This lecture will provide a brief overview of technical considerations involved in in vivo diffusion MR microstructural imaging studies with a focus on human neuroimaging. We will cover biophysical models and signal representations of the diffusion MRI signal as they relate to probing tissue microstructure in the human brain. We will study metrics derived using different diffusion models and acquisition schemes in the setting of normal development and pathological changes in white matter disease. In addition, the specific advantages of high-gradient systems for characterizing tissue microstructure for in vivo human imaging will be explored.
- To familiarize the user with technical considerations involved in in vivo diffusion MR microstructural imaging
- To understand the applications of in vivo diffusion microstructural imaging in gaining insight into normal brain development and white matter pathology
- To explore the benefits of using high diffusion-encoding gradient strengths for imaging of tissue microstructure
Diffusion MRI approaches for inferring information regarding tissue microstructure fall into two general categories: those based on biophysical models, in which tissue components are modeled as geometric compartments, such as CHARMED (1, 2), AxCaliber (3) and related models (4), and NODDI (5); and model-independent approaches to representing the diffusion signal, such as diffusion kurtosis imaging (6). A wealth of modeling and theoretic approaches for diffusion microstructural imaging have emerged in the last decade and remain an active area of research and debate (7, 8).
Experimental constraints that must be considered in applying tissue microstructure models for in vivo diffusion MR imaging include the range of acquisition parameters accessible on human MR scanners, such as diffusion-encoding gradient strength(s), diffusion time, and diffusion pulse duration, as well as signal-to-noise ratio considerations with regard to echo time, spatial resolution and overall imaging time. Techniques for mitigating motion, susceptibility, and eddy current artifacts will be briefly discussed.
Recent advances in human MR systems (e.g., high performance gradients and high sensitivity RF coils) and imaging pulse sequences have enabled a rich array of diffusion measurements that allow unparalleled in vivo assessment of neural tissue microstructure, such as the relative size and packing density of cells and axons. The sensitivity of compartment size mapping techniques to small pore sizes is limited by the maximum gradient strength of clinical MR scanners (9, 10). The recent availability of higher maximum gradient strengths on human MRI scanners (11-14) has enabled the translation of these methods from animal (15) and ex vivo studies (3, 4, 16-18) to the in vivo human brain (4, 10, 19-21). Such technological advancements offer the possibility of in vivo diffusion microscopy with unprecedented resolution of fine white matter structure (14) and micron-sized axons (10, 21) in the living human brain for the study of multiple sclerosis (22) and other neurological disorders affecting white matter.
Related techniques such as NODDI measure orientational dispersion and neurite density (5), which has been shown to provide better distinction of microstructural disruption in white matter compared to conventional DTI metrics. NODDI utilizes a two-shell optimized acquisition at modest b-values and is supported by a user-friendly toolbox that is well-suited for large-scale studies (23) and clinical imaging. To date, NODDI has been applied to study white matter microstructure in newborns (24) and the developing human brain (25). NODDI has also been used to detect changes in neurite density across the lifespan (26) and probe gray matter pathology in a number of neurological conditions such as epilepsy (27) and neurodegenerative disorders (28).
The advent of dedicated high-gradient human MR scanners has enabled advanced applications of diffusion MRI to examine brain tissue microstructure and its disruption under pathological conditions. A valuable contribution of these systems, beyond signal-to-noise ratio improvements for diffusion MRI, is elucidating the dependence of microstructural metrics on the accessible diffusion MRI parameters in vivo, which is critical for gaining a better understanding of the complex relationship between the diffusion-weighted MR signal and underlying tissue microstructure. In addition, there remains a great need for systematic validation studies that relate microstructural metrics estimated in vivo to histopathology. These multi-faceted efforts will ultimately help to facilitate the translation of microstructural imaging methods to the clinical arena.
1. Assaf Y, Freidlin RZ, Rohde GK, Basser PJ. New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Magn Reson Med. 2004;52(5):965-78.
2. Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27(1):48-58.
3. 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(6):1347-54.
4. Alexander DC, Hubbard PL, Hall MG, Moore EA, Ptito M, Parker GJ, et al. Orientationally invariant indices of axon diameter and density from diffusion MRI. Neuroimage. 2010;52(4):1374-89.
5. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61(4):1000-16.
6. Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage. 2011;58(1):177-88.
7. Alexander DC, Dyrby TB, Nilsson M, Zhang H. Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. 2017.
8. Novikov DS, Kiselev VG, Jespersen SN. On modeling. Magn Reson Med. 2018;79(6):3172-93.
9. Dyrby TB, Sogaard LV, Hall MG, Ptito M, Alexander DC. Contrast and stability of the axon diameter index from microstructure imaging with diffusion MRI. Magn Reson Med. 2012.
10. Huang SY, Nummenmaa A, Witzel T, Duval T, Cohen-Adad J, Wald LL, et al. The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter. Neuroimage. 2015;106:464-72.
11. Setsompop K, Kimmlingen R, Eberlein E, Witzel T, Cohen-Adad J, McNab JA, et al. Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. Neuroimage. 2013;80:220-33.
12. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TE, Bucholz R, et al. The Human Connectome Project: a data acquisition perspective. Neuroimage. 2012;62(4):2222-31.
13. Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, et al. The WU-Minn Human Connectome Project: an overview. Neuroimage. 2013;80:62-79.
14. Fan Q, Nummenmaa A, Witzel T, Zanzonico R, Keil B, Cauley S, et al. Investigating the capability to resolve complex white matter structures with high b-value diffusion magnetic resonance imaging on the MGH-USC Connectom scanner. Brain Connect. 2014;4(9):718-26.
15. Barazany D, Basser PJ, Assaf Y. In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. Brain. 2009;132(Pt 5):1210-20.
16. Morozov D, Bar L, Sochen N, Cohen Y. Modeling of the diffusion MR signal in calibrated model systems and nerves. NMR Biomed. 2013;26(12):1787-95.
17. Ong HH, Wehrli FW. Quantifying axon diameter and intra-cellular volume fraction in excised mouse spinal cord with q-space imaging. Neuroimage. 2010;51(4):1360-6.
18. 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(3):2241-54.
19. Zhang H, Hubbard PL, Parker GJ, Alexander DC. Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. Neuroimage. 2011;56(3):1301-15.
20. Ferizi U, Schneider T, Tariq M, Wheeler-Kingshott CA, Zhang H, Alexander DC. The importance of being dispersed: A ranking of diffusion MRI models for fibre dispersion using in vivo human brain data. Med Image Comput Comput Assist Interv. 2013;16(Pt 1):74-81.
21. McNab JA, Edlow BL, Witzel T, Huang SY, Bhat H, Heberlein K, et al. The Human Connectome Project and beyond: initial applications of 300 mT/m gradients. Neuroimage. 2013;80:234-45.
22. Huang SY, Tobyne SM, Nummenmaa A, Witzel T, Wald LL, McNab JA, et al. Characterization of Axonal Disease in Patients with Multiple Sclerosis Using High-Gradient-Diffusion MR Imaging. Radiology. 2016:151582.
23. Cox SR, Ritchie SJ, Tucker-Drob EM, Liewald DC, Hagenaars SP, Davies G, et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat Commun. 2016;7:13629.
24. Kunz N, Zhang H, Vasung L, O'Brien KR, Assaf Y, Lazeyras F, et al. Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models. Neuroimage. 2014;96:288-99.
25. Chang YS, Owen JP, Pojman NJ, Thieu T, Bukshpun P, Wakahiro ML, et al. White Matter Changes of Neurite Density and Fiber Orientation Dispersion during Human Brain Maturation. PLoS One. 2015;10(6):e0123656.
26. Nazeri A, Chakravarty MM, Rotenberg DJ, Rajji TK, Rathi Y, Michailovich OV, et al. Functional consequences of neurite orientation dispersion and density in humans across the adult lifespan. J Neurosci. 2015;35(4):1753-62.
27. Winston GP, Micallef C, Symms MR, Alexander DC, Duncan JS, Zhang H. Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy. Epilepsy Res. 2014;108(2):336-9.
28. Parker TD, Slattery CF, Zhang J, Nicholas JM, Paterson RW, Foulkes AJM, et al. Cortical microstructure in young onset Alzheimer's disease using neurite orientation dispersion and density imaging. Hum Brain Mapp. 2018.