Low frequency oscillating gradient spin-echo sequences improve sensitivity to axon diameter: An experimental study in viable nerve tissue

Abstract

Mapping axon diameters within the central and peripheral nervous system could play an important role in our understanding of nerve pathways, and help diagnose and monitor an array of neurological disorders. Numerous diffusion MRI methods have been proposed for imaging axon diameters, most of which use conventional single diffusion encoding (SDE) spin echo sequences. However, a growing number of studies show that oscillating gradient spin echo (OGSE) sequences can provide additional advantages over conventional SDE sequences. Recent theoretical results suggest that this is especially the case in realistic scenarios, such as when fibres have unknown or dispersed orientation. In the present study, we adopt the ActiveAx approach to experimentally investigate the extent of these advantages by comparing the performances of SDE and trapezoidal OGSE in viable nerve tissue. We optimise SDE and OGSE ActiveAx protocols for a rat peripheral nerve tissue and test their performance using Monte Carlo simulations and a 800 mT/m gradient strength pre-clinical imaging experiment. The imaging experiment uses excised sciatic nerve from a rat's leg placed in a MRI compatible viable isolated tissue (VIT) maintenance chamber, which keeps the tissue in a viable physiological state that preserves the structural complexity of the nerve and enables lengthy scan times. We compare model estimates to histology, which we perform on the nerve post scanning. Optimisation produces a three-shell SDE and OGSE ActiveAx protocol, with the OGSE protocol consisting of one SDE sequence and two low-frequency oscillating gradient waveform sequences. Both simulation and imaging results show that the OGSE ActiveAx estimates of the axon diameter index have a higher accuracy and a higher precision compared to those from SDE. Histology estimates of the axon diameter index in our nerve tissue samples are 4-5.8 μm and these are excellently matched with the OGSE estimates 4.2-6.5 μm, while SDE overestimates at 5.2-8 μm for the same sample. We found OGSE estimates to be more precise with on average a 0.5 μm standard deviation compared to the SDE estimates which have a 2 μm standard deviation. When testing the robustness of the estimates when the number of the diffusion gradient directions reduces, we found that both OGSE and SDE estimates are affected, however OGSE is more robust to these changes than the SDE. Overall, these results suggest, quantitatively and in in vivo conditions, that low-frequency OGSE sequences may provide improved accuracy of axon diameter mapping compared to standard SDE sequences.

Time‐dependent diffusion in undulating thin fibers: Impact on axon diameter estimation

“…The fifth limitation is that for the protocol optimized in reference and employed in this study the diffusion spectral content above 50 Hz is not encoded into the signal. Waveforms with high‐frequency content, using eg oscillating gradient waveforms, may be required to distinguish time‐dependent diffusion effects due to cylinder‐like structures and undulating thin fibers, because only in cylinders does the spectral height reach the bulk diffusivity. The sixth limitation is that we represent axons by undulating thin fibers while omitting a wealth of other microscopic features.…”

Single‐ and double‐Diffusion encoding MRI for studying ex vivo apparent axon diameter distribution in spinal cord white matter

“…When models of increasing complexity are suggested for gleaning microstructural information from diffusion MRI data, it is important to challenge them first on complex but well‐defined phantoms, then on isolated organs and last proceed to organs in vivo . Despite the limitations of phantoms as tissue models and the differences that may be found between ex vivo and in vivo MR measurements, such strategies were used to challenge different diffusion MR methodologies and modeling approaches . Recently, we have used a simple model based on a superposition of Gaussian diffusion and a series of restricted diffusions in infinite cylindrical geometries to fit the signal decay in spectroscopy based diffusion MR experiments .…”

“…29 Despite the limitations of phantoms as tissue models and the differences that may be found between ex vivo and in vivo MR measurements, such strategies were used to challenge different diffusion MR methodologies and modeling approaches. 35,50,[58][59][60][61][62][63][64][65][66][67] Recently, we have used a simple model based on a superposition of Gaussian diffusion and a series of restricted diffusions in infinite cylindrical geometries to fit the signal decay in spectroscopy based diffusion MR experiments. 62,65,68 These studies were first performed on well-defined microcapillary phantoms, where the ground truth is known and later on neuronal tissues such as pig optic nerves (which are considered microstructurally less complicated relative to a pig spinal cord).…”

Relevance of time‐dependence for clinically viable diffusion imaging of the spinal cord

“…The “stick” model was introduced previously for practical imaging in the brain, assuming negligible perpendicular intra‐axonal diffusion. Nonetheless, it is unclear to what extent such an approximation holds for the largest spinal axons, whose diameters can exceed those of the brain, and for the gradient strengths and signal‐to‐noise ratios typical of clinical hardware …”

“…Nonetheless, it is unclear to what extent such an approximation holds for the largest spinal axons, whose diameters can exceed those of the brain, and for the gradient strengths and signal-to-noise ratios typical of clinical hardware. [37][38][39] Furthermore, this paper assesses for the first time the relevance of considering time-dependence in clinically feasible DW imaging of the spinal cord. Inspired by recent brain studies, 22,40 we test whether time-dependence of macroscopic 41 and microscopic 42 diffusion tensor imaging (DTI) parameters as well as diffusion kurtosis imaging (DKI) and two-compartment 6,36 model parameters can be measured in the spinal cord.…”