Analysis-Associated Factors Interfering With Diffusion Tensor Indices of Peripheral Nerves

Authors

  • Luka Pušnik MD
  • Lucija Laubry
  • Igor Serša
  • Armin Alibegović
  • Erika Cvetko
  • Žiga Snoj
  • Nejc Umek

DOI:

https://doi.org/10.5566/ias.3332

Keywords:

diffusion tensor imaging, fractional anisotropy, image analysis, perineural tissue, peripheral nerve, diffusion weighted imaging, nerve anatomy

Abstract

Diffusion tensor imaging (DTI) enables a non-invasive assessment of tissue architecture based on water diffusion. High-resolution magnetic resonance imaging (MRI) systems have enhanced the visualization of nerve compartments. This study investigates the influence of region of interest (ROI) selection on DTI indices in ex vivo peripheral nerves. Cadaveric median nerves (n=10) were harvested and immersed in fluorinated-carbon liquid, and 9-mm segments were scanned with a 9.4-T MRI system. Diffusion-weighted signals were summed, and diffusion tensors were calculated for anatomical compartments using different delineation techniques. Following the initial scan, five samples were scanned for a second time using an identical protocol. The analysis showed that the inclusion of an inert background into the ROI significantly reduces fractional anisotropy (FA). Significant differences in diffusion tensors were observed between the initial and intermediate segments, with FA and mean diffusivity (MD) differing by as much as 25.2% and 25.6%, respectively. Tracing the fascicles without the interfascicular epineurium exhibited an 8.8% lower FA compared to the delineations of the epineurium. Second MRI scans showed significant changes in dif-fusion tensors with higher eigenvalues D1-D3 and MD; along with lower FA. In conclusion, the intermediate portions of the nerve demonstrated greater consistency in DTI indices and are recommended for analysis over the initial portions. Using an inert liquid can minimize background effects, but the inherent diffusion properties of the tissue must be carefully considered. Prolonged scanning times can alter diffusion tensors, likely due to autolytic processes, underscoring the importance of consistent pre-scanning conditions and acquisition protocols. Additionally, maintaining consistency in delineations is paramount, regardless of whether the tracing method includes or excludes the interfascicular epineurium.

References

Alibegović, A., Umek, N., Pušnik, L., & Zubiavrre Martinez, I. (2024). Comparison of the Visual Scoring Method and Semi-Automatic Image Analysis for Evaluating Staining Intensity of Human Cartilage Sections. Image Analysis and Stereology, 43(2), 131–137. https://doi.org/10.5566/ias.3171

Awais, K., Snoj, Ž., Cvetko, E., & Serša, I. (2022). Diffusion Tensor Imaging of a Median Nerve by Magnetic Resonance: A Pilot Study. Life, 12(5), 748. https://doi.org/10.3390/life12050748

Bihan, D. (1995). Molecular diffusion, tissue microdynamics and microstructure. NMR in Biomedicine, 8(7), 375–386. https://doi.org/10.1002/nbm.1940080711

Breckwoldt, M. O., Stock, C., Xia, A., Heckel, A., Bendszus, M., Pham, M., Heiland, S., & Bäumer, P. (2015). Diffusion Tensor Imaging Adds Diagnostic Accuracy in Magnetic Resonance Neurography. Investigative Radiology, 50(8), 498–504. https://doi.org/10.1097/RLI.0000000000000156

Chen, H., Xu, Y., Wang, W., Deng, R., Li, Z., Xie, S., & Jiao, J. (2023). Assessment of Lumbosacral Nerve Roots in Patients with Type 2 Diabetic Peripheral Neuropathy Using Diffusion Tensor Imaging. Brain Sciences, 13(5), 828. https://doi.org/10.3390/brainsci13050828

Cosottini, M., & Roccatagliata, L. (2021). Neuroimaging at 7 T: are we ready for clinical transition? European Radiology Experimental, 5(1), 37. https://doi.org/10.1186/s41747-021-00234-0

Gallagher, T. A., Simon, N. G., & Kliot, M. (2015). Diffusion tensor imaging to visualize axons in the setting of nerve injury and recovery. Neurosurgical Focus, 39(3), E10. https://doi.org/10.3171/2015.6.FOCUS15211

Goyal, M., Samuel, A. J., & Mittal, A. (2022). Diffusion tensor imaging in patients with diabetic peripheral neuropathy: Fractional anisotropy and apparent diffusion coefficient dataset of posterior tibial nerve. Data in Brief, 43, 108421. https://doi.org/10.1016/j.dib.2022.108421

Jeon, T., Fung, M. M., Koch, K. M., Tan, E. T., & Sneag, D. B. (2018). Peripheral nerve diffusion tensor imaging: Overview, pitfalls, and future directions. Journal of Magnetic Resonance Imaging, 47(5), 1171–1189. https://doi.org/10.1002/jmri.25876

Koo, T. K., & Li, M. Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

Lehmann, H. C., Zhang, J., Mori, S., & Sheikh, K. A. (2010). Diffusion tensor imaging to assess axonal regeneration in peripheral nerves. Experimental Neurology, 223(1), 238–244. https://doi.org/10.1016/j.expneurol.2009.10.012

Longmore, D. B. (1989). The principles of magnetic resonance. British Medical Bulletin, 45(4), 848–880. https://doi.org/10.1093/oxfordjournals.bmb.a072371

Prats‐Galino, A., Čapek, M., Reina, M. A., Cvetko, E., Radochova, B., Tubbs, R. S., Damjanovska, M., & Stopar Pintarič, T. (2018). 3D reconstruction of peripheral nerves from optical projection tomography images: A method for studying fascicular interconnections and intraneural plexuses. Clinical Anatomy, 31(3), 424–431. https://doi.org/10.1002/ca.23028

Pušnik, L., Serša, I., Umek, N., Cvetko, E., & Snoj, Ž. (2023). Correlation between diffusion tensor indices and fascicular morphometric parameters of peripheral nerve. Frontiers in Physiology, 14:1070227. https://doi.org/10.3389/fphys.2023.1070227

Seehaus, A., Roebroeck, A., Bastiani, M., Fonseca, L., Bratzke, H., Lori, N., Vilanova, A., Goebel, R., & Galuske, R. (2015). Histological validation of high-resolution DTI in human post mortem tissue. Frontiers in Neuroanatomy, 9(JULY), 1–12. https://doi.org/10.3389/fnana.2015.00098

Snoj, Ž., Pušnik, L., Cvetko, E., Burica Matičič, U., Jengojan, S. A., & Omejec, G. (2024). Sciatic nerve fascicle differentiation on high‐resolution ultrasound with histological verification: An ex vivo study. Muscle & Nerve, 70(2), 265–272. https://doi.org/10.1002/mus.28181

Snoj, Ž., Serša, I., Maticic, U., Cvetko, E., & Omejec, G. (2020). Nerve fascicle depiction at MR microscopy and high-frequency us with anatomic verification. Radiology, 297(3), 672–674. https://doi.org/10.1148/radiol.2020201910

Sveinsson, B., Rowe, O. E., Stockmann, J. P., Park, D. J., Lally, P. J., Rosen, M. S., Barry, R. L., Eichler, F., Rosen, B. R., & Sadjadi, R. (2022). Feasibility of simultaneous high‐resolution anatomical and quantitative magnetic resonance imaging of sciatic nerves in patients with Charcot–Marie–Tooth type 1A (CMT1A) at 7T. Muscle & Nerve, 66(2), 206–211. https://doi.org/10.1002/mus.27647

Wang, X., Luo, L., Xing, J., Wang, J., Shi, B., Li, Y.-M., & Li, Y.-G. (2022). Assessment of peripheral neuropathy in type 2 diabetes by diffusion tensor imaging. Quantitative Imaging in Medicine and Surgery, 12(1), 395–405. https://doi.org/10.21037/qims-21-126

Weber, M., Wilhelm, T., & Schmidt, V. (2021). Multidimensional Characterisation of Time-dependent Image Data: A Case Study for the Peripheral Nervous System in Ageing Mice. Image Analysis & Stereology, 40(2), 85–94. https://doi.org/10.5566/ias.2499

Downloads

Published

2024-11-29

Issue

Section

Original Research Paper

How to Cite

Pušnik, L., Laubry, L., Serša, I., Alibegović, A., Cvetko, E., Snoj, Žiga, & Umek, N. (2024). Analysis-Associated Factors Interfering With Diffusion Tensor Indices of Peripheral Nerves. Image Analysis and Stereology, 43(3), 203-210. https://doi.org/10.5566/ias.3332