Effects of signal averaging, gradient encoding scheme, and spatial resolution on diffusion kurtosis imaging: An empirical study using 7T MRI.
Diffusion kurtosis imaging
diffusion gradient encoding scheme
signal averaging
spatial resolution
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
15
01
2019
accepted:
04
04
2019
pubmed:
17
4
2019
medline:
24
11
2020
entrez:
17
4
2019
Statut:
ppublish
Résumé
Although diffusion gradient directions and b-values have been optimized for diffusion kurtosis imaging (DKI), little is known about the effect of signal averaging on DKI reliability. To evaluate how signal averaging influences the reliability of DKI indices using two gradient encoding schemes with three spatial resolutions. Prospective. Fifteen naïve Sprague-Dawley rats. DKI was performed at 7T using two schemes (30 directions with three b-values [30d-3b] and six directions with 15 b-values [6d-15b]), three resolutions, and eight repetitions. DKI reliability was assessed using voxelwise relative error (σ) and test-retest error of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) within gray matter (GM) and white matter (WM). The number of excitations (NEX) was optimized by considering DKI reliability. The influence of the partial volume effect (PVE) was also assessed. One-way analysis of variance. The 30d-3b scheme, compared with the 6d-15b scheme, exhibited apparently smaller σ A higher number of diffusion directions would benefit FA and MK estimation. A higher spatial resolution helps to reduce PVE. By using the 30d-3b scheme, MK is considered a robust index to reflect microstructural changes in GM and WM. We propose a systematic approach to determine the optimal DKI protocols for appropriate preclinical settings. 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1593-1603.
Sections du résumé
BACKGROUND
Although diffusion gradient directions and b-values have been optimized for diffusion kurtosis imaging (DKI), little is known about the effect of signal averaging on DKI reliability.
PURPOSE
To evaluate how signal averaging influences the reliability of DKI indices using two gradient encoding schemes with three spatial resolutions.
STUDY TYPE
Prospective.
ANIMAL MODEL
Fifteen naïve Sprague-Dawley rats.
FIELD STRENGTH/SEQUENCE
DKI was performed at 7T using two schemes (30 directions with three b-values [30d-3b] and six directions with 15 b-values [6d-15b]), three resolutions, and eight repetitions.
ASSESSMENT
DKI reliability was assessed using voxelwise relative error (σ) and test-retest error of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) within gray matter (GM) and white matter (WM). The number of excitations (NEX) was optimized by considering DKI reliability. The influence of the partial volume effect (PVE) was also assessed.
STATISTICAL TEST
One-way analysis of variance.
RESULTS
The 30d-3b scheme, compared with the 6d-15b scheme, exhibited apparently smaller σ
DATA CONCLUSION
A higher number of diffusion directions would benefit FA and MK estimation. A higher spatial resolution helps to reduce PVE. By using the 30d-3b scheme, MK is considered a robust index to reflect microstructural changes in GM and WM. We propose a systematic approach to determine the optimal DKI protocols for appropriate preclinical settings.
LEVEL OF EVIDENCE
2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1593-1603.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1593-1603Informations de copyright
© 2019 International Society for Magnetic Resonance in Medicine.
Références
Delouche A, Attye A, Heck O, et al. Diffusion MRI: Pitfalls, literature review and future directions of research in mild traumatic brain injury. Eur J Radiol 2016;85:25-30.
Rovaris M, Gass A, Bammer R, et al. Diffusion MRI in multiple sclerosis. Neurology 2005;65:1526-1532.
Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR imaging of the brain. Radiology 2000;217:331-345.
Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994;103:247-254.
Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259-267.
Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging: Concepts and applications. J Magn Reson Imaging 2001;13:534-546.
Horsfield MA, Jones DK. Applications of diffusion-weighted and diffusion tensor MRI to white matter diseases - A review. NMR Biomed 2002;15:570-577.
Shenton ME, Dickey CC, Frumin M, McCarley RW. A review of MRI findings in schizophrenia. Schizophr Res 2001;49:1-52.
Basser PJ, Jones DK. Diffusion-tensor MRI: Theory, experimental design and data analysis - A technical review. NMR Biomed 2002;15:456-467.
Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 2002;48:577-582.
Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: The quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432-1440.
Lu H, Jensen JH, Ramani A, Helpern JA. Three-dimensional characterization of non-Gaussian water diffusion in humans using diffusion kurtosis imaging. NMR Biomed 2006;19:236-247.
Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698-710.
Hui ES, Cheung MM, Qi L, Wu EX. Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage 2008;42:122-134.
Cheung MM, Hui ES, Chan KC, Helpern JA, Qi L, Wu EX. Does diffusion kurtosis imaging lead to better neural tissue characterization? A rodent brain maturation study. Neuroimage 2009;45:386-392.
Umesh Rudrapatna S, Wieloch T, Beirup K, et al. Can diffusion kurtosis imaging improve the sensitivity and specificity of detecting microstructural alterations in brain tissue chronically after experimental stroke? Comparisons with diffusion tensor imaging and histology. Neuroimage 2014;97:363-373.
Steven AJ, Zhuo J, Melhem ER. Diffusion kurtosis imaging: An emerging technique for evaluating the microstructural environment of the brain. AJR Am J Roentgenol 2014;202:W26-33.
Grinberg F, Ciobanu L, Farrher E, Shah NJ. Diffusion kurtosis imaging and log-normal distribution function imaging enhance the visualisation of lesions in animal stroke models. NMR Biomed 2012;25:1295-1304.
Yan X, Zhou M, Ying L, et al. Evaluation of optimized b-value sampling schemas for diffusion kurtosis imaging with an application to stroke patient data. Comput Med Imaging Graph 2013;37:272-280.
Yokosawa S, Sasaki M, Bito Y, et al. Optimization of scan parameters to reduce acquisition time for diffusion kurtosis imaging at 1.5T. Magn Reson Med Sci 2016;15:41-48.
Fukunaga I, Hori M, Masutani Y, et al. Effects of diffusional kurtosis imaging parameters on diffusion quantification. Radiol Phys Technol 2013;6:343-348.
Chuhutin A, Hansen B, Jespersen SN. Precision and accuracy of diffusion kurtosis estimation and the influence of b-value selection. NMR Biomed 2017;30(11).
Poot DH, den Dekker AJ, Achten E, Verhoye M, Sijbers J. Optimal experimental design for diffusion kurtosis imaging. IEEE Trans Med Imaging 2010;29:819-829.
Veraart J, Poot DH, Van Hecke W, et al. More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging. Magn Reson Med 2011;65:138-145.
Kuo YS, Yang SC, Chung HW, Wu WC. Toward quantitative fast diffusion kurtosis imaging with b-values chosen in consideration of signal-to-noise ratio and model fidelity. Med Phys 2018;45:605-612.
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.
Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B 1996;111:209-219.
Farrell JA, Landman BA, Jones CK, et al. Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T. J Magn Reson Imaging 2007;26:756-767.
Dyvorne H, Jajamovich G, Kakite S, Kuehn B, Taouli B. Intravoxel incoherent motion diffusion imaging of the liver: Optimal b-value subsampling and impact on parameter precision and reproducibility. Eur J Radiol 2014;83:2109-2113.
Jones DK. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study. Magn Reson Med 2004;51:807-815.
Papadakis NG, Murrills CD, Hall LD, Huang CL, Adrian Carpenter T. Minimal gradient encoding for robust estimation of diffusion anisotropy. Magn Reson Imaging 2000;18:671-679.
Correia MM, Carpenter TA, Williams GB. Looking for the optimal DTI acquisition scheme given a maximum scan time: Are more b-values a waste of time? Magn Reson Imaging 2009;27:163-175.
Papadakis NG, Xing D, Huang CL, Hall LD, Carpenter TA. A comparative study of acquisition schemes for diffusion tensor imaging using MRI. J Magn Reson 1999;137:67-82.
Poonawalla AH, Zhou XJ. Analytical error propagation in diffusion anisotropy calculations. J Magn Reson Imaging 2004;19:489-498.
Vos SB, Jones DK, Viergever MA, Leemans A. Partial volume effect as a hidden covariate in DTI analyses. Neuroimage 2011;55:1566-1576.
Choi S, Cunningham DT, Aguila F, et al. DTI at 7 and 3 T: Systematic comparison of SNR and its influence on quantitative metrics. Magn Reson Imaging 2011;29:739-751.
Alexander AL, Hasan KM, Lazar M, Tsuruda JS, Parker DL. Analysis of partial volume effects in diffusion-tensor MRI. Magn Reson Med 2001;45:770-780.
Yang AW, Jensen JH, Hu CC, Tabesh A, Falangola MF, Helpern JA. Effect of cerebral spinal fluid suppression for diffusional kurtosis imaging. J Magn Reson Imaging 2013;37:365-371.