Tensor Domain Averaging in Diffusion Imaging of Small Animals to Generate Reliable Tractography

  • Juan Yepes Zuluaga Science Based Platforms
  • Fernando Yepes-Calderon GYM Group SA
Keywords: Neurodegenerative Diseases, Small Animal MRI, Animal Models, Diffusion Tensor Imaging, Image Enhancement


Testing on small animal models is roughly the only path to transfer science-based knowledge to human use. More avidly than other human organs, we study the brain through animal models due to the complexity of experimenting directly on human subjects, even at a cellular level where the skull makes tissue sampling harder than in any other organ.
Thanks to recent technological advances in imaging, animals do not need to be sacrificed. Magnetic resonance, in particular, favors long-term analysis and monitoring since its methods do not perturb the organ functions nor compromise the metabolism of the animals. Neurons' integrity is now indirectly visible under specialized mechanisms that use water displacement to track static boundaries. Although these water diffusion methods have proven to be successful in detecting neuronal structure at the submillimeter scale, they yield noisy results when applied to the resolutions required by small animals or when facing low myeline contents as in neonates and young children.
This manuscript presents a strategy to display neuronal trending representations that follow the corticospinal tract's pathway and neuronal integrity in small rodents. The strategy is the foundation to study human neurodegenerative diseases and neurodevelopment as well.


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Y. Yamori, R. Horie, H. Handa, M. Sato, and M. Fukase, “Pathogenetic similarity of strokes in stroke-prone spontaneously hypertensive rats and humans,” Stroke, vol. 7, pp. 46–53, 1976.

I. M. Macrae, “New models of focal cerebral ischaemia,” British Journal of Clinical Pharmacology, vol. 34, no. 4, pp. 302–308, 1992.

B. Ellenbroek and J. Youn, “Rodent models in neuroscience research: Is it a rat race?” Disease Models & Mechanisms, vol. 9, no. 10, pp. 1079–1087, 2016, doi: 10.1242/dmm.026120. [Online]. Available: https://dmm.biologists.org/content/9/10/1079

M. A. Cenci, I. Q. Whishaw, and T. Schallert, “Animal models of neurological deficits: How relevant is the rat?” Nature, vol. 3, no. 1, pp. 574–79, 2002.

B. Ellenbroek and J. Youn, “Rodent models in neuroscience research: Is it a rat race?” Disease Models & Mechanisms, vol. 9, no. 1, pp. 1079–87, 2016.

A. Björklund, U. Stenevi, S. Dunnett, and F. Gage, “Cross-species neural grafting in a rat model of parkinson’s disease,” Nature, vol. 298, no. 2, pp. 652–654, 1982.

S. Jiao, V. Gurevich, and J. A. Wolff, “Long-term correction of rat model of parkinson’s disease by gene therapy,” Letters to Nature, vol. 362, no. 1, pp. 450–453, 1993.

L. V. K. James B. Koprich and J. M. Brotchie, “Animal models of


-synucleinopathy for parkinson disease drug development,” Nature Reviews Neuroscience, vol. 18, no. 1, pp. 515–529, 2017.

R. Adalbert et al., “A rat model of slow wallerian degeneration (WldS) with improved preservation of neuromuscular synapses,” European Journal of Neuroscience, vol. 21, no. 1, pp. 271–277, 2004.

A. Llobet Rosell and L. J. Neukomm, “Axon death signalling in wallerian degeneration among species and in disease,” Open Biology, vol. 9, no. 8, p. 190118, 2019, doi: 10.1098/rsob.190118. [Online]. Available: https://royalsocietypublishing.org/doi/abs/10.1098/rsob.190118

H. Chahboune et al., “DTI abnormalities in anterior corpus callosum of rats with spike-wave epilepsy,” NeuroImage, vol. 47, no. 2, pp. 459–466, 2009.

Z. Li, Z. You, M. Li, L. P. J. Cheng, and L. Wang, “Protective effect of resveratrol on the brain in a rat model of epilepsy,” Neuroscience Bulletin volume, vol. 33, no. 1, pp. 273–280, 2017, doi: 10.1007/s12264-017-0097-2.

F. G. Mina, F. Dal-Pizzol, J. Quevedo, and A. I. Zugno, “Different sub-anesthetic doses of ketamine increase oxidative stress in the brain of rats,” Progress in Neuropsychopharmacology and Biological Psychiatry, vol. 33, no. 6, pp. 1003–1008, 2009.

A. Sánchez-González et al., “Increased thin-spine density in frontal cortex pyramidal neurons in a genetic rat model of schizophrenia-relevant features,” European Neuropsychopharmacology, vol. 44, pp. 79–91, 2021, doi: https://doi.org/10.1016/j.euroneuro.2021.01.006. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0924977X21000092

L. de Oliveira et al., “Behavioral changes and mitochondrial dysfunction in a rat model of schizophrenia induced by ketamine,” Metabolic Brain Disease, vol. 26, no. 1, pp. 69–77, 2010.

S. Pluchino et al., “Injection of adult neurospheres induces recovery in a chronic model of multiple sclerosis,” Nature, vol. 422, no. 1, pp. 3–6, 2003.

S. Khezri, S. M. A. Froushani, and M. Shahmoradi, “Nicotine augments the beneficial effects of mesenchymal stem cell-based therapy in rat model of multiple sclerosis,” Immunological Investigations, vol. 47, no. 2, pp. 113–124, 2018, doi: 10.1080/08820139.2017.1391841. [Online]. Available: https://doi.org/10.1080/08820139.2017.1391841

S. Soghomonian, J. Tjuvajev, D. Ballon, and J. A. Koutcher, “In vivo multiple-mouse imaging at 1.5 t,” Magnetic Resonance in Medicine, vol. 49, pp. 551–557, 2003.

C. Fink et al., “High-resolution three-dimensional MR angiography of rodent tumors: Morphologic characterization of intratumoral vasculature,” JOURNAL OF MAGNETIC RESONANCE IMAGING, vol. 18, pp. 59–65, 2003.

M.-A. Brockmann et al., “Analysis of mouse brain using a clinical 1.5 t scanner and a standard small loop surface coil,” Brain research, vol. 3, pp. 8–14, 2005.

J. Hiltunen, R. Hari, V. Jousmaki, K. Muller, R. Sepponen, and R. Joensuu, “Quantification of mechanical vibration during diffusion tensor imaging at 3 t,” NeuroImage, vol. 32, pp. 93–103, 2006.

S. Brockstedt et al., “Triggering in quantitative diffusion imaging with single-shot EPI,” Acta Radiologica, vol. 40, no. 3, pp. 263–69, 1999.

S. Kim, “Effects of cardiac pulsation in diffusion tensor imaging of the rat brain,” Journal of Neuroscience Methods, vol. 194, no. 1, pp. 116–121, 2010.

C. Pierpaoli, S. Marenco, G. Rohde, D. K. Jones, and A. S. Barnett, “Analyzing the contribution of cardiac pulsation to the variability of quantities derived from the diffusion tensor,” Proc. Intl. Soc. Mag. Reson. Med., vol. 11, no. 1, p. 4, 2003.

B. A. Landman, J. A. D. Farrell, H. Huang, J. L. Prince, and S. Mori, “Diffusion tensor imaging at low SNR: Nonmonotonic behaviors of tensor contrasts,” Magnetic Resonance Imaging, vol. 26, pp. 790–800, 2008.

A. L. Alexander, K. M. Hasan, M. Lazar, J. S. Tsuruda, and D. L. Parker, “Analysis of partial volume effects in diffusion-tensor MRI,” Magnetic Resonance in Medicine, vol. 45, pp. 770–780, 2001.

H. Oouchi, K. Yamada, K. Sakai, O. Kizu, and T. Kubota, “Diffusion anisotropy measurement of brain white matter is affected by voxel size: Underestimation occurs in areas with crossing fibers,” American Journal of Radioneurology, vol. 10, pp. 2–4, 2006.

D. M. Weinstein, G. L. Kindlmann, and E. C. Lundberg, “Tensorlines: Advection-diffusion based propagation through diffusion tensor fields,” in Proceedings of the 10th IEEE visualization 1999 conference (VIS ’99), 1999, p. –.

M. D. Abramoff, P. J. Magalhaes, and S. J. Ram, “Image processing with ImageJ,” Biophotonics International, vol. 11, no. 7, pp. 36–42, 2004.

P. Fillard, N. Toussaint, and X. Pennec, “Medinria : Dt-mri processing and visualization software,” 2006.

M. M. Correia, T. A. Carpentera, and G. Williamsa, “Looking for the optimal DTI acquisition scheme given a maximum scan time: Are more b-values a waste of time?” Magnetic Resonance Imaging, vol. 27, no. 2, pp. 163–175, 2009.

E. Hui, M. Cheung, K. Chan, and E. Wu, “B-value dependence of DTI quantitation and sensitivity in detecting neural tissue changes,” NeuroImage, vol. 49, no. 3, pp. 2366–2374, 2010.

A. Pierce, E. Lo, J. Mandeville, R. Gonzalez, B. Rosen, and G. Wolf, “MRI measurements of water diffusion and cerebral perfusion: Their realtionship in a rat model of focal cerebral ischemia,” Journal of Cerebral Blood Flow and Metabolism, vol. 17, no. 2, pp. 183–190, Feb. 1997.

J. Röther, A. de Crespigny, H. D’Arceuil, and M. Mosley, “MR detection of cortical spreading depression immediately after focal ischemia in the rat,” Journal of Cerebral Blood Flow and Metabolism, vol. 16, no. 2, pp. 214–220, 1996.

M. Rudin, D. Baumann, D. Ekatodramis, R. Stirnimannb, K. McAllister, and A. Sauter, “MRI analysis of the changes in apparent water diffusion coefficient, T2 relaxation time, and cerebral blood flow and volume in the temporal evolution of cerebral infarction following permanent middle cerebral artery occlusion in rats,” Experimental Neurology, vol. 169, no. 1, pp. 56–63, May 2001.

D. C. Alexander, C. Pierpaoli, P. J. Basser, and J. C. Gee, “Spatial transformations of diffusion tensor magnetic resonance images,” IEEE Transactions on Medical Imaging, vol. 20, pp. 1131–1139, 2001.

P. Fillard, X. Pennec, V. Arsigny, and N. Ayache, “Clinical DT-MRI estimation, smoothing and fiber tracking with log-euclidean metrics,” IEEE Transactions on Medical Imaging, vol. 26, no. 11, pp. 1472–1482, 2007.

A. Kunimatsu, S. Aoki, Y. Masutani, O. Abe, H. Mori, and K. Ohtomo, “Three-dimensional white matter tractography by diffusion tensor imaging in ischaemic stroke involving the corticospinal tract,” Neuroradiology, vol. 45, no. 8, pp. 532–535, 2003.

S. Choi, “DTI at 7 and 3 t: Systematic comparison of SNR and its influence on quantitative metrics,” Magnetic Resonance Imaging, vol. 29, no. 6, pp. 739–751, 2011.

Y. Watanabe and E. Han, “Image registration accuracy of GammaPlan: A phantom study,” Special Supplements, vol. 109, no. 6, pp. 21–24, 2008.

L. Tang and X. J. Zhou, “Diffusion MRI of cancer: From low to high b-values,” Journal of Magnetic Resonance Imaging, vol. 49, no. 1, pp. 23–40, 2019, doi: https://doi.org/10.1002/jmri.26293. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.26293

L. Zhan et al., “How does angular resolution affect diffusion imaging measures?” NeuroImage, vol. 49, no. 2, pp. 1357–1371, 2010.

M. Norimoto and others, “Does the increased motion probing gradient directional diffusion tensor imaging of lumbar nerves using multi-band SENSE improve the visualization and accuracy of FA values?” European Spine Journal volume, vol. 29, no. 1, pp. 1693–1701, 2020.

X. Santarelli, G. Garbin, M. Ukmar, and R. Longo, “Dependence of the fractional anisotropy in cervical spine from the number of diffusion gradients, repeated acquisition and voxel size,” Magnetic Resonance Imaging, vol. 28, no. 1, pp. 70–76, 2010.

How to Cite
J. Yepes Zuluaga and F. Yepes-Calderon, “Tensor Domain Averaging in Diffusion Imaging of Small Animals to Generate Reliable Tractography”, paradigmplus, vol. 2, no. 1, pp. 1-19, Apr. 2021.