CoChat
Sign Up
Cited by 73Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

Analysis of the effects of noise, DWI sampling, and value of assumed parameters in diffusion MRI models

Open access
Hutchinson, Elizabeth B., Avram, Alexandru V., Irfanoglu, M. Okan, Koay, C. Guan, Barnett, Alan S., Komlosh, Michal E., Özarslan, Evren, Schwerin, Susan C. +2 more·2017·Magnetic Resonance in Medicine
DOI 10.1002/mrm.26575PMC PMC6084345
Open this paper in CoChat — free accountOpen PDF

Abstract

Purpose: This study was a systematic evaluation across different and prominent diffusion MRI models to better understand the ways in which scalar metrics are influenced by experimental factors, including experimental design (diffusion‐weighted imaging [DWI] sampling) and noise. Methods: Four diffusion MRI models—diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator MRI (MAP‐MRI), and neurite orientation dispersion and density imaging (NODDI)—were evaluated by comparing maps and histogram values of the scalar metrics generated using DWI datasets obtained in fixed mouse brain with different noise levels and DWI sampling complexity. Additionally, models were fit with different input parameters or constraints to examine the consequences of model fitting procedures. Results: Experimental factors affected all models and metrics to varying degrees. Model complexity influenced sensitivity to DWI sampling and noise, especially for metrics reporting non‐Gaussian information. DKI metrics were highly susceptible to noise and experimental design. The influence of fixed parameter selection for the NODDI model was found to be considerable, as was the impact of initial tensor fitting in the MAP‐MRI model. Conclusion: Across DTI, DKI, MAP‐MRI, and NODDI, a wide range of dependence on experimental factors was observed that elucidate principles and practical implications for advanced diffusion MRI. Magn Reson Med 78:1767–1780, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/mrm.26575

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Fitting the NODDI model with Din as a free parameter reveals heterogeneity in Din across the brain.

    “Hutchinson et al. found that fitting the NODDI model with Din as a free parameter reveals heterogeneity in this value across the brain, thus, anatomical contrast evident on the Din map may also call for caution in interpreting the NODDI fitting results for ex vivo specimens (Hutchinson et al. 2017).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Hutchinson et al. 2017

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 36 · Cited by 73

Research works printed but not linked: 35

  1. [1]Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994; 66: 259– 267. 8130344 PMC1275686
  2. [3]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. 15906300
  3. [4]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. 21337412 PMC3042509
  4. [5]Tournier JD, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion‐weighted MRI data using spherical deconvolution. NeuroImage 2004; 23: 1176– 1185. 15528117
  5. [6]Callaghan PT, Eccles CD, Xia Y. NMR microscopy of dynamic displacements: k‐space and q‐space imaging. J Phys E 1988; 21: 820.
  6. [7]Callaghan PT, MacGowan D, Packer KJ. High‐resolution q‐space imaging in porous structures. High‐resolution q‐space imaging in porous structures. J Magn Reson Imaging 1990; 90: 177– 182.
  7. [9]Wedeen VJ, Hagmann P, Tseng W‐YIY, Reese TG, Weisskoff RM. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med 2005; 54: 1377– 1386. 16247738
  8. [10]Cheng J, Jiang T, Deriche R. Nonnegative definite EAP and ODF estimation via a unified multi‐shell HARDI reconstruction. Med Image Comput Comput Assist Interv 2012; 15: 313– 321. 23286063
  9. [11]Hosseinbor AP, Chung MK, Wu Y‐CC, Alexander AL. Bessel Fourier orientation reconstruction (BFOR): an analytical diffusion propagator reconstruction for hybrid diffusion imaging and computation of q‐space indices. NeuroImage 2013; 64: 650– 670. 22963853 PMC3508305
  10. [12]Özarslan E, Koay CG, Shepherd TM, Komlosh ME, İrfanoğlu MO, Pierpaoli C, Basser PJ. Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. NeuroImage 2013; 78: 16– 32. 23587694 PMC4059870
  11. [13]Özarslan E, Koay CG, Basser PJ. Simple Harmonic Oscillator Based Estimation and Reconstruction for One‐Dimensional q‐Space MR. In Proceedings of the 16th Annual Meeting of ISMRM, Toronto, Canada, 2008 p. 35.
  12. [14]Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage 2005; 27: 48– 58. 15979342
  13. [15]Panagiotaki E, Schneider T, Siow B, Hall MG, Lythgoe MF, Alexander DC. Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. NeuroImage 2012; 59: 2241– 2254. 22001791
  14. [16]Assaf Y, Blumenfeld‐Katzir T, Yovel Y, Basser PJ. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn Reson Med 2008; 59: 1347– 1354. 18506799 PMC4667732
  15. [19]Kunz N, Zhang H, Vasung L, O’Brien KR, Assaf Y, Lazeyras F, Alexander DC, Hüppi PS. Assessing white matter microstructure of the newborn with multi‐shell diffusion MRI and biophysical compartment models. NeuroImage 2014; 96: 288– 299. 24680870
  16. [20]Kodiweera C, Alexander AL, Harezlak J, McAllister TW, Wu Y‐CC. Age effects and sex differences in human brain white matter of young to middle‐aged adults: A DTI, NODDI, and q‐space study. NeuroImage 2016; 128: 180– 192. 26724777 PMC4824064
  17. [21]Hutchinson EB, Avram A, Komlosh M, Okan Irfanoglu M, Barnett A, Ozarslan E, Schwerin S, Radomski K, Juliano S, Pierpaoli C. A Systematic Comparative Study of DTI and Higher Order Diffusion Models in Brain Fixed Tissue. In Proceedings of the 24th Annual Meeting of ISMRM, Singapore, 2016. Abstract 1048.
  18. [22]Pierpaoli C, Barnett A, Basser P, Chang L, Koay C, Pajevic S, Rohde G, Sarlls J, Wu M. TORTOISE: an integrated software package for processing of diffusion MRI data. In Proceedings of the 18th Annual Meeting of ISMRM, Stockholm, Sweden, 2010. Abstract 1597.
  19. [23]Johnson GA, Badea A, Brandenburg J, Cofer G, Fubara B, Liu S, Nissanov J. Waxholm space: an image‐based reference for coordinating mouse brain research. NeuroImage 2010; 53: 365– 372. 20600960 PMC2930145
  20. [24]Veraart J, Novikov DS, Christiaens D, Ades‐aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. NeuroImage 2016;142:394–406. PMC5159209 27523449
  21. [25]Chang L‐CC, Walker L, Pierpaoli C. Informed RESTORE: a method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts. Magn Reson Med 2012; 68: 1654– 1663. 22287298 PMC3615560
  22. [26]Koay CG, Ozarslan E, Basser PJ. A signal transformational framework for breaking the noise floor and its applications in MRI. J Magn Reson 2009; 197: 108– 119. 19138540 PMC2765718
  23. [27]Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996; 36: 893– 906. 8946355
  24. [28]Glenn GR, Helpern JA, Tabesh A, Jensen JH. Quantitative assessment of diffusional kurtosis anisotropy. NMR Biomed 2015; 28: 448– 459. 25728763 PMC4378654
  25. [29]Avram AV, Sarlls JE, Barnett AS, Özarslan E, Thomas C, Irfanoglu MO, Hutchinson E, Pierpaoli C, Basser PJ. Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure. NeuroImage 2016; 127: 422– 434. 26584864 PMC4755846
  26. [30]Zhang H, Hubbard PL, Parker GJ, Alexander DC. Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. NeuroImage 2011; 56: 1301– 1315. 21316474
  27. [31]Veraart J, Poot DH, Van Hecke W, Blockx I, Van der Linden A, Verhoye M, Sijbers J. More accurate estimation of diffusion tensor parameters using diffusion Kurtosis imaging. Magn Reson Med 2011; 65: 138– 145. 20878760
  28. [32]Anderson AW. Theoretical analysis of the effects of noise on diffusion tensor imaging. Magn Reson Med 2001; 46: 1174– 1188. 11746585
  29. [33]Basser PJ, Pajevic S. Statistical artifacts in diffusion tensor MRI (DT‐MRI) caused by background noise. Magn Reson Med 2000; 44: 41– 50. 10893520
  30. [34]Jones DK, Basser PJ. “Squashing peanuts and smashing pumpkins”: how noise distorts diffusion‐weighted MR data. Magn Reson Med 2004; 52: 979– 993. 15508154
  31. [35]Alexander DC. A general framework for experiment design in diffusion MRI and its application in measuring direct tissue‐microstructure features. Magn Reson Med 2008; 60: 439– 448. 18666109
  32. [36]Alexander DC, Hubbard PL, Hall MG, Moore EA, Ptito M, Parker GJ, Dyrby TB. Orientationally invariant indices of axon diameter and density from diffusion MRI. NeuroImage 2010; 52: 1374– 1389. 20580932
  33. [37]Glenn GR, Tabesh A, Jensen JH. A simple noise correction scheme for diffusional kurtosis imaging. Magn Reson Imaging 2015; 33: 124– 133. 25172990 PMC4268031
  34. [38]Jelescu IO, Veraart J, Fieremans E, Novikov DS. Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue. NMR Biomed 2016; 29: 33– 47. 26615981 PMC4920129
  35. [39]Koay CG, Chang L‐CC, Carew JD, Pierpaoli C, Basser PJ. A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. J Magn Reson 2006; 182: 115– 125. 16828568

Authors
Hutchinson, E. B., Avram, A. V., Irfanoglu, M. O., Koay, C. G., Barnett, A. S., Komlosh, M. E., Özarslan, E., Schwerin, S. C., Juliano, S. L., & Pierpaoli, C.
Affiliations
  • Henry M. Jackson Foundation (US)
  • National Institutes of Health (US)
  • National Intrepid Center of Excellence Bethesda Maryland USA
  • Linköping University (SE)
  • Uniformed Services University of the Health Sciences (US)
Corresponding author
Hutchinson, Elizabeth B.
ORCID
Venue
Magnetic Resonance in Medicine
Publisher
Wiley
Published
2017-01-16
Volume
78
Issue
5
Pages
1767-1780
ISSN
  • 0740-3194 (ISSN-L)
  • 1522-2594
DOI
10.1002/mrm.26575
PMC
PMC6084345
OpenAlex
W2571611893
Cited by
73
References
36
Citations by year
2026: 5 · 2025: 7 · 2024: 5 · 2023: 3 · 2022: 10 · 2021: 10 · 2020: 12 · 2019: 11 · 2018: 8 · 2017: 2
FWCI
6.92
Type
journal article
Language
en
Open access
Licence
Locations
  • Magnetic Resonance in Medicine (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
Center for Neuroscience and Regenerative Medicine
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 › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
Keywords
noise sensitivity, diffusion tensor imaging, diffusion kurtosis imaging, NODDI, non-Gaussian diffusion, mouse brain
MeSH terms
Animals, Brain, Male, Models, Theoretical, Water, Diffusion Magnetic Resonance Imaging, Mice, Neuroimaging

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review