Cited by 225Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

Whole brain myelin mapping using T1- and T2-weighted MR imaging data.

Open access
Ganzetti, Marco, Wenderoth, Nicole, Mantini, Dante·2014·Frontiers in Human Neuroscience

Abstract

Despite recent advancements in MR imaging, non-invasive mapping of myelin in the brain still remains an open issue. Here we attempted to provide a potential solution. Specifically, we developed a processing workflow based on T1-w and T2-w MR data to generate an optimized myelin enhanced contrast image. The workflow allows whole brain mapping using the T1-w/T2-w technique, which was originally introduced as a non-invasive method for assessing cortical myelin content. The hallmark of our approach is a retrospective calibration algorithm, applied to bias-corrected T1-w and T2-w images, that relies on image intensities outside the brain. This permits standardizing the intensity histogram of the ratio image, thereby allowing for across-subject statistical analyses. Quantitative comparisons of image histograms within and across different datasets confirmed the effectiveness of our normalization procedure. Not only did the calibrated T1-w/T2-w images exhibit a comparable intensity range, but also the shape of the intensity histograms was largely corresponding. We also assessed the reliability and specificity of the ratio image compared to other MR-based techniques, such as magnetization transfer ratio (MTR), fractional anisotropy (FA), and fluid-attenuated inversion recovery (FLAIR). With respect to these other techniques, T1-w/T2-w had consistently high values, as well as low inter-subject variability, in brain structures where myelin is most abundant. Overall, our results suggested that the T1-w/T2-w technique may be a valid tool supporting the non-invasive mapping of myelin in the brain. Therefore, it might find important applications in the study of brain development, aging and disease.

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 · 10.3389/fnhum.2014.00671

Cites

5 of 5 references verified0 citation errors · 0 not checkable

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?

  • Other quantitative MRI techniques such as calibrated T1w/T2w ratio mapping, inhomogeneous magnetization transfer (ihMT) imaging, and T2 relaxation-based myelin water imaging (MWI) are likely less affected by fiber orientation.

    “other quantitative MRI techniques such as calibrated T1w/T2w ratio mapping (Ganzetti et al., 2014; Uddin et al., 2018), inhomogeneous magnetization transfer (ihMT) imaging (Manning et al., 2017; Swanson et al., 2017), and T2 relaxation-based myelin water imaging MWI (Prasloski et al., 2012; Lee et al., 2018) are likely less affected by fiber orientation”

Self-check

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

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

Cites

Flagged first, then by citations

Research works printed but not linked: 50

  1. Ashburner J. Friston K. J. (1999). Nonlinear spatial normalization using basis functions. Hum. Brain Mapp. 7, 254– 266 10408769 PMC6873340
  2. Ashburner J. Friston K. J. (2005). Unified segmentation. Neuroimage 26, 839– 851 10.1016/j.neuroimage.2005.02.018 15955494 · DOI · Paper page
  3. Ashikaga R. Araki Y. Ono Y. Nishimura Y. Ishida O. (1999). Appearance of normal brain maturation on fluid-attenuated inversion-recovery (FLAIR) MR images. AJNR Am. J. Neuroradiol. 20, 427– 431 10219408 PMC7056081
  4. Barkovich A. J. (1988). Techniques and methods in pediatric magnetic resonance imaging. Semin. Ultrasound CT MR 9, 186– 191 3078667
  5. Barkovich A. J. (2000). Concepts of myelin and myelination in neuroradiology. AJNR Am. J. Neuroradiol. 21, 1099– 1109 10871022 PMC7973874
  6. Barkovich A. J. (2005). Magnetic resonance techniques in the assessment of myelin and myelination. J. Inherit. Metab. Dis. 28, 311– 343 10.1007/s10545-005-5952-z 15868466 · DOI
  7. Basser D. S. Matiello J. Le Bihan L. (1994). Estimation of the effective selfdiffusion tensor from the NMR spin echo. J. Magn. Reson. B 103, 247– 254 8019776
  8. Beaulieu C. Fenrich F. R. Allen P. S. (1998). Multicomponent water proton transverse relaxation and T2-discriminated water diffusion in myelinated and nonmyelinated nerve. Magn. Reson. Imaging 16, 1201– 1210 9858277
  9. Belaroussi B. Milles J. Carme S. Zhu Y. M. Benoit-Cattin H. (2006). Intensity non-uniformity correction in MRI: existing methods and their validation. Med. Image Anal. 10, 234– 246 10.1016/j.media.2005.09.004 16307900 · DOI
  10. Crooks L. E. Hylton N. M. Ortendahl D. A. Posin J. P. Kaufman L. (1987). The value of relaxation times and density measurements in clinical MRI. Invest. Radiol. 22, 158– 169 3557889
  11. Deoni S. C. Mercure E. Blasi A. Gasston D. Thomson A. Johnson M. (2011). Mapping infant brain myelination with magnetic resonance imaging. J. Neurosci. 31, 784– 791 10.1523/JNEUROSCI.2106-10.2011 21228187 PMC6623428 · DOI · Paper page
  12. Deoni S. C. Rutt B. K. Arun T. Pierpaoli C. Jones D. K. (2008). Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magn. Reson. Med. 60, 1372– 1387 10.1002/mrm.21704 19025904 · DOI
  13. Gareau P. J. Rutt B. K. Karlik S. J. Mitchell J. R. (2000). Magnetization transfer and multicomponent T2 relaxation measurements with histopathologic correlation in an experimental model of MS. J. Magn. Reson. Imaging 11, 586– 595 10.1002/1522-2586(200006)11:6<586::AID-JMRI3>3.0.CO;2-V 10862056 · DOI
  14. Genovese C. R. Lazar N. A. Nichols T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870– 878 10.1006/nimg.2001.1037 11906227 · DOI · Paper page
  15. Glasser M. F. Goyal M. S. Preuss T. M. Raichle M. E. Van Essen D. C. (2014). Trends and properties of human cerebral cortex: correlations with cortical myelin content. Neuroimage 93(Pt 2), 165– 175 10.1016/j.neuroimage.2013.03.060 23567887 PMC3795824 · DOI
  16. Glasser M. F. Sotiropoulos S. N. Wilson J. A. Coalson T. S. Fischl B. Andersson J. L. (2013). The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105– 124 10.1016/j.neuroimage.2013.04.127 23668970 PMC3720813 · DOI · Paper page
  17. Glasser M. F. Van Essen D. C. (2011). Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 31, 11597– 11616 10.1523/JNEUROSCI.2180-11.2011 21832190 PMC3167149 · DOI · Paper page
  18. Grossman R. I. Gomori J. M. Ramer K. N. Lexa F. J. Schnall M. D. (1994). Magnetization transfer: theory and clinical applications in neuroradiology. Radiographics 14, 279– 290 8190954
  19. Horsfield M. A. (1999). Mapping eddy current induced fields for the correction of diffusion-weighted echo planar images. Magn. Reson. Imaging 17, 1335– 1345 10576719
  20. Huang H. Zhang J. Wakana S. Zhang W. Ren T. Richards L. J. (2006). White and gray matter development in human fetal, newborn and pediatric brains. Neuroimage 33, 27– 38 10.1016/j.neuroimage.2006.06.009 16905335 · DOI
  21. Kitzler H. H. Su J. Zeineh M. Harper-Little C. Leung A. Kremenchutzky M. (2012). Deficient MWF mapping in multiple sclerosis using 3D whole-brain multi-component relaxation MRI. Neuroimage 59, 2670– 2677 10.1016/j.neuroimage.2011.08.052 21920444 PMC3673309 · DOI
  22. Kizildag B. Dusunceli E. Fitoz S. Erden I. (2005). The role of classic spin echo and FLAIR sequences for the evaluation of myelination in MR imaging. Diagn. Interv. Radiol. 11, 130– 136 16206052
  23. Koenig S. H. (1991). Cholesterol of myelin is the determinant of gray-white contrast in MRI of brain. Magn. Reson. Med. 20, 285– 291 1775053
  24. Kolind S. Matthews L. Johansen-Berg H. Leite M. I. Williams S. C. Deoni S. (2012). Myelin water imaging reflects clinical variability in multiple sclerosis. Neuroimage 60, 263– 270 10.1016/j.neuroimage.2011.11.070 22155325 PMC3671155 · DOI
  25. Landman B. A. Huang A. J. Gifford A. Vikram D. S. Lim I. A. Farrell J. A. (2011). Multi-parametric neuroimaging reproducibility: a 3-T resource study. Neuroimage 54, 2854– 2866 10.1016/j.neuroimage.2010.11.047 21094686 PMC3020263 · DOI
  26. Laule C. Leung E. Lis D. K. Traboulsee A. L. Paty D. W. MacKay A. L. (2006). Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult. Scler. 12, 747– 753 10.1177/1352458506070928 17263002 · DOI · Paper page
  27. Leemans A. Jones D. K. (2009). The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61, 1336– 1349 10.1002/mrm.21890 19319973 · DOI
  28. Leppert I. R. Almli C. R. McKinstry R. C. Mulkern R. V. Pierpaoli C. Rivkin M. J. (2009). T(2) relaxometry of normal pediatric brain development. J. Magn. Reson. Imaging 29, 258– 267 10.1002/jmri.21646 19161173 PMC2767196 · DOI
  29. MacKay A. Whittall K. Adler J. Li D. Paty D. Graeb D. (1994). In vivo visualization of myelin water in brain by magnetic resonance. Magn. Reson. Med. 31, 673– 677 8057820
  30. Miller D. H. Grossman R. I. Reingold S. C. McFarland H. F. (1998). The role of magnetic resonance techniques in understanding and managing multiple sclerosis. Brain 121(Pt 1), 3– 24 9549485
  31. Miot-Noirault E. Barantin L. Akoka S. Le Pape A. (1997). T2 relaxation time as a marker of brain myelination: experimental MR study in two neonatal animal models. J. Neurosci. Methods 72, 5– 14 9128162
  32. Murakami J. W. Weinberger E. Shaw D. W. (1999). Normal myelination of the pediatric brain imaged with fluid-attenuated inversion-recovery (FLAIR) MR imaging. AJNR Am. J. Neuroradiol. 20, 1406– 1411 10512220 PMC7657763
  33. Oh J. Han E. T. Pelletier D. Nelson S. J. (2006). Measurement of in vivo multi-component T2 relaxation times for brain tissue using multi-slice T2 prep at 1.5 and 3 T. Magn. Reson. Imaging 24, 33– 43 10.1016/j.mri.2005.10.016 16410176 · DOI
  34. Paus T. Collins D. L. Evans A. C. Leonard G. Pike B. Zijdenbos A. (2001). Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Res. Bull. 54, 255– 266 10.1016/S0361-9230(00)00434-2 11287130 · DOI · Paper page
  35. Pierpaoli C. Basser P. J. (1996). Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36, 893– 906 8946355
  36. Provenzale J. M. Liang L. Delong D. White L. E. (2007). Diffusion tensor imaging assessment of brain white matter maturation during the first postnatal year. AJR Am. J. Roentgenol. 189, 476– 486 10.2214/AJR.07.2132 17646476 · DOI
  37. Rademacher J. Engelbrecht V. Burgel U. Freund H. Zilles K. (1999). Measuring in vivo myelination of human white matter fiber tracts with magnetization transfer MR. Neuroimage 9, 393– 406 10191168
  38. Schmierer K. Scaravilli F. Altmann D. R. Barker G. J. Miller D. H. (2004). Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 56, 407– 415 10.1002/ana.20202 15349868 · DOI
  39. Smith S. A. Farrell J. A. Jones C. K. Reich D. S. Calabresi P. A. van Zijl P. C. (2006). Pulsed magnetization transfer imaging with body coil transmission at 3 Tesla: feasibility and application. Magn. Reson. Med. 56, 866– 875 10.1002/mrm.21035 16964602 · DOI
  40. Staudt M. Schropp C. Staudt F. Obletter N. Bise K. Breit A. (1994). MRI assessment of myelination: an age standardization. Pediatr. Radiol. 24, 122– 127 8078713
  41. Steenweg M. E. Vanderver A. Blaser S. Bizzi A. de Koning T. J. Mancini G. M. (2010). Magnetic resonance imaging pattern recognition in hypomyelinating disorders. Brain 133, 2971– 2982 10.1093/brain/awq257 20881161 PMC3589901 · DOI
  42. Tofts P. S. (1998). Standardisation and optimisation of magnetic resonance techniques for multicentre studies. J. Neurol. Neurosurg. Psychiatr. 64 Suppl. 1, S37– S43 9647283
  43. Tofts P. S. du Boulay E. P. (1990). Towards quantitative measurements of relaxation times and other parameters in the brain. Neuroradiology 32, 407– 415 2259435
  44. van Buchem M. A. Steens S. C. Vrooman H. A. Zwinderman A. H. McGowan J. C. Rassek M. (2001). Global estimation of myelination in the developing brain on the basis of magnetization transfer imaging: a preliminary study. AJNR Am. J. Neuroradiol. 22, 762– 766 11290496 PMC7976006
  45. Vidarsson L. Conolly S. M. Lim K. O. Gold G. E. Pauly J. M. (2005). Echo time optimization for linear combination myelin imaging. Magn. Reson. Med. 53, 398– 407 10.1002/mrm.20360 15678534 · DOI
  46. Vovk U. Pernus F. Likar B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26, 405– 421 10.1109/TMI.2006.891486 17354645 · DOI
  47. Wedeen V. J. Wang R. P. Schmahmann J. D. Benner T. Tseng W. Y. Dai G. (2008). Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage 41, 1267– 1277 10.1016/j.neuroimage.2008.03.036 18495497 · DOI
  48. Weiskopf N. Lutti A. Helms G. Novak M. Ashburner J. Hutton C. (2011). Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT). Neuroimage 54, 2116– 2124 10.1016/j.neuroimage.2010.10.023 20965260 PMC3018573 · DOI
  49. Welker K. M. Patton A. (2012). Assessment of normal myelination with magnetic resonance imaging. Semin. Neurol. 32, 15– 28 10.1055/s-0032-1306382 22422203 · DOI
  50. Whittall K. P. MacKay A. L. Graeb D. A. Nugent R. A. Li D. K. Paty D. W. (1997). In vivo measurement of T2 distributions and water contents in normal human brain. Magn. Reson. Med. 37, 34– 43 8978630

Cited by

Sorted by citations

Authors
Ganzetti, M., Wenderoth, N., & Mantini, D.
Affiliations
ETH Zurich (CH)
Corresponding author
Mantini, Dante
Venue
Frontiers in Human Neuroscience
Published
2014-09-02
Volume
8
Pages
671-671
ISSN
1662-5161 (ISSN-L)
OpenAlex
W2008880867
Cited by
225
References
5
Citations by year
2026: 23 · 2025: 25 · 2024: 24 · 2023: 28 · 2022: 30 · 2021: 21 · 2020: 23 · 2019: 11 · 2018: 20 · 2017: 6 · 2016: 9 · 2015: 5
FWCI
5.22
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Human Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Lirias (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection (cc-by)
Funders
Wellcome Trust (101253/Z/13/Z., 101253)
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
myelin mapping, bias correction, magnetization transfer ratio, fractional anisotropy, FLAIR, white matter, non-invasive brain imaging

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