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Efficient whole-brain tract-specific T1 mapping with slice-shuffled inversion-recovery diffusion-weighted imaging at 3T
Daniel A. Andrews1,2, Jennifer S. W. Campbell1, Ilana R. Leppert1, Daniel J. Park3, G. Bruce Pike1,2,4,5, Jonathan R. Polimeni3,6,7, and Christine L. Tardif1,2,8

1McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada, 2Department of Biomedical Engineering, McGill University, Montreal, QC, Canada, 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 4Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, 5Department of Radiology and Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada, 6Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 7Department of Radiology, Harvard Medical School, Harvard University, Boston, MA, United States, 8Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada

Synopsis

The majority of voxels in magnetic resonance images of human white matter contain crossing tracts. Conventional T1 mapping techniques are sensitive to the myelin content of the entire voxel and are thus non-specific to individual tract myelination. We recently proposed an efficient slice-shuffled, multiband accelerated inversion-recovery diffusion-weighted MRI (IR-DWI) sequence at 3 Tesla. Here we demonstrate that IR-DWI can be used to estimate tract-specific T1 in voxels with crossing fibres in a phantom and in a healthy subject in a reasonable scan time.

Introduction

White matter tracts can be reconstructed from diffusion-weighted imaging (DWI) data to form a structural network. However, these reconstructions are incomplete since they do not directly include differences in tract-specific myelin content, a measure that is relevant to the study of plasticity, healthy aging, and many disorders. Conventional myelin mapping techniques do not allow us to estimate the myelination of individual fibres in voxels where they cross (>60% of voxels in white matter), which limits our ability to study how altered myelination of particular tracts affects structural connectivity throughout the brain1,2.

A recently proposed MRI method combines inversion recovery (IR, sensitive to T1) and DWI (sensitive to fibre orientation) to distinguish the T1-weighted signal contribution from each tract in a voxel and assign each a unique T13,4. IR-DWI was shown to disentangle T1 values of multiple tracts in a voxel at 7T, but the original implementation is limited by a long scan time (50 mins for 21 slices)4. We recently developed an accelerated 3T IR-DWI implementation5 that employs two complementary acceleration techniques: simultaneous multi-slice imaging (SMS) and a slice-shuffled acquisition (Figure 1). Here, we demonstrate that using this accelerated IR-DWI sequence, we can accurately differentiate and map the T1 times of crossing fibres in a phantom and a healthy human subject.

Methods

Two pulse sequences are needed to distinguish multiple T1 values in a voxel: a high angular resolution diffusion imaging (HARDI) sequence, and an IR-DWI sequence. The HARDI sequence is first used to extract the fibre orientation, fibre volume fraction fi, and diffusion anisotropy of each fibre population i in each voxel. These parameters are fixed in the IR-DWI signal Equation 1 and T1i and Di are fit in each voxel to the IR-DWI data3,4.

A healthy human subject and a phantom comprised of two asparagus pieces were imaged on a Siemens 3T Prisma scanner using a 64-channel head coil. To alter their T1 times, the asparagus pieces were soaked in PBS (Asp. 1) and a Gadovist-PBS solution (Asp. 2, 6mM Gad.) before imaging. Even and odd numbered slices in the human scan were acquired after separate inversion pulses to mitigate slice cross-talk during the longitudinal T1 recovery. One b = 0 s/mm2 excitation dummy was placed at the start of each slice shuffle in the short TR human scan in order to ensure steady state (Figure 1). Table 1 lists scan parameters for the phantom and human scans.

HARDI data were de-noised, then the fibre volume fractions and fibre directions were extracted from the fibre orientation distribution in each voxel (calculated via constrained spherical deconvolution in MRTrix3)6,7. An apparent fibre density ≥ 0.2 threshold was set for all voxels for the subsequent fitting. The fibre diffusion anisotropy was extracted using a tortuosity model incorporating the intra-axonal volume fraction obtained from NODDI fitting. IR-DWI data were unshuffled and the tract-specific T1 was fit using Equation 1 with a Gaussian noise term8.

Results

Figure 2 a) shows T1 values of asparagus pieces 1 and 2 fit within single and crossing fibre voxels using IR-DWI data. When diffusion weighting is used in the IR-DWI acquisition, the T1 of each asparagus piece in crossing fibre voxels can be differentiated. Figure 2 b) shows the fibre-specific T1 map in the phantom generated from IR-DWI data.

Figure 3 shows tract-specific T1 estimates in voxels at the crossing of the anterior corpus callosum and cingulum in the human subject. T1 in the cingulum is higher than T1 in the corpus callosum in both single (p = 0.00002) and crossing fibre voxels (p = 0.0008). This agrees with the relative tract-specific T1 estimates obtained using IR-DWI at 7T by De Santis et al. (2016)4.

Discussion and Conclusion

Preliminary and accelerated 3T IR-DWI implementations were used to estimate fibre-specific T1 in a phantom and a healthy human subject, demonstrating the feasibility of IR-DWI at 3T. SMS, slice-shuffling, and a short TR combine to increase IR-DWI efficiency, allowing whole-brain imaging in under 30 minutes. Scan time can be further reduced to under 15 minutes when greater SMS or shuffle factors are used. This will reduce the number of sampled inversion times, as shown by Panchuelo et al.9.

The outcome of this work will give researchers a highly efficient method of estimating tract-specific myelination, including potentially tract-specific g-ratio, in-vivo10. IR-DWI will enable new research into how tract-specific myelination changes affect structural network connectivity throughout the brain in neurodevelopment, aging, cognitive training, and in neurological and psychiatric disorders characterized by myelin deficits11,12.

Acknowledgements

No acknowledgement found.

References

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2. Mancini, M., Giulietti, G., Dowell, N., Spanò, B., Harrison, N., Bozzali, M., Cercignani, M. (2017). Introducing axonal myelination in connectomics: A preliminary analysis of g-ratio distribution in healthy subjects. NeuroImage,182, 351-359. doi:10.1016/j.neuroimage.2017.09.018

3. De Santis, S., Barazany, D., Jones, D. K., Assaf, Y. (2016) Resolving relaxometry and diffusion properties within the same voxel in the presence of crossing fibres by combining inversion recovery and diffusion‐weighted acquisitions. Magn. Reson. Med. 75:372–380. doi: 10.1002/mrm.25644

4. De Santis S., Assaf Y., Jeurissen B., Jones D. K., Roebroeck A. (2016) T1 relaxometry of crossing fibres in the human brain. NeuroImage. 141:133–142. doi: 10.1016/j.neuroimage.2016.07.037.

5. Park D. J., Witzel T., Leppert L., Yen Y., Fan Q., Tardif C., Polimeni J. (2018). Rapid multi-inversion SMS-EPI integrated with gradient-echo, spin-echo and diffusion-weighted EPI acquisitions. 2018 Annual Meeting of the International Society for Magnetic Resonance in Medicine. 4229.

6. Jeurissen, B., Tournier, J., Dhollander, T., Connelly, A., & Sijbers, J. (2014). Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage,103, 411-426. doi:10.1016/j.neuroimage.2014.07.061


7. J. Veraart, D.S. Novikov, D. Christiaens, B. Ades-aron, J. Sijbers, and E. Fieremans (2016). Denoising of diffusion MRI using random matrix theory. NeuroImage,142, 394–406. doi:10.1016/j.neuroimage.2016.08.016

8. Assaf Y., Freidlin R. Z., Rohde G. K., & Basser P. J. (2004). New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Magnetic Resonance in Medicine,52(5), 965-978. doi:10.1002/mrm.20274

9. Panchuelo, R. S., Turner R., Mougin O., Francis S. (2018). A 2D multi-shot inversion recovery EPI (MS-IR-EPI) sequence for high spatial resolution T1 mapping at 7T. 2018 Annual Meeting of the International Society for Magnetic Resonance in Medicine. 0060. 


10. Campbell, J. S., Leppert, I. R., Narayanan, S., Boudreau, M., Duval, T., Cohen-Adad, J., Pike, G. B, Stikov, N. (2018). Promise and pitfalls of g-ratio estimation with MRI. NeuroImage,182, 80-96. doi:10.1016/j.neuroimage.2017.08.038

11. Caeyenberghs, K., Metzler-Baddeley, C., Foley, S., & Jones, D. K. (2016). Dynamics of the Human Structural Connectome Underlying Working Memory Training. Journal of Neuroscience,36(14), 4056-4066. doi:10.1523/jneurosci.1973-15.2016

12. Heuvel, M. P., Mandl, R. C., Stam, C. J., Kahn, R. S., & Pol, H. E. (2010). Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis. Journal of Neuroscience,30(47), 15915-15926. doi:10.1523/jneurosci.2874-10.2010

Figures

Figure 1. IR-DWI pulse sequence applied in the human scan, where a shuffle factor of 2 (i.e. slice-shuffle in sets of 2) and an SMS factor of 2 were used. One b = 0 s/mm2 excitation dummy was included at the start of each slice shuffle in order to ensure steady state. Even and odd slices were acquired after separate inversion pulses in order to mitigate slice cross-talk during longitudinal magnetization recovery. A shuffle factor of 1 and no SMS were used in the phantom scan. No imaging dummies were used in the phantom scan.

Equation 1. IR-DWI signal equation where S: total IR-DWI signal; fi: fibre volume fraction i; TI: inversion time; T1i: T1 of fibre population i; Di: diffusion tensor i; b: b-value; g: gradient direction; and η2: Gaussian noise term.

Figure 2. Phantom experiment. a) T1 values of asparagus pieces 1 and 2 in single and crossing fibre voxels, and b) fibre-specific T1 map of the asparagus phantom generated using IR-DWI data. T1s generated from diffusion-weighted IR-DWI data in crossing fibre voxels are differentiated, while T1s estimated in crossing fibre voxels using non-diffusion-weighted IR-DWI data are intermediate to T1s calculated in single fibre voxels. Colour in b) corresponds to T1 magnitude. The orientation of each stick in each T1 map voxel corresponds to a dominant diffusion direction (i.e. a unique asparagus piece).

Figure 3. Human experiment. ROI a) shows tract-specific T1 values at the crossing of the anterior corpus callosum and cingulum of a healthy human subject generated using IR-DWI data. The colour map in the brain image corresponds to the primary diffusion direction. The cingulum in green is oriented in the A-P and the corpus callosum in red is orientated in the L-R direction. Bar plot b) shows that the cingulum has a higher T1 than the corpus callosum in both single fibre voxels (p = 0.00002) and crossing fibre voxels (p = 0.0008).

Table 1. HARDI and IR-DWI phantom and human subject scan parameters.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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