Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates.
apparent differences
axisymmetric DKI
bias
biophysical parameters
standard DKI
white matter
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
02 Feb 2024
02 Feb 2024
Historique:
revised:
12
01
2024
received:
07
06
2023
accepted:
15
01
2024
medline:
3
2
2024
pubmed:
3
2
2024
entrez:
2
2
2024
Statut:
aheadofprint
Résumé
The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation. Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model. The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects. For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Bundesministerium für Bildung und Forschung
ID : 01EW1711A
Organisme : Bundesministerium für Bildung und Forschung
ID : 01EW1711B
Organisme : Deutsche Forschungsgemeinschaft
ID : MO 2397/5-1
Organisme : Deutsche Forschungsgemeinschaft
ID : MO 2397/5-2
Organisme : Deutsche Forschungsgemeinschaft
ID : MO 2397/4-1
Organisme : Deutsche Forschungsgemeinschaft
ID : MO 2397/4-2
Informations de copyright
© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
Références
Coutu J-P, Chen JJ, Rosas HD, Salat DH. Non-Gaussian water diffusion in aging white matter. Neurobiol Aging. 2014;35:1412-1421.
Genç E, Fraenz C, Schlüter C, et al. Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nat Commun. 2018;9:1905.
Donat CK, Yanez Lopez M, Sastre M, et al. From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury. Brain. 2021;144:70-91.
Zhuo J, Xu S, Proctor JL, et al. Diffusion kurtosis as an in vivo imaging marker for reactive astrogliosis in traumatic brain injury. Neuroimage. 2012;59:467-477. Neuroergonomics: The human brain in action and at work.
Steven AJ, Zhuo J, Melhem ER. Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain. Am J Roentgenol. 2014;202:W26-W33.
Taha HT, Chad JA, Chen JJ. DKI enhances the sensitivity and interpretability of age-related DTI patterns in the white matter of UK biobank participants. Neurobiol Aging. 2022;115:39-49.
Hansen B, Shemesh N, Jespersen SN. Fast imaging of mean, axial and radial diffusion kurtosis. Neuroimage. 2016;142:381-393.
Hansen B, Khan AR, Shemesh N, et al. White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging. NMR Biomed. 2017;30:e3741.
Hansen B, Jespersen SN. Recent developments in fast kurtosis imaging. Front Phys. 2017;5:40.
Hansen B, Lund TE, Sangill R, Jespersen SN. Experimentally and computationally fast method for estimation of a mean kurtosis. Magn Reson Med. 2013;69:1754-1760.
Tabelow K, Mohammadi S, Weiskopf N, Polzehl J. POAS4SPM: a toolbox for SPM to denoise diffusion MRI data. Neuroinformatics. 2015;13:19-29.
Tournier J-D, Smith R, Raffelt D, et al. Mrtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage. 2019;202:116137.
Novikov DS, Veraart J, Jelescu IO, Fieremans E. Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. Neuroimage. 2018;174:518-538.
Alexander DC, Dyrby TB, Nilsson M, Zhang H. Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. 2019;32:e3841.
Novikov DS, Fieremans E, Jespersen SN, Kiselev VG. Quantifying brain microstructure with diffusion MRI: theory and parameter estimation. NMR Biomed. 2019;32:e3998.
Becker S, Tabelow K, Voss H, Anwander A, Heidemann R, Polzehl J. Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (poas). Med Image Anal. 2012;16:1142-1155.
Becker S, Tabelow K, Mohammadi S, Weiskopf N, Polzehl J. Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by mspoas. Neuroimage. 2014;95:90-105.
Hansen B, Jespersen SN. Data for evaluation of fast kurtosis strategies, b-value optimization and exploration of diffusion MRI contrast. Sci Data. 2016;3:160072.
Oeschger JM, Tabelow K, Mohammadi S. Axisymmetric diffusion kurtosis imaging with Rician bias correction: a simulation study. Magn Reson Med. 2022;89:787-799. doi:10.1002/mrm.29474
David G, Fricke B, Oeschger JM, et al. Acid: a comprehensive toolbox for image processing and modeling of brain, spinal cord, and post-mortem diffusion mri data. bioRxiv. 2023.
André ED, Grinberg F, Farrher E, et al. Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging. PloS One. 2014;9:1-15.
Callaghan MF, Josephs O, Herbst M, Zaitsev M, Todd N, Weiskopf N. An evaluation of prospective motion correction (pmc) for high resolution quantitative mri. Front Neurosci. 2015;9:1-9.
Tabelow K, Balteau E, Ashburner J, et al. Hmri - a toolbox for quantitative mri in neuroscience and clinical research. Neuroimage. 2019;194:191-210.
Lätt J, Nilsson M, Wirestam R, et al. Regional values of diffusional kurtosis estimates in the healthy brain. J Magn Reson Imaging. 2013;37:610-618.
Westin CF, Maier SE, Mamata H, Nabavi A, Jolesz FA, Kikinis R. Processing and visualization for diffusion tensor MRI. Med Image Anal. 2002;6:93-108.
Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage. 2011;58:177-188.
Tournier JD, Yeh C-H, Calamante F, Cho K-H, Connelly A, Lin C-P. Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. Neuroimage. 2008;42:617-625.
Veraart J, Nunes D, Rudrapatna U, et al. Noninvasive quantification of axon radii using diffusion MRI. Elife. 2020;9:e49855.
Manzano-Patron J-P, Moeller S, Andersson JL, Yacoub E, Sotiropoulos SN. Denoising diffusion MRI: considerations and implications for analysis. Imag Neurosci. 2023;2:1-29.
Hua K, Zhang J, Wakana S, et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage. 2008;39:336-347.
Jespersen SN, Olesen JL, Hansen B, Shemesh N. Diffusion time dependence of microstructural parameters in fixed spinal cord. Neuroimage. 2018;182:329-342.
Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods. 2020;344:108861.
Schurr R, Mezer AA. The glial framework reveals white matter fiber architecture in human and primate brains. Science. 2021;374:762-767.
Tabesh A, Jensen JH, Ardekani BA, Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med. 2011;65:823-836.
Nørhøj Jespersen S. White matter biomarkers from diffusion MRI. J Magn Reson. 2018;291:127-140.
Jeurissen B, Leemans A, Tournier J-D, Jones DK, Sijbers J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp. 2013;34:2747-2766. doi:10.1002/hbm.22099
Wu Y, Kim J, Chan S-T, et al. Comparison of image sensitivity between conventional tensor-based and fast diffusion kurtosis imaging protocols in a rodent model of acute ischemic stroke. NMR Biomed. 2016;29:625-630. doi:10.1002/nbm.3506
Mohammadi S, Möller HE, Kugel H, Müller DK, Deppe M. Correcting eddy current and motion effects by affine whole-brain registrations: evaluation of three-dimensional distortions and comparison with slicewise correction. Magn Reson Med. 2010;64:1047-1056.