Low frequency oscillating gradient spin-echo sequences improves sensitivity to axon diameter – an experimental validation study in live nerve tissue
Lebina Shrestha Kakkar1, Oscar Bennett1, David Atkinson2, Bernard Siow3, James Phillips4, Simon Richardson3, Enrico Kaden1, and Ivana Drobnjak1

1Centre for Medical Image Computing, University College London, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom, 3Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom, 4Department of Biomaterials & Tissue Engineering, University College London, London, United Kingdom

Synopsis

In a recent simulation study, Drobnjak et al demonstrates that low-frequency oscillating gradient spin-echo (OGSE) sequence is more sensitive to axon diameter than conventional pulsed gradient spin-echo (PGSE) sequence when fibre orientation is unknown or when fibre dispersion exists. Here, we experimentally validate this claim. We image a live rat sciatic nerve tissue using both sequences and compare its agreement with histology. Our results confirm that OGSE provides more accurate and precise diameter estimates compared to PGSE. Additionally, OGSE parameter estimates are less affected by reduced number of diffusion gradient directions, suggesting their use could translate into faster scan times.

Introduction

In a recent simulation study, Drobnjak et al1 demonstrates that low-frequency oscillating gradient spin-echo (OGSE) sequence is more sensitive to axon diameter than conventional pulsed gradient spin-echo (PGSE) sequence when fibre orientation is unknown or when fibre dispersion exists. Here, we experimentally validate this claim. We image a live rat sciatic nerve tissue using both sequences and compare its agreement with histology. Our aim is to investigate whether OGSE provides more accurate, and more precise measurements, and how robust these estimates are to the number of diffusion gradient directions.

Materials and Methods

Tissue sample preparation: The sciatic nerve of a freshly sacrificed rat was carefully ligated, excised and then immediately immersed in gassed artificial cerebrospinal fluid (aCSF). The sample was transferred to a MRI compatible incubation chamber2, where it was bathed in circulating aCSF at a constant temperature of 37°C, for the whole duration of the scan, to simulate an in-vivo environment. At the end of the scan, the nerve sample was fixed and histology of the same slice was later obtained with transmission electron microscopy (fig.1b,1c).

Optimised imaging protocol: We used the ActiveAx optimisation framework described previously by [3,4] to generate optimal OGSE and PGSE imaging protocols for 8, 16 and 32 gradient directions (fig.2). The algorithm minimizes the Cramer-Rao Lower Bound on parameters of a model to fit to a set of measurements. The tissue model used was a simplified composition of hindered and restricted diffusion compartments3,5,6 with a single axon diameter. The apriori model parameters reflected the expected microstructure in rat sciatic nerve and were taken from literature6,7,8: intrinsic diffusivity (Di=1.7µm2/ms), intra-axonal volume fraction (ficvf=0.6) and mixture of diameters (α=2.26µm,4.50µm,6.74µm). The protocols were run on a 9.4T Agilent Technologies, Inc. pre-clinical system with a diffusion gradient strength of 800mT/m. A single slice fast spin-echo readout with the following settings were used: echo train length=8, resolution=0.09×0.09×1mm3,8 repetitions, TR=1100ms and total acquisition time of 12 hours.

Model fitting: We used a three stage fitting model procedure3,6 (grid search, gradient descent, Markov Chain Monte Carlo (MCMC) posterior sampling (2000 burn-in, 30000 iterations)) to fit the model parameters to the measured signals from each voxel. The final estimated parameters were from the gradient descent stage. Their precision was described by the width of posterior distribution of the parameter obtained from the MCMC stage. We used TortZeppelinCylinder (as described by [9]) as our MR signal model. The parallel cylinders of a single radius represented the axons and TortZeppelin accounted for contributions to the signal from anisotropic and tortuous movement of water molecules in the extracellular space.

Results

Histological images were processed with an in-house segmentation algorithm to calculate the mean and standard deviation of axon diameter, α,3.64±0.34 µm, and intra-axonal volume fraction, ficvf,0.51±0.09 (fig.1). The 32 gradient direction OGSE protocol provides more accurate diameter estimates (fig.3bii) than those from the equivalent PGSE protocol (fig.3aii). OGSEs also exhibit significantly lower uncertainties in their parameter estimates of α and ficvf in comparison to PGSEs (fig.4). Furthermore, OGSE diameter estimates in the nerve changes significantly less than those from PGSE protocols when the number of directions were reduced from 32 to 16 and then to 8 directions (fig.5). A similar but not statistically significant pattern was observed for ficvf.

Discussion

Here we experimentally validated the simulation study in [1] using live nerve tissue and showed that low-frequency OGSE improves sensitivity to axon diameter in comparison to PGSE. We showed that OGSE provides more accurate and more precise axon diameter estimates compared to PGSE. Furthermore OGSE estimates are more robust to fewer gradient directions allowing for less measurements and faster scanning.

OGSE and PGSE sequences both seem to overestimate the true diameter and underestimate the intra-axonal volume fraction in the nerve tissue. This is possibly due to axon diameter shrinkage11 and further water loss from the extracellular compartment which happens during the tissue fixation process.

Our OGSE protocols contained low and high frequency waveforms (fig.2b). As shown in [1], the low-frequency OGSE provides higher sensitivity to axon diameter compared to PGSE because the free diffusion component of the restricted signal attenuates less in OGSE due to their lower b values, giving rise to higher SNR, and facilitating better detection of the smaller components. The high frequency OGSE, on the other hand, enhances sensitivity to estimates of diffusivity1,11.

Conclusion

This is the first time the comparison between OGSE and PGSE has been done in live nerve tissue using model based approach. Our study gives encouraging evidence for the superiority of low-frequency OGSE over PGSE protocols providing more accurate and precise axon diameter estimates.

Acknowledgements

We thank EPSRC for funding the research studentship of Lebina Shrestha Kakkar and Leverhulme Trust for the fellowship of Ivana Drobnjak.

References

1. Drobnjak, I., Zhang, H., Ianus¸, A., Kaden, E. & Alexander, D. C. PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study. Magnetic Resonance in Medicine. 2015 (Early View)

2. Richardson, S., Siow, B., Batchelor, A. M., Lythgoe, M. F. & Alexander, D. C. A viable isolated tissue system: A tool for detailed MR measurements and controlled perturbation in physiologically stable tissue. Magnetic Resonance in Medicine. 2013;69:1603–1610

3. Alexander, D. C. A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features. Magnetic Resonance in Medicine. 2008; 60:439–448

4. Drobnjak, I. & Alexander, D. C. Optimising time-varying gradient orientation for microstructure sensitivity in diffusion-weighted MR. Journal of Magnetic Resonance. 2011;212:344-354

5. Assaf Y, Blumenfeld-Katzir T, Yovel Y & Basser PJ. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magnetic Resonance in Medicine. 2008;59:1347–1354

6. Alexander, D. C., Hubbard, P. L., Hall, M. G., Moore, E. A., Ptito, M., Parker, G. J. M. & Dyrby,T. B. Orientationally invariant indices of axon diameter and density from diffusion MRI. NeuroImage. 2010; 52(4):1374–1389

7. Ikeda, M. & Oka, Y. The relationship between nerve conduction velocity and fiber morphology during peripheral nerve regeneration. Brain and Behavior. 2012; 2:382–390

8. Kempton, L. B., Gonzalez, M. H., Leven, R. M., Hughes, W. F., Beddow, S., Santhiraj, Y.,Archibald, S. J., El Hassan, B., Shott, S. & Kerns, J. M. Assessment of axonal growth into collagen nerve guides containing VEGF-transfected stem cells in matrigel. The Anatomical Record. 2009;292(2):214–224

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11. Drobnjak, I., Siow, B., & Alexander, D. C. Optimizing gradient waveforms for microstructure sensitivity in diffusion-weighted MR. Journal of Magnetic Resonance. 2010;206:41-51

Figures

(a) Non-diffusion weighted cross-sectional image of a rat sciatic nerve. (b) Histology, (c) magnified histology and (d) volume weighted diameter frequency histogram of the same slice of the nerve. ~30 magnified images from an ROI within (b) generated volume weighted diameter, α, 3.64±0.34µm and intra-axonal volume fraction, ficvf, 0.51±0.09.

Optimised (a) PGSE and (b) OGSE protocols for (i) 8, (ii) 16 and (iii) 32 gradient directions, with an assumption of unknown fibre orientation. Each protocol has three shells. Note that the second gradient waveform is flipped because of the 180° RF pulse.

Comparison of parameter estimates (i) intra-axonal volume fraction (ficvf), and (ii) diameter (α) between (a) 32 gradient direction PGSE protocol, (b) 32 gradient direction OGSE protocol and histology given in fig.1. The histology and model estimates are from the ROI within the sample.

Bar charts showing the relative mean uncertainty in the parameter estimates (a) intra-axonal volume fraction (ficvf) and (b) diameter (α) obtained from PGSE measurements and OGSE measurements within the ROI. At a significance level of p=0.05, the differences between PGSE and OGSE measurements are statistically significant (pα=0.0495, pficvf=0.0058).

Mean and standard deviation of the voxelwise absolute difference in the parameter maps of diameter for 8 and 16 gradient direction protocols with respect to 32 gradient direction protocols. Differences between OGSE and PGSE protocols are only statistically significant for diameter estimates with 8 direction protocols (pα<0.05).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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