Cited by 1,880Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

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Q-ball imaging

Tuch, David S.·2004·Magnetic Resonance in Medicine

DOI 10.1002/mrm.20279

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Abstract

Magnetic resonance diffusion tensor imaging (DTI) provides a powerful tool for mapping neural histoarchitecture in vivo. However, DTI can only resolve a single fiber orientation within each imaging voxel due to the constraints of the tensor model. For example, DTI cannot resolve fibers crossing, bending, or twisting within an individual voxel. Intravoxel fiber crossing can be resolved using q-space diffusion imaging, but q-space imaging requires large pulsed field gradients and time-intensive sampling. It is also possible to resolve intravoxel fiber crossing using mixture model decomposition of the high angular resolution diffusion imaging (HARDI) signal, but mixture modeling requires a model of the underlying diffusion process.Recently, it has been shown that the HARDI signal can be reconstructed model-independently using a spherical tomographic inversion called the Funk-Radon transform, also known as the spherical Radon transform. The resulting imaging method, termed q-ball imaging, can resolve multiple intravoxel fiber orientations and does not require any assumptions on the diffusion process such as Gaussianity or multi-Gaussianity. The present paper reviews the theory of q-ball imaging and describes a simple linear matrix formulation for the q-ball reconstruction based on spherical radial basis function interpolation. Open aspects of the q-ball reconstruction algorithm are discussed.

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The Physics of Liquid Crystals
de Gennes, P. G., Prost, J., & Pelcovits, R. A. (1995) · Physics Today · doi:10.1063/1.2808028 · Cited by 9,810
Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient
Stejskal, E. O., & Tanner, J. E. (1965) · The Journal of Chemical Physics · doi:10.1063/1.1695690 · Cited by 8,239
MR diffusion tensor spectroscopy and imaging
Basser, P. J., Mattiello, J., & LeBihan, D. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80775-1 · Cited by 5,970
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,628
Principles of Nuclear Magnetic Resonance Microscopy
Callaghan, P. T. (1991) · doi:10.1093/oso/9780198539445.001.0001 · Cited by 3,614
Characterization and propagation of uncertainty in diffusion-weighted MR imaging
Behrens, T. E. J., Woolrich, M. W., Jenkinson, M., Johansen‐Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., & Smith, S. M. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10609 · Cited by 3,038
Diffusion tensor MR imaging of the human brain
Pierpaoli, C., Jezzard, P., Basser, P. J., Barnett, A., & Di Chiro, G. (1996) · Radiology · doi:10.1148/radiology.201.3.8939209 · Cited by 2,664
Fiber tracking: principles and strategies – a technical review
Mori, S., & van Zijl, P. C. M. (2002) · NMR in Biomedicine · doi:10.1002/nbm.781 · Cited by 2,019
Looking into the functional architecture of the brain with diffusion MRI.
Le Bihan, D. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1119 · Cited by 1,571
High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity
Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10268 · Cited by 1,548
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Fasshauer GE, 1998, Mathematical Methods for Curves and Surfaces II, Innovations in Applied Mathematics, 117

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Authors: Tuch, D. S.
Affiliations: Massachusetts General Hospital (US)
Corresponding author: Tuch, David S.
Venue: Magnetic Resonance in Medicine
Publisher: Wiley
Published: 2004-11-23
Volume: 52
Issue: 6
Pages: 1358-1372
ISSN: 0740-3194 (ISSN-L)
1522-2594
DOI: 10.1002/mrm.20279
OpenAlex: W1988954604
Cited by: 1,880
References: 43
Citations by year: 2026: 19 · 2025: 39 · 2024: 55 · 2023: 47 · 2022: 57 · 2021: 80 · 2020: 86 · 2019: 81 · 2018: 100 · 2017: 106 · 2016: 97 · 2015: 116 · 2014: 126 · 2013: 148 · 2012: 141
FWCI: 38.55
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Magnetic Resonance in Medicine (publishedVersion)
PubMed (publishedVersion)
Funders: National Institute of Neurological Disorders and Stroke (K25NS046532, NS46532)
National Center for Research Resources (RR 14075, P41RR014075)
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
Physical Sciences › Chemistry › Spectroscopy › NMR spectroscopy and applications
Keywords: Q-ball imaging, spherical Radon transform, high angular resolution diffusion imaging, fiber orientation distribution, diffusion MRI reconstruction
MeSH terms: Algorithms, Brain Mapping, Humans, Image Processing, Computer-Assisted, Diffusion Magnetic Resonance Imaging

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