, J. M. Y., Iturria‐Medina, Y., Melie‐García, L., Alemán‐Gómez, Y., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138910 · Cited by 71
“In this model the diffusion process taking place inside each compartment of parallel fibers is described by a second-order self-diffusion tensor [6].”
BackgroundMethodInfluentialMicro-structural assessment of short term plasticity dynamics
Tavor, I., Hofstetter, S., & Assaf, Y. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.050 · Cited by 75
“Severalmodels have been suggested to allowamore comprehensive model of diffusion in the tissue than the diffusion tensor (Alexander et al., 2010; Barazany et al., 2009; Tuch et al., 2002).”
BackgroundEstimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging
Axer, M., Strohmer, S., Gräßel, D., Bücker, O., Dohmen, M., Reckfort, J., Zilles, K., & Amunts, K. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00040 · Cited by 73
Whole mouse brain structural connectomics using magnetic resonance histology
Wang, N., Anderson, R. J., Badea, A., Cofer, G., Dibb, R., Qi, Y., & Johnson, G. A. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1750-x · Cited by 71
Diffusion MR imaging characteristics of the developing primate brain
Kroenke, C., Bretthorst, G., Inder, T., & Neil, J. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.045 · Cited by 72
“Keywords: Magnetic resonance imaging; Diffusion anisotropy; Baboon brains; Brain development”
BackgroundMethodInfluentialEstimating Diffusion Propagator and Its Moments Using Directional Radial Basis Functions
Ning, L., Westin, C., & Yogesh Rathi, A. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2418674 · Cited by 70
Probabilistic white matter fiber tracking using particle filtering and von Mises–Fisher sampling
Zhang, F., Hancock, E. R., Goodlett, C., & Gerig, G. (2008) · Medical Image Analysis · doi:10.1016/j.media.2008.05.001 · Cited by 75
“To overcome this difficulty, a multi-tensor (or multi-compartment) extension of the single diffusion tensor model has been proposed (Tuch et al., 2002; Frank, 2001; Behrens et al., 2007).”
MethodNeher, P., Descoteaux, M., Houde, J., Stieltjes, B., & Maier-Hein, K. H. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.10.011 · Cited by 68
An Introduction to Computational Diffusion MRI: the Diffusion Tensor and Beyond
Alexander, D. C. (2006) · Mathematics and Visualization · doi:10.1007/3-540-31272-2_5 · Cited by 70
“Tuch [28] thresholds the correlation of the measurements with their predictions from a single-component model to decide whether to use one or two components.”
Background
Metadata
- Authors
- Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J.
- Affiliations
- Massachusetts General Hospital (US)
- Corresponding author
- Wedeen, Van J.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2002-09-26
- Volume
- 48
- Issue
- 4
- Pages
- 577-582
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2080198834
- Cited by
- 1,548
- References
- 27
- Citations by year
- 2026: 20 · 2025: 43 · 2024: 39 · 2023: 52 · 2022: 59 · 2021: 65 · 2020: 65 · 2019: 82 · 2018: 76 · 2017: 84 · 2016: 84 · 2015: 115 · 2014: 93 · 2013: 95 · 2012: 79
- FWCI
- 23.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Sol Goldman Charitable Trust
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- high angular resolution diffusion imaging
- MeSH terms
- Brain, Computer Simulation, Humans, Image Processing, Computer-Assisted, Male, Nerve Fibers, Anisotropy, Diffusion Magnetic Resonance Imaging
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