Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Technical Review
  • Published:

Quantitative magnetic resonance imaging of brain anatomy and in vivo histology

Nature Reviews Physics volume 3, pages 570–588 (2021)Cite this article

Subjects

Abstract

Quantitative magnetic resonance imaging (qMRI) goes beyond conventional MRI, which aims primarily at local image contrast. It provides specific physical parameters related to the nuclear spin of protons in water, such as relaxation times. These parameters carry information about the local microstructural environment of the protons (such as myelin in the brain). Non-invasive in vivo histology using MRI (hMRI) aims to use this information to directly characterize biological tissue microstructure, partially replacing or complementing classical invasive histology. The understanding of MRI tissue contrast provided by hMRI is, in turn, crucial for further improvements of qMRI, and they should be considered closely interlinked. We discuss concepts, models and validation approaches, pointing out challenges and the latest advances in this field. Further, we point out links to physics, including computational and analytical approaches and developments in materials science and photonics, that aid in reference data acquisition and model validation.

Key points

  • Quantitative magnetic resonance imaging (qMRI) provides quantitative measurements of specific physical parameters related to the nuclear spin of protons in water.

  • Water proton spins act as intrinsic probes of the surrounding tissue microstructure.

  • qMRI parameters, including longitudinal and transverse relaxation rates, magnetic susceptibility, proton density and magnetization transfer, carry important information about myelination, iron and cell membranes in the living brain.

  • In vivo histology using MRI (hMRI) aims to provide quantitative whole-brain measures of brain microstructure in health and disease.

  • qMRI and hMRI promise much needed sensitive biomarkers in health and disease.

  • Model building and validation require comprehensive reference data of brain microstructure that capture all features relevant for the MRI contrast.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Conventional and quantitative magnetic resonance imaging.
Fig. 2: Acquisition strategies for quantitative magnetic resonance imaging.
Fig. 3: An example of the interplay of magnetic resonance imaging, quantitative magnetic resonance imaging and in vivo histology using magnetic resonance imaging.
Fig. 4: Quantitative magnetic resonance imaging parameters reflect water–tissue interactions on multiple temporal and spatial scales.
Fig. 5: Longitudinal relaxation rate map of the human brain provides multiscale structural information on a spatial scale spanning several orders of magnitude.

Similar content being viewed by others

References

  1. Boesch, C. Nobel Prizes for nuclear magnetic resonance: 2003 and historical perspectives. J. Magn. Reson. Imaging 20, 177–179 (2004).

    Article  Google Scholar 

  2. Lauterbur, P. C. Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature 242, 190–191 (1973).

    Article  ADS  Google Scholar 

  3. Mansfield, P. & Grannell, P. K. NMR ‘diffraction’ in solids? J. Phys. C Solid State Phys. 6, L422–L426 (1973).

    Article  ADS  Google Scholar 

  4. Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17, 162–173 (2018).

    Article  Google Scholar 

  5. Geraldes, R. et al. The current role of MRI in differentiating multiple sclerosis from its imaging mimics. Nat. Rev. Neurol. 14, 199–213 (2018).

    Article  Google Scholar 

  6. Brownlee, W. J., Hardy, T. A., Fazekas, F. & Miller, D. H. Diagnosis of multiple sclerosis: progress and challenges. Lancet 389, 1336–1346 (2017).

    Article  Google Scholar 

  7. Young, I. R. et al. Nuclear magnetic resonance imaging of the brain in multiple sclerosis. Lancet 2, 1063–1066 (1981).

    Article  Google Scholar 

  8. Rees, J. H. Diagnosis and treatment in neuro-oncology: an oncological perspective. Br. J. Radiol. 84, S82–S89 (2011).

    Article  Google Scholar 

  9. Fiebach, J. B. et al. Stroke magnetic resonance imaging is accurate in hyperacute intracerebral hemorrhage: a multicenter study on the validity of stroke imaging. Stroke 35, 502–506 (2004).

    Article  Google Scholar 

  10. Ross, M. A., Biller, J., Adams, H. P. Jr & Dunn, V. Magnetic resonance imaging in Wallenberg’s lateral medullary syndrome. Stroke 17, 542–545 (1986).

    Article  Google Scholar 

  11. Moseley, M. E. et al. Diffusion-weighted MR imaging of acute stroke: correlation with T2-weighted and magnetic susceptibility-enhanced MR imaging in cats. Am. J. Neuroradiol. 11, 423–429 (1990).

    Google Scholar 

  12. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    Article  Google Scholar 

  13. German National Cohort (GNC) Consortium. The German National Cohort: aims, study design and organization. Eur. J. Epidemiol. 29, 371–382 (2014).

    Article  Google Scholar 

  14. Rosen, B. R. & Savoy, R. L. fMRI at 20: has it changed the world? Neuroimage 62, 1316–1324 (2012).

    Article  Google Scholar 

  15. Bandettini, P. A. fMRI. The MIT Press Essential Knowledge Series (MIT Press, 2020).

  16. Whitaker, K. J. et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc. Natl Acad. Sci. USA 113, 9105–9110 (2016).

    Article  Google Scholar 

  17. Natu, V. S. et al. Apparent thinning of human visual cortex during childhood is associated with myelination. Proc. Natl Acad. Sci. USA 116, 20750–20759 (2019).

    Article  Google Scholar 

  18. Good, C. D. et al. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14, 21–36 (2001).

    Article  Google Scholar 

  19. Callaghan, M. F. et al. Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiol. Aging 35, 1862–1872 (2014).

    Article  Google Scholar 

  20. Fox, N. C. et al. Imaging of onset and progression of Alzheimer’s disease with voxel-compression mapping of serial magnetic resonance images. Lancet 358, 201–205 (2001).

    Article  Google Scholar 

  21. Shah, N. J. et al. Quantitative cerebral water content mapping in hepatic encephalopathy. Neuroimage 41, 706–717 (2008).

    Article  Google Scholar 

  22. Freund, P. et al. MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: a prospective longitudinal study. Lancet Neurol. 12, 873–881 (2013).

    Article  Google Scholar 

  23. Maguire, E. A. et al. Navigation-related structural change in the hippocampi of taxi drivers. Proc. Natl Acad. Sci. USA 97, 4398–4403 (2000).

    Article  ADS  Google Scholar 

  24. Draganski, B. et al. Changes in grey matter induced by training newly honed juggling skills show up as a transient feature on a brain-imaging scan. Nature 427, 311–312 (2004).

    Article  ADS  Google Scholar 

  25. Zatorre, R. J., Fields, R. D. & Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15, 528–536 (2012).

    Article  Google Scholar 

  26. Sagi, Y. et al. Learning in the fast lane: new insights into neuroplasticity. Neuron 73, 1195–1203 (2012).

    Article  Google Scholar 

  27. Sereno, M. I., Lutti, A., Weiskopf, N. & Dick, F. Mapping the human cortical surface by combining quantitative T1 with retinotopy. Cereb. Cortex 23, 2261–2268 (2013). Demonstrates systematic mapping of visual brain areas based on non-invasive R1 myelin measures, including a comparison with functional neuroanatomy.

    Article  Google Scholar 

  28. Kuehn, E. et al. Body topography parcellates human sensory and motor cortex. Cereb. Cortex 27, 3790–3805 (2017).

    Article  Google Scholar 

  29. Attar, F. M. et al. Mapping short association fibers in the early cortical visual processing stream using in vivo diffusion tractography. Cereb. Cortex 30, 4496–4514 (2020).

    Article  Google Scholar 

  30. Bernstein, M. A., King, K. F. & Zhou, X. J. Handbook of MRI Pulse Sequences (Academic Press, 2004).

  31. Filo, S. et al. Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI. Nat. Commun. 10, 3403 (2019). Shows that lipid composition of myelin may be captured by combination of qMRI parameters.

    Article  ADS  Google Scholar 

  32. Stüber, C. et al. Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. Neuroimage 93, 95–106 (2014).

    Article  Google Scholar 

  33. Koenig, S. H. Classes of hydration sites at protein-water interfaces: the source of contrast in magnetic resonance imaging. Biophys. J. 69, 593–603 (1995).

    Article  ADS  Google Scholar 

  34. Langkammer, C. et al. Quantitative MR imaging of brain iron: a postmortem validation study. Radiology 257, 455–462 (2010).

    Article  Google Scholar 

  35. Möller, H. E. et al. Iron, myelin, and the brain: neuroimaging meets neurobiology. Trends Neurosci. 42, 384–401 (2019).

    Article  Google Scholar 

  36. Jespersen, S. N., Leigland, L. A., Cornea, A. & Kroenke, C. D. Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging. IEEE Trans. Med. Imaging 31, 16–32 (2012).

    Article  Google Scholar 

  37. Beaulieu, C. & Allen, P. S. Water diffusion in the giant axon of the squid: implications for diffusion-weighted MRI of the nervous system. Magn. Reson. Med. 32, 579–583 (1994).

    Article  Google Scholar 

  38. Palombo, M. et al. SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage 215, 116835 (2020).

    Article  Google Scholar 

  39. Cercignani, M., Dowell, N. G. & Tofts, P. S. (eds) Quantitative MRI of the Brain: Principles of Physical Measurement 2nd edn (CRC Press, 2018).

  40. Weiskopf, N. et al. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Front. Neurosci. 7, 95 (2013).

    Article  Google Scholar 

  41. Gracien, R.-M. et al. How stable is quantitative MRI? – Assessment of intra- and inter-scanner-model reproducibility using identical acquisition sequences and data analysis programs. Neuroimage 207, 116364 (2020).

    Article  Google Scholar 

  42. Leutritz, T. et al. Multiparameter mapping of relaxation (R1, R2*), proton density and magnetization transfer saturation at 3 T: A multicenter dual-vendor reproducibility and repeatability study. Hum. Brain Mapp. 41, 4232–4247 (2020).

    Article  Google Scholar 

  43. Damadian, R. Tumor detection by nuclear magnetic resonance. Science 171, 1151–1153 (1971).

    Article  ADS  Google Scholar 

  44. Bakker, C. J., de Graaf, C. N. & van Dijk, P. Derivation of quantitative information in NMR imaging: a phantom study. Phys. Med. Biol. 29, 1511–1525 (1984).

    Article  Google Scholar 

  45. Tofts, P. S. & du Boulay, E. P. Towards quantitative measurements of relaxation times and other parameters in the brain. Neuroradiology 32, 407–415 (1990).

    Article  Google Scholar 

  46. Edwards, L. J., Kirilina, E., Mohammadi, S. & Weiskopf, N. Microstructural imaging of human neocortex in vivo. Neuroimage 182, 184–206 (2018).

    Article  Google Scholar 

  47. Does, M. D. Inferring brain tissue composition and microstructure via MR relaxometry. Neuroimage 182, 136–148 (2018).

    Article  Google Scholar 

  48. Sled, J. G. Modelling and interpretation of magnetization transfer imaging in the brain. Neuroimage 182, 128–135 (2018).

    Article  Google Scholar 

  49. David, G. et al. Traumatic and nontraumatic spinal cord injury: pathological insights from neuroimaging. Nat. Rev. Neurol. 15, 718–731 (2019).

    Article  Google Scholar 

  50. Enzinger, C. et al. Nonconventional MRI and microstructural cerebral changes in multiple sclerosis. Nat. Rev. Neurol. 11, 676–686 (2015).

    Article  Google Scholar 

  51. Albers, G. W. Diffusion-weighted MRI for evaluation of acute stroke. Neurology 51, S47–S49 (1998).

    Article  Google Scholar 

  52. Barkhof, F., Jäger, R., Thurnher, M. & Rovira, A. (eds) Clinical Neuroradiology: The ESNR Textbook (Springer, 2019).

  53. Setsompop, K. et al. Pushing the limits of in vivo diffusion MRI for the human connectome project. Neuroimage 80, 220–233 (2013).

    Article  Google Scholar 

  54. Medgadget Editors. FDA gives first clearance to Siemens high-field 7 Tesla MRI scanner. Medgadget https://www.medgadget.com/2017/10/fda-gives-first-clearance-high-field-7-tesla-mri-scanner.html (2017).

  55. Medgadget Editors. EU gives first approval for ultra-high-field MRI scanner, the Siemens Magnetom Terra. Medgadget https://www.medgadget.com/2017/08/eu-gives-first-approval-ultra-high-field-mri-scanner-siemens-magnetom-terra.html (2017).

  56. Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R. & Rosen, M. S. Image reconstruction by domain-transform manifold learning. Nature 555, 487–492 (2018).

    Article  ADS  Google Scholar 

  57. Ma, D. et al. Magnetic resonance fingerprinting. Nature 495, 187–192 (2013). Introduces non-repetitive MRI pulse sequences to estimate qMRI parameters.

    Article  ADS  Google Scholar 

  58. Tabelow, K. et al. hMRI – a toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 194, 191–210 (2019).

    Article  Google Scholar 

  59. Karakuzu, A. et al. qMRLab: Quantitative MRI analysis, under one umbrella. J. Open Source Softw. 5, 2343 (2020).

    Article  ADS  Google Scholar 

  60. Novikov, D. S., Fieremans, E., Jespersen, S. N. & Kiselev, V. G. Quantifying brain microstructure with diffusion MRI: theory and parameter estimation. NMR Biomed. 32, e3998 (2019).

    Article  Google Scholar 

  61. Weiskopf, N., Mohammadi, S., Lutti, A. & Callaghan, M. F. Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. Curr. Opin. Neurol. 28, 313–322 (2015).

    Article  Google Scholar 

  62. Deistung, A. et al. Toward in vivo histology: A comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength. Neuroimage 65, 299–314 (2013).

    Article  Google Scholar 

  63. Bridge, H. & Clare, S. High-resolution MRI: in vivo histology? Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 137–146 (2006).

    Article  Google Scholar 

  64. Alexander, D. C., Dyrby, T. B., Nilsson, M. & Zhang, H. Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. 32, e3841 (2019).

    Article  Google Scholar 

  65. Patel, Y. et al. Virtual histology of multi-modal magnetic resonance imaging of cerebral cortex in young men. Neuroimage 218, 116968 (2020).

    Article  Google Scholar 

  66. Kessler, L. G. et al. The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat. Methods Med. Res. 24, 9–26 (2015).

    Article  MathSciNet  Google Scholar 

  67. European Society of Radiology (ESR). ESR statement on the validation of imaging biomarkers. Insights Imaging 11, 76 (2020).

    Article  Google Scholar 

  68. Haller, S. et al. Arterial spin labeling perfusion of the brain: emerging clinical applications. Radiology 281, 337–356 (2016).

    Article  ADS  Google Scholar 

  69. Germuska, M. & Wise, R. G. Calibrated fMRI for mapping absolute CMRO2: Practicalities and prospects. Neuroimage 187, 145–153 (2019).

    Article  Google Scholar 

  70. Demetriou, E., Kujawa, A. & Golay, X. Pulse sequences for measuring exchange rates between proton species: From unlocalised NMR spectroscopy to chemical exchange saturation transfer imaging. Prog. Nucl. Magn. Reson. Spectrosc. 120–121, 25–71 (2020).

    Article  Google Scholar 

  71. van Zijl, P. C. M., Lam, W. W., Xu, J., Knutsson, L. & Stanisz, G. J. Magnetization transfer contrast and chemical exchange saturation transfer MRI. Features and analysis of the field-dependent saturation spectrum. Neuroimage 168, 222–241 (2018).

    Article  Google Scholar 

  72. McRobbie, D. W., Moore, E. A., Graves, M. J. & Prince, M. R. MRI from Picture to Proton (Cambridge Univ. Press, 2017).

  73. Seiberlich, N. et al. Quantitative Magnetic Resonance Imaging (Academic Press, 2020).

  74. Vlaardingerbroek, M. T. & den Boer, J. A. in Magnetic Resonance Imaging 9–54 (Springer, 2003).

  75. Brown, R. W., Cheng, Y.-C. N., Haacke, E. M., Thompson, M. R. & Venkatesan, R. Magnetic Resonance Imaging: Physical Principles and Sequence Design 2nd edn (Wiley, 2014).

  76. Bloch, F. Nuclear induction. Phys. Rev. 70, 460–474 (1946).

    Article  ADS  Google Scholar 

  77. Hanson, L. G. Is quantum mechanics necessary for understanding magnetic resonance? Concepts Magn. Reson. 32A, 329–340 (2008).

    Article  Google Scholar 

  78. Edzes, H. T. & Samulski, E. T. Cross relaxation and spin diffusion in the proton NMR or hydrated collagen. Nature 265, 521–523 (1977).

    Article  ADS  Google Scholar 

  79. McConnell, H. M. Reaction rates by nuclear magnetic resonance. J. Chem. Phys. 28, 430–431 (1958).

    Article  ADS  Google Scholar 

  80. Henkelman, R. M. et al. Quantitative interpretation of magnetization transfer. Magn. Reson. Med. 29, 759–766 (1993).

    Article  Google Scholar 

  81. Torrey, H. C. Bloch equations with diffusion terms. Phys. Rev. 104, 563–565 (1956).

    Article  ADS  Google Scholar 

  82. Spencer, R. G. & Bi, C. A tutorial introduction to inverse problems in magnetic resonance. NMR Biomed. 33, e4315 (2020).

    Google Scholar 

  83. Venkatesan, R., Lin, W. & Haacke, E. M. Accurate determination of spin-density and T1 in the presence of RF-field inhomogeneities and flip-angle miscalibration. Magn. Reson. Med. 40, 592–602 (1998).

    Article  Google Scholar 

  84. Helms, G., Dathe, H. & Dechent, P. Quantitative FLASH MRI at 3T using a rational approximation of the Ernst equation. Magn. Reson. Med. 59, 667–672 (2008).

    Article  Google Scholar 

  85. Mackay, A. et al. In vivo visualization of myelin water in brain by magnetic resonance. Magn. Reson. Med. 31, 673–677 (1994).

    Article  Google Scholar 

  86. Helms, G., Dathe, H., Kallenberg, K. & Dechent, P. High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI. Magn. Reson. Med. 60, 1396–1407 (2008).

    Article  Google Scholar 

  87. Basser, P. J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B 111, 209–219 (1996).

    Article  Google Scholar 

  88. McCollough, C. H., Leng, S., Yu, L. & Fletcher, J. G. Dual- and multi-energy CT: principles, technical approaches, and clinical applications. Radiology 276, 637–653 (2015).

    Article  Google Scholar 

  89. Preibisch, C. & Deichmann, R. Influence of RF spoiling on the stability and accuracy of T1 mapping based on spoiled FLASH with varying flip angles. Magn. Reson. Med. 61, 125–135 (2009).

    Article  Google Scholar 

  90. Haskell, M. W. et al. Network accelerated motion estimation and reduction (NAMER): convolutional neural network guided retrospective motion correction using a separable motion model. Magn. Reson. Med. 82, 1452–1461 (2019).

    Article  Google Scholar 

  91. Tamir, J. I. et al. Computational MRI with physics-based constraints: application to multicontrast and quantitative imaging. IEEE Signal Process. Mag. 37, 94–104 (2020).

    Article  Google Scholar 

  92. Assländer, J. A perspective on MR fingerprinting. J. Magn. Reson. Imaging 53, 676–685 (2021).

    Article  Google Scholar 

  93. Novikov, D. S., Kiselev, V. G. & Jespersen, S. N. On modeling. Magn. Reson. Med. 79, 3172–3193 (2018).

    Article  Google Scholar 

  94. West, D. J. et al. Inherent and unpredictable bias in multi-component DESPOT myelin water fraction estimation. Neuroimage 195, 78–88 (2019).

    Article  Google Scholar 

  95. Fiedler, T. M., Ladd, M. E. & Bitz, A. K. SAR simulations & safety. Neuroimage 168, 33–58 (2018).

    Article  Google Scholar 

  96. Davids, M., Guérin, B., Vom Endt, A., Schad, L. R. & Wald, L. L. Prediction of peripheral nerve stimulation thresholds of MRI gradient coils using coupled electromagnetic and neurodynamic simulations. Magn. Reson. Med. 81, 686–701 (2019).

    Article  Google Scholar 

  97. Pohmann, R., Speck, O. & Scheffler, K. Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 tesla using current receive coil arrays. Magn. Reson. Med. 75, 801–809 (2016).

    Article  Google Scholar 

  98. Budinger, T. F. & Bird, M. D. MRI and MRS of the human brain at magnetic fields of 14 T to 20 T: Technical feasibility, safety, and neuroscience horizons. Neuroimage 168, 509–531 (2018).

    Article  Google Scholar 

  99. Sadeghi-Tarakameh, A. et al. In vivo human head MRI at 10.5 T: a radiofrequency safety study and preliminary imaging results. Magn. Reson. Med. 84, 484–496 (2020).

    Article  Google Scholar 

  100. Schmitt, M. et al. A 128-channel receive-only cardiac coil for highly accelerated cardiac MRI at 3 Tesla. Magn. Reson. Med. 59, 1431–1439 (2008).

    Article  Google Scholar 

  101. Wiggins, G. C. et al. 96-Channel receive-only head coil for 3 Tesla: design optimization and evaluation. Magn. Reson. Med. 62, 754–762 (2009).

    Article  Google Scholar 

  102. Pruessmann, K. P., Weiger, M., Scheidegger, M. B. & Boesiger, P. SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952–962 (1999).

    Article  Google Scholar 

  103. Griswold, M. A. et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47, 1202–1210 (2002).

    Article  Google Scholar 

  104. Setsompop, K. et al. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn. Reson. Med. 67, 1210–1224 (2012).

    Article  Google Scholar 

  105. Padormo, F., Beqiri, A., Hajnal, J. V. & Malik, S. J. Parallel transmission for ultrahigh-field imaging. NMR Biomed. 29, 1145–1161 (2016).

    Article  Google Scholar 

  106. Lutti, A. et al. Robust and fast whole brain mapping of the RF transmit field B1 at 7T. PLoS ONE 7, e32379 (2012).

    Article  ADS  Google Scholar 

  107. Pohmann, R. & Scheffler, K. A theoretical and experimental comparison of different techniques for B1 mapping at very high fields. NMR Biomed. 26, 265–275 (2013).

    Article  Google Scholar 

  108. Turner, R. Gradient coil design: a review of methods. Magn. Reson. Imaging 11, 903–920 (1993).

    Article  Google Scholar 

  109. Littin, S. et al. Development and implementation of an 84-channel matrix gradient coil. Magn. Reson. Med. 79, 1181–1191 (2018).

    Article  Google Scholar 

  110. Veraart, J. et al. Noninvasive quantification of axon radii using diffusion MRI. eLife 9, e49855 (2020). Addresses accuracy issues of MRI-based effective axon diameter measurements.

    Article  Google Scholar 

  111. Veraart, J., Raven, E. P., Edwards, L. J., Weiskopf, N. & Jones, D. K. The variability of MR axon radii estimates in the human white matter. Hum. Brain Mapp. 42, 2201–2213 (2021).

    Article  Google Scholar 

  112. Kirilina, E. et al. Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping. Sci. Adv. 6, eaaz9281 (2020). Derives a biophysical model of iron-driven contrast in superficial white matter from first principles.

    Article  ADS  Google Scholar 

  113. Triantafyllou, C. et al. Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. Neuroimage 26, 243–250 (2005).

    Article  Google Scholar 

  114. Federau, C. & Gallichan, D. Motion-correction enabled ultra-high resolution in-vivo 7T-MRI of the brain. PLoS One 11, e0154974 (2016).

    Article  Google Scholar 

  115. Zaitsev, M., Dold, C., Sakas, G., Hennig, J. & Speck, O. Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system. Neuroimage 31, 1038–1050 (2006).

    Article  Google Scholar 

  116. Callaghan, M. F. et al. An evaluation of prospective motion correction (PMC) for high resolution quantitative MRI. Front. Neurosci. 9, 97 (2015).

    Article  Google Scholar 

  117. Atkinson, D., Hill, D. L. G., Stoyle, P. N. R., Summers, P. E. & Keevil, S. F. in Information Processing in Medical Imaging (eds Duncan, J. & Gindi, G.) 341–354 (Springer, 1997).

  118. Mohammadi, S., Hutton, C., Nagy, Z., Josephs, O. & Weiskopf, N. Retrospective correction of physiological noise in DTI using an extended tensor model and peripheral measurements. Magn. Reson. Med. 70, 358–369 (2013).

    Article  Google Scholar 

  119. Callaghan, M. F., Mohammadi, S. & Weiskopf, N. Synthetic quantitative MRI through relaxometry modelling. NMR Biomed. 29, 1729–1738 (2016).

    Article  Google Scholar 

  120. Stockmann, J. P. & Wald, L. L. In vivo B0 field shimming methods for MRI at 7 T. Neuroimage 168, 71–87 (2018).

    Article  Google Scholar 

  121. Versluis, M. J. et al. Origin and reduction of motion and f0 artifacts in high resolution T2*-weighted magnetic resonance imaging: application in Alzheimer’s disease patients. Neuroimage 51, 1082–1088 (2010).

    Article  Google Scholar 

  122. Vannesjo, S. J. et al. Retrospective correction of physiological field fluctuations in high-field brain MRI using concurrent field monitoring. Magn. Reson. Med. 73, 1833–1843 (2015).

    Article  Google Scholar 

  123. Prasloski, T., Mädler, B., Xiang, Q.-S., MacKay, A. & Jones, C. Applications of stimulated echo correction to multicomponent T2 analysis. Magn. Reson. Med. 67, 1803–1814 (2012).

    Article  Google Scholar 

  124. Ben-Eliezer, N., Sodickson, D. K. & Block, K. T. Rapid and accurate T2 mapping from multi-spin-echo data using Bloch-simulation-based reconstruction. Magn. Reson. Med. 73, 809–817 (2015).

    Article  Google Scholar 

  125. Teixeira, A. G., P., R., Malik, S. J. & Hajnal, J. V. Fast quantitative MRI using controlled saturation magnetization transfer. Magn. Reson. Med. 81, 907–920 (2019).

    Article  Google Scholar 

  126. Barker, G. J. et al. A standardised method for measuring magnetisation transfer ratio on MR imagers from different manufacturers — the EuroMT sequence. MAGMA 18, 76–80 (2005).

    Article  Google Scholar 

  127. Stikov, N. et al. On the accuracy of T1 mapping: Searching for common ground. Magn. Reson. Med. 73, 514–522 (2015).

    Article  Google Scholar 

  128. Bloembergen, N., Purcell, E. M. & Pound, R. V. Relaxation effects in nuclear magnetic resonance absorption. Phys. Rev. 73, 679–712 (1948).

    Article  ADS  Google Scholar 

  129. Whittall, K. P., MacKay, A. L. & Li, D. K. Are mono-exponential fits to a few echoes sufficient to determine T2 relaxation for in vivo human brain? Magn. Reson. Med. 41, 1255–1257 (1999).

    Article  Google Scholar 

  130. Knight, M. J., Wood, B., Couthard, E. & Kauppinen, R. Anisotropy of spin-echo T2 relaxation by magnetic resonance imaging in the human brain in vivo. Biomed. Spectrosc. Imaging 4, 299–310 (2015).

    Article  Google Scholar 

  131. Pampel, A., Müller, D. K., Anwander, A., Marschner, H. & Möller, H. E. Orientation dependence of magnetization transfer parameters in human white matter. Neuroimage 114, 136–146 (2015).

    Article  Google Scholar 

  132. Wharton, S. & Bowtell, R. Fiber orientation-dependent white matter contrast in gradient echo MRI. Proc. Natl Acad. Sci. USA 109, 18559–18564 (2012). Explains the orientation dependence of gradient echo signal based on a hollow cylinder multi-compartment model of myelinated axons and myelin susceptibility tensor.

    Article  ADS  Google Scholar 

  133. Pakkenberg, B. et al. Aging and the human neocortex. Exp. Gerontol. 38, 95–99 (2003).

    Article  Google Scholar 

  134. Nieuwenhuys, R., Voogd, J. & van Huijzen, C. The Human Central Nervous System: A Synopsis and Atlas (Springer, 2007).

  135. Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).

    Article  Google Scholar 

  136. Motta, A. et al. Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science 366, eaay3134 (2019).

    Article  Google Scholar 

  137. Lazari, A. & Lipp, I. Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. Neuroimage 230, 117744 (2021).

    Article  Google Scholar 

  138. Mancini, M. et al. An interactive meta-analysis of MRI biomarkers of myelin. eLife 9, e61523 (2020).

    Article  Google Scholar 

  139. Kiselev, V. G. & Novikov, D. S. Transverse NMR relaxation in biological tissues. Neuroimage 182, 149–168 (2018).

    Article  Google Scholar 

  140. Halle, B. & Denisov, V. P. A new view of water dynamics in immobilized proteins. Biophys. J. 69, 242–249 (1995).

    Article  ADS  Google Scholar 

  141. Fullerton, G. D., Potter, J. L. & Dornbluth, N. C. NMR relaxation of protons in tissues and other macromolecular water solutions. Magn. Reson. Imaging 1, 209–226 (1982).

    Article  Google Scholar 

  142. Chávez, F. V. & Halle, B. Molecular basis of water proton relaxation in gels and tissue. Magn. Reson. Med. 56, 73–81 (2006).

    Article  Google Scholar 

  143. Barta, R. et al. Modeling T1 and T2 relaxation in bovine white matter. J. Magn. Reson. 259, 56–67 (2015).

    Article  ADS  Google Scholar 

  144. Labadie, C. et al. Myelin water mapping by spatially regularized longitudinal relaxographic imaging at high magnetic fields. Magn. Reson. Med. 71, 375–387 (2014).

    Article  Google Scholar 

  145. Pine, K. J., Davies, G. R. & Lurie, D. J. Field-cycling NMR relaxometry with spatial selection. Magn. Reson. Med. 63, 1698–1702 (2010).

    Article  Google Scholar 

  146. Weiger, M. et al. Advances in MRI of the myelin bilayer. Neuroimage 217, 116888 (2020).

    Article  Google Scholar 

  147. Stanisz, G. J., Kecojevic, A., Bronskill, M. J. & Henkelman, R. M. Characterizing white matter with magnetization transfer and T2. Magn. Reson. Med. 42, 1128–1136 (1999). Introduces the four-compartment model for T2 and MT featuring exchange of the visible water pools.

    Article  Google Scholar 

  148. West, K. L. et al. Myelin volume fraction imaging with MRI. Neuroimage 182, 511–521 (2018). Relates MRI-based myelin measures to the myelin volume fraction determined by gold standard electron microscopy in hypomyelinated and hypermyelinated mouse models.

    Article  Google Scholar 

  149. Schmierer, K. et al. Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain. J. Magn. Reson. Imaging 26, 41–51 (2007).

    Article  Google Scholar 

  150. Laule, C. & Moore, G. R. W. Myelin water imaging to detect demyelination and remyelination and its validation in pathology. Brain Pathol. 28, 750–764 (2018).

    Article  Google Scholar 

  151. Varma, G. et al. Interpretation of magnetization transfer from inhomogeneously broadened lines (ihMT) in tissues as a dipolar order effect within motion restricted molecules. J. Magn. Reson. 260, 67–76 (2015).

    Article  ADS  Google Scholar 

  152. Manning, A. P., Chang, K. L., MacKay, A. L. & Michal, C. A. The physical mechanism of “inhomogeneous” magnetization transfer MRI. J. Magn. Reson. 274, 125–136 (2017).

    Article  ADS  Google Scholar 

  153. Duhamel, G. et al. Validating the sensitivity of inhomogeneous magnetization transfer (ihMT) MRI to myelin with fluorescence microscopy. Neuroimage 199, 289–303 (2019).

    Article  Google Scholar 

  154. Mezer, A. et al. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat. Med. 19, 1667–1672 (2013).

    Article  Google Scholar 

  155. Zimmerman, J. R. & Brittin, W. E. Nuclear magnetic resonance studies in multiple phase systems: lifetime of a water molecule in an adsorbing phase on silica gel. J. Phys. Chem. 61, 1328–1333 (1957).

    Article  Google Scholar 

  156. Schmierer, K., Scaravilli, F., Altmann, D. R., Barker, G. J. & Miller, D. H. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 56, 407–415 (2004).

    Article  Google Scholar 

  157. Menon, R. S., Rusinko, M. S. & Allen, P. S. Proton relaxation studies of water compartmentalization in a model neurological system. Magn. Reson. Med. 28, 264–274 (1992).

    Article  Google Scholar 

  158. Dick, F. et al. In vivo functional and myeloarchitectonic mapping of human primary auditory areas. J. Neurosci. 32, 16095–16105 (2012).

    Article  Google Scholar 

  159. Dinse, J. et al. A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI. Neuroimage 114, 71–87 (2015).

    Article  Google Scholar 

  160. Helms, G. & Hagberg, G. E. In vivo quantification of the bound pool T1 in human white matter using the binary spin-bath model of progressive magnetization transfer saturation. Phys. Med. Biol. 54, N529–N540 (2009).

    Article  ADS  Google Scholar 

  161. Koenig, S. H., Brown, R. D. 3rd, Spiller, M. & Lundbom, N. Relaxometry of brain: why white matter appears bright in MRI. Magn. Reson. Med. 14, 482–495 (1990).

    Article  Google Scholar 

  162. Schyboll, F., Jaekel, U., Petruccione, F. & Neeb, H. Origin of orientation-dependent R1 (=1/T1) relaxation in white matter. Magn. Reson. Med. 84, 2713–2723 (2020).

    Article  Google Scholar 

  163. Fukunaga, M. et al. Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast. Proc. Natl Acad. Sci. USA 107, 3834–3839 (2010).

    Article  ADS  Google Scholar 

  164. Brammerloh, M. et al. Measuring the iron content of dopaminergic neurons in substantia nigra with MRI relaxometry. Preprint at bioRxiv https://doi.org/10.1101/2020.07.01.170563 (2020).

  165. Wen, J., Goyal, M. S., Astafiev, S. V., Raichle, M. E. & Yablonskiy, D. A. Genetically defined cellular correlates of the baseline brain MRI signal. Proc. Natl Acad. Sci. USA 115, E9727–E9736 (2018).

    Article  Google Scholar 

  166. Yablonskiy, D. A. & Haacke, E. M. Theory of NMR signal behavior in magnetically inhomogeneous tissues: the static dephasing regime. Magn. Reson. Med. 32, 749–763 (1994).

    Article  Google Scholar 

  167. Luo, J., Jagadeesan, B. D., Cross, A. H. & Yablonskiy, D. A. Gradient echo plural contrast imaging — signal model and derived contrasts: T2*, T1, phase, SWI, T1f, FST2*and T2*-SWI. Neuroimage 60, 1073–1082 (2012).

    Article  Google Scholar 

  168. Bender, B. & Klose, U. The in vivo influence of white matter fiber orientation towards B0 on T2* in the human brain. NMR Biomed. 23, 1071–1076 (2010).

    Article  Google Scholar 

  169. Rudko, D. A. & Klassen, L. M. Origins of R2* orientation dependence in gray and white matter. Proc. Natl Acad. Sci. USA 111, E159–E167 (2014).

    Article  Google Scholar 

  170. Marques, J. P. & Bowtell, R. Application of a Fourier-based method for rapid calculation of field inhomogeneity due to spatial variation of magnetic susceptibility. Concepts Magn. Reson. B 25, 65–78 (2005).

    Article  Google Scholar 

  171. Deistung, A., Schweser, F. & Reichenbach, J. R. Overview of quantitative susceptibility mapping. NMR Biomed. 30, e3569 (2017).

    Article  Google Scholar 

  172. Song, S.-K. et al. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage 17, 1429–1436 (2002).

    Article  Google Scholar 

  173. Wheeler-Kingshott, C. A. M. & Cercignani, M. About “axial” and “radial” diffusivities. Magn. Reson. Med. 61, 1255–1260 (2009).

    Article  Google Scholar 

  174. Le Bihan, D. Looking into the functional architecture of the brain with diffusion MRI. Nat. Rev. Neurosci. 4, 469–480 (2003).

    Article  Google Scholar 

  175. Jeurissen, B., Descoteaux, M., Mori, S. & Leemans, A. Diffusion MRI fiber tractography of the brain. NMR Biomed. 32, e3785 (2019).

    Article  Google Scholar 

  176. Lynn, C. W. & Bassett, D. S. The physics of brain network structure, function and control. Nat. Rev. Phys. 1, 318–332 (2019).

    Article  Google Scholar 

  177. Niendorf, T., Norris, D. G. & Leibfritz, D. Detection of apparent restricted diffusion in healthy rat brain at short diffusion times. Magn. Reson. Med. 32, 672–677 (1994).

    Article  Google Scholar 

  178. Stanisz, G. J., Szafer, A., Wright, G. A. & Henkelman, R. M. An analytical model of restricted diffusion in bovine optic nerve. Magn. Reson. Med. 37, 103–111 (1997).

    Article  Google Scholar 

  179. Lee, J.-H. & Springer, C. S. Jr. Effects of equilibrium exchange on diffusion-weighted NMR signals: the diffusigraphic “shutter-speed”. Magn. Reson. Med. 49, 450–458 (2003).

    Article  Google Scholar 

  180. Georgi, J., Metere, R., Jäger, C., Morawski, M. & Möller, H. E. Influence of the extracellular matrix on water mobility in subcortical gray matter. Magn. Reson. Med. 81, 1265–1279 (2019).

    Article  Google Scholar 

  181. Niendorf, T., Dijkhuizen, R. M., Norris, D. G., van Lookeren Campagne, M. & Nicolay, K. Biexponential diffusion attenuation in various states of brain tissue: implications for diffusion-weighted imaging. Magn. Reson. Med. 36, 847–857 (1996).

    Article  Google Scholar 

  182. Dhital, B., Labadie, C., Stallmach, F., Möller, H. E. & Turner, R. Temperature dependence of water diffusion pools in brain white matter. Neuroimage 127, 135–143 (2016).

    Article  Google Scholar 

  183. Güllmar, D., Haueisen, J. & Reichenbach, J. R. Analysis of b-value calculations in diffusion weighted and diffusion tensor imaging. Concepts Magn. Reson. A 25A, 53–66 (2005).

    Article  Google Scholar 

  184. Kiselev, V. G. Microstructure with diffusion MRI: what scale we are sensitive to? J. Neurosci. Methods 347, 108910 (2020).

    Article  Google Scholar 

  185. Einstein, A. Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Ann. Phys. 322, 549–560 (1905).

    Article  MATH  Google Scholar 

  186. Kiselev, V. G. & Il’yasov, K. A. Is the “biexponential diffusion” biexponential? Magn. Reson. Med. 57, 464–469 (2007).

    Article  Google Scholar 

  187. Callaghan, P. T., Coy, A., MacGowan, D., Packer, K. J. & Zelaya, F. O. Diffraction-like effects in NMR diffusion studies of fluids in porous solids. Nature 351, 467–469 (1991).

    Article  ADS  Google Scholar 

  188. Panagiotaki, E. et al. Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. Neuroimage 59, 2241–2254 (2012).

    Article  Google Scholar 

  189. Lampinen, B. et al. Searching for the neurite density with diffusion MRI: challenges for biophysical modeling. Hum. Brain Mapp. 40, 2529–2545 (2019).

    Article  Google Scholar 

  190. Alexander, D. C. et al. Orientationally invariant indices of axon diameter and density from diffusion MRI. Neuroimage 52, 1374–1389 (2010).

    Article  Google Scholar 

  191. Horowitz, A. et al. In vivo correlation between axon diameter and conduction velocity in the human brain. Brain Struct. Funct. 220, 1777–1788 (2015).

    Article  Google Scholar 

  192. Innocenti, G. M., Caminiti, R. & Aboitiz, F. Comments on the paper by Horowitz et al. (2014). Brain Struct. Funct. 220, 1789–1790 (2015).

    Article  Google Scholar 

  193. Horowitz, A., Barazany, D., Tavor, I., Yovel, G. & Assaf, Y. Response to the comments on the paper by Horowitz et al. (2014). Brain Struct. Funct. 220, 1791–1792 (2015).

    Article  Google Scholar 

  194. Waxman, S. G. Determinants of conduction velocity in myelinated nerve fibers. Muscle Nerve 3, 141–150 (1980).

    Article  Google Scholar 

  195. Jelescu, I. O. et al. One diffusion acquisition and different white matter models: how does microstructure change in human early development based on WMTI and NODDI? Neuroimage 107, 242–256 (2015).

    Article  Google Scholar 

  196. Kiselev, V. G. & Posse, S. Analytical model of susceptibility-induced MR signal dephasing: effect of diffusion in a microvascular network. Magn. Reson. Med. 41, 499–509 (1999).

    Article  Google Scholar 

  197. Chan, K.-S. & Marques, J. P. Multi-compartment relaxometry and diffusion informed myelin water imaging – promises and challenges of new gradient echo myelin water imaging methods. Neuroimage 221, 117159 (2020).

    Article  Google Scholar 

  198. Veraart, J., Novikov, D. S. & Fieremans, E. TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T2 relaxation times. Neuroimage 182, 360–369 (2018).

    Article  Google Scholar 

  199. Gong, T. et al. MTE-NODDI: multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times. Neuroimage 217, 116906 (2020).

    Article  Google Scholar 

  200. Mohammadi, S. et al. Whole-brain in-vivo measurements of the axonal g-ratio in a group of 37 healthy volunteers. Front. Neurosci. 9, 441 (2015).

    Article  Google Scholar 

  201. Stikov, N. et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage 118, 397–405 (2015).

    Article  Google Scholar 

  202. Ellerbrock, I. & Mohammadi, S. Four in vivo g-ratio-weighted imaging methods: comparability and repeatability at the group level. Hum. Brain Mapp. 39, 24–41 (2018).

    Article  Google Scholar 

  203. Berman, S., West, K. L., Does, M. D., Yeatman, J. D. & Mezer, A. A. Evaluating g-ratio weighted changes in the corpus callosum as a function of age and sex. Neuroimage 182, 304–313 (2018).

    Article  Google Scholar 

  204. Stikov, N. et al. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. Neuroimage 54, 1112–1121 (2011). Introduces a biophysical model for in vivo measurement of the MRI-based g-ratio by combining myelin and diffusion MRI.

    Article  Google Scholar 

  205. Callaghan, M. F., Helms, G., Lutti, A., Mohammadi, S. & Weiskopf, N. A general linear relaxometry model of R1 using imaging data. Magn. Reson. Med. 73, 1309–1314 (2015).

    Article  Google Scholar 

  206. Mangeat, G., Govindarajan, S. T., Mainero, C. & Cohen-Adad, J. Multivariate combination of magnetization transfer, T2* and B0 orientation to study the myelo-architecture of the in vivo human cortex. Neuroimage 119, 89–102 (2015).

    Article  Google Scholar 

  207. Draganski, B. et al. Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ). Neuroimage 55, 1423–1434 (2011).

    Article  Google Scholar 

  208. DeWeerdt, S. How to map the brain. Nature 571, S6–S8 (2019).

    Article  ADS  Google Scholar 

  209. Lee, H.-H. et al. Along-axon diameter variation and axonal orientation dispersion revealed with 3D electron microscopy: implications for quantifying brain white matter microstructure with histology and diffusion MRI. Brain Struct. Funct. 224, 1469–1488 (2019).

    Article  Google Scholar 

  210. Kleinnijenhuis, M., Johnson, E., Mollink, J., Jbabdi, S. & Miller, K. L. A semi-automated approach to dense segmentation of 3D white matter electron microscopy. Preprint at bioRxiv https://doi.org/10.1101/2020.03.19.979393 (2020).

  211. Lee, H.-H., Jespersen, S. N., Fieremans, E. & Novikov, D. S. The impact of realistic axonal shape on axon diameter estimation using diffusion MRI. Neuroimage 223, 117228 (2020).

    Article  Google Scholar 

  212. Andersson, M. et al. Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure-function relationship. Proc. Natl Acad. Sci. USA 117, 33649–33659 (2020).

    Article  Google Scholar 

  213. Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013).

    Article  ADS  Google Scholar 

  214. Morawski, M. et al. Developing 3D microscopy with CLARITY on human brain tissue: towards a tool for informing and validating MRI-based histology. Neuroimage 182, 417–428 (2018).

    Article  Google Scholar 

  215. Amunts, K., Mohlberg, H., Bludau, S. & Zilles, K. Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Science 369, 988–992 (2020).

    Article  ADS  Google Scholar 

  216. Bulk, M. et al. Quantitative comparison of different iron forms in the temporal cortex of Alzheimer patients and control subjects. Sci. Rep. 8, 6898 (2018).

    Article  ADS  Google Scholar 

  217. Davis, H. C. et al. Mapping the microscale origins of magnetic resonance image contrast with subcellular diamond magnetometry. Nat. Commun. 9, 131 (2018).

    Article  ADS  Google Scholar 

  218. Leuze, C. et al. The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing. Neuroimage 156, 412–422 (2017).

    Article  Google Scholar 

  219. Kampmann, M. CRISPR-based functional genomics for neurological disease. Nat. Rev. Neurol. 16, 465–480 (2020).

    Article  Google Scholar 

  220. Massner, C. et al. Genetically controlled lysosomal entrapment of superparamagnetic ferritin for multimodal and multiscale imaging and actuation with low tissue attenuation. Adv. Funct. Mater. 28, 1706793 (2018).

    Article  Google Scholar 

  221. Cakir, B. et al. Engineering of human brain organoids with a functional vascular-like system. Nat. Methods 16, 1169–1175 (2019).

    Article  Google Scholar 

  222. Schmierer, K. et al. Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation. Magn. Reson. Med. 59, 268–277 (2008).

    Article  Google Scholar 

  223. Helbling, S. et al. Structure predicts function: combining non-invasive electrophysiology with in-vivo histology. Neuroimage 108, 377–385 (2015).

    Article  Google Scholar 

  224. Novikov, D. S., Jensen, J. H., Helpern, J. A. & Fieremans, E. Revealing mesoscopic structural universality with diffusion. Proc. Natl Acad. Sci. USA 111, 5088–5093 (2014). Introduces universality classes of structural correlations and describes how they affect the MRI diffusion measurements.

    Article  ADS  Google Scholar 

  225. Levitt, M. & Warshel, A. Computer simulation of protein folding. Nature 253, 694–698 (1975).

    Article  ADS  Google Scholar 

  226. Noid, W. G. Perspective: Coarse-grained models for biomolecular systems. J. Chem. Phys. 139, 090901 (2013).

    Article  ADS  Google Scholar 

  227. De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).

    Article  Google Scholar 

  228. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  ADS  Google Scholar 

  229. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

    Article  ADS  Google Scholar 

  230. Alexander, D. C. et al. Image quality transfer and applications in diffusion MRI. Neuroimage 152, 283–298 (2017).

    Article  Google Scholar 

  231. Wilm, B. J. et al. Diffusion MRI with concurrent magnetic field monitoring. Magn. Reson. Med. 74, 925–933 (2015).

    Article  Google Scholar 

  232. Amunts, K. et al. BigBrain: an ultrahigh-resolution 3D human brain model. Science 340, 1472–1475 (2013).

    Article  ADS  Google Scholar 

  233. Xu, Q. et al. CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research. Mol. Psychiatry 25, 517–529 (2020).

    Article  Google Scholar 

  234. Meyer, A. Paul Flechsig’s system of myelogenetic cortical localization in the light of recent research in neuroanatomy and neurophysiology part II. Can. J. Neurol. Sci. 8, 95–104 (1981).

    Article  Google Scholar 

  235. MacKay, A. L. & Laule, C. Magnetic resonance of myelin water: an in vivo marker for myelin. Brain Plast. 2, 71–91 (2016).

    Article  Google Scholar 

  236. Panda, A. et al. Magnetic resonance fingerprinting – an overview. Curr. Opin. Biomed. Eng. 3, 56–66 (2017).

    Article  Google Scholar 

  237. Papazoglou, S. et al. Biophysically motivated efficient estimation of the spatially isotropic component from a single gradient-recalled echo measurement. Magn. Reson. Med. 82, 1804–1811 (2019).

    Article  Google Scholar 

  238. Gil, R. et al. An in vivo study of the orientation-dependent and independent components of transverse relaxation rates in white matter. NMR Biomed. 29, 1780–1790 (2016).

    Article  Google Scholar 

  239. Wharton, S. & Bowtell, R. Gradient echo based fiber orientation mapping using R2* and frequency difference measurements. Neuroimage 83, 1011–1023 (2013).

    Article  Google Scholar 

  240. Rabi, I. I., Ramsey, N. F. & Schwinger, J. Use of rotating coordinates in magnetic resonance problems. Rev. Mod. Phys. 26, 167–171 (1954).

    Article  ADS  MATH  Google Scholar 

  241. Solomon, I. Relaxation processes in a system of two spins. Phys. Rev. 99, 559–565 (1955).

    Article  ADS  Google Scholar 

  242. Wolff, S. D. & Balaban, R. S. Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Magn. Reson. Med. 10, 135–144 (1989).

    Article  Google Scholar 

  243. Abragam, A. The Principles of Nuclear Magnetism (Clarendon Press, 1961).

  244. Sled, J. G. & Pike, G. B. Quantitative imaging of magnetization transfer exchange and relaxation properties in vivo using MRI. Magn. Reson. Med. 46, 923–931 (2001).

    Article  Google Scholar 

  245. Deoni, S. C. L., Rutt, B. K., Arun, T., Pierpaoli, C. & Jones, D. K. Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magn. Reson. Med. 60, 1372–1387 (2008).

    Article  Google Scholar 

  246. Stejskal, E. O. & Tanner, J. E. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42, 288–292 (1965).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

The authors thank H. Möller (MPI-CBS, Leipzig), J. Schmidt (MPI-CBS, Leipzig) and R. Valiullin (Leipzig University) for their very helpful comments on earlier versions of the manuscript. They thank T. Reinert (MPI-CBS, Leipzig) and M. Morozova (MPI-CBS, Leipzig) for providing data for illustrations, including electron microscopy and PIXE. They also thank J. Grant (MPI-CBS, Leipzig) for proofreading an earlier version of the manuscript. N.W. received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 616905. N.W. also received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 681094 and the Federal Ministry of Education and Research (BMBF; 01EW1711A and B) in the framework of ERA-NET NEURON. G.H. was funded by the Swedish Research Council (NT 2014-6193). S.M. was supported by the ERA-NET NEURON (hMRIofSCI), the BMBF (01EW1711A and B) and the German Research Foundation (DFG Priority Program 2041 ‘Computational Connectomics’ (AL 1156/2-1; GE 2967/1-1; MO 2397/5-1; MO 2249/3-1), DFG Emmy Noether Stipend: MO 2397/4-1).

Author information

Authors and Affiliations

  1. Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

    Nikolaus Weiskopf, Luke J. Edwards, Gunther Helms, Siawoosh Mohammadi & Evgeniya Kirilina

  2. Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany

    Nikolaus Weiskopf

  3. Department of Clinical Sciences, Lund, Lund University, Lund, Sweden

    Gunther Helms

  4. Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Siawoosh Mohammadi

  5. Center for Cognitive Neuroscience Berlin, Free University Berlin, Berlin, Germany

    Evgeniya Kirilina

Authors
  1. Nikolaus Weiskopf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luke J. Edwards
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gunther Helms
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Siawoosh Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Evgeniya Kirilina
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to all aspects of the article.

Corresponding author

Correspondence to Nikolaus Weiskopf.

Ethics declarations

Competing interests

The Max Planck Institute for Human Cognitive and Brain Sciences has an institutional research agreement with Siemens Healthcare. N.W. holds a patent on MRI data acquisition during spoiler gradients (United States Patent 10,401,453). N.W. was a speaker at an event organized by Siemens Healthcare and was reimbursed for the travel expenses.

Additional information

Peer review information

Nature Reviews Physics thanks Kathryn Keenan, Richard Spencer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

BRAIN Initiative: https://braininitiative.nih.gov/

BigBrain Project: https://bigbrainproject.org/

German National Cohort: https://nako.de/informationen-auf-englisch/

UK Biobank: https://www.ukbiobank.ac.uk/

Glossary

MRI pulse sequence

Sequence of radio frequency pulses, spatially varying magnetic field gradients and data acquisition periods executed on the MRI scanner for creating, manipulating and measuring the MRI signal.

Longitudinal relaxation time

The characteristic time T1 for the return of the net magnetization of the spin ensemble to its thermal equilibrium value parallel to the external magnetic field (Box 1).

Transverse relaxation time

The characteristic time T2 describing the irreversible loss of the magnetization transverse to the static magnetic field (Box 1).

Effective transverse relaxation time

The characteristic time T2* describing the decay of the magnetization transverse to the static magnetic field due to reversible and irreversible processes (Box 1).

Proton density

Proton density reflects the content of magnetic-resonance-visible free water in the tissue, which is often expressed as a percentage of the proton concentration in water.

Iron

Iron is accumulated in the brain to cover demands for oxygen transport, myelination and neurotransmitter synthesis. Iron overload in ageing leads to cellular damage and neurodegeneration.

Inverse problem

In physics, this refers to inferring unknown physical properties of a system from measurements.

Forward model

A forward model predicts measurements from physical properties of a system.

Ill-posed problem

A problem is regarded as ill-posed (in contrast to well-posed problems) when a solution either does not exist, is not unique (often the case in qMRI/hMRI) or is unstable in the presence of small perturbations (such as noise).

Signal-to-noise ratio

A measure comparing the level of signal of interest to the level of noise. Noise may include thermal and instrumental noise, as well as physiological processes of no interest.

Specific absorption rate

(SAR). Measure of radio frequency power deposition leading to tissue heating, typically given in Watts per kilogram of tissue.

Peripheral nerve stimulation

(PNS). Stimulation of peripheral nerve fibres due to the electric field induced by the fast switching of magnetic field gradients.

RF coil arrays

Coils for receiving and transmitting radio frequency fields used to manipulate the spin system and read out its magnetization state.

Gradient systems

Systems consisting of a power amplifier and a set of three gradient coils providing switchable magnetic field gradients for spatial and diffusion encoding along the three spatial axes.

Axon

Long projection of a neuronal cell body that transmits neuronal signals over long distances.

Navigators

Short, low-resolution, self-contained acquisitions inserted into a pulse sequence to measure and correct for phase instabilities or motion.

Shimming

Shimming increases the magnetic field (B0) spatial homogeneity in the imaged body part or object. This is achieved using additional resistive coils that can generate various field distributions (linear and higher order) to compensate for inhomogeneities.

Voxel

The smallest 3D volume element in an imaging volume (typically represented as a cuboid in a 3D grid) as a logical extension of a 2D pixel (picture element).

Glial cells

Non-neuronal cells in the nervous system that support and protect neurons, maintain homeostasis and form myelin.

Myelin

A lipid-rich insulating substance surrounding axons that increases nerve conduction velocity.

Bloembergen–Purcell–Pound theory

Explains that the main determinant of longitudinal and transverse relaxation rates in liquids is molecular motion stochastically modulating intramolecular and intermolecular dipolar interactions.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weiskopf, N., Edwards, L.J., Helms, G. et al. Quantitative magnetic resonance imaging of brain anatomy and in vivo histology. Nat Rev Phys 3, 570–588 (2021). https://doi.org/10.1038/s42254-021-00326-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42254-021-00326-1

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing