Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Laplacian-Regularized Mean Apparent Propagator-MRI in Evaluating Corticospinal Tract Injury in Patients with Brain Glioma

  • Rifeng Jiang (Department of Radiology, Fujian Medical University Union Hospital) ;
  • Shaofan Jiang (Department of Radiology, Fujian Medical University Union Hospital) ;
  • Shiwei Song (Department of Neurosurgery, Fujian Medical University Union Hospital) ;
  • Xiaoqiang Wei (Department of Neurosurgery, Fujian Medical University Union Hospital) ;
  • Kaiji Deng (Department of Radiology, Fujian Medical University Union Hospital) ;
  • Zhongshuai Zhang (MR Scientific Marketing, Siemens Healthcare) ;
  • Yunjing Xue (Department of Radiology, Fujian Medical University Union Hospital)
  • Received : 2020.06.03
  • Accepted : 2020.08.09
  • Published : 2021.05.01
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Abstract

Objective: To evaluate the application of laplacian-regularized mean apparent propagator (MAPL)-MRI to brain glioma-induced corticospinal tract (CST) injury. Materials and Methods: This study included 20 patients with glioma adjacent to the CST pathway who had undergone structural and diffusion MRI. The entire CSTs of the affected and healthy sides were reconstructed, and the peritumoral CSTs were manually segmented. The morphological characteristics of the CST (track number, average length, volume, displacement of the affected CST) were examined and the diffusion parameter values, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probabilities (RTAP), and return-to-plane probabilities (RTPP) along the entire and peritumoral CSTs, were calculated. The entire and peritumoral CST characteristics of the affected and healthy sides as well as those relative CST characteristics of the patients with motor weakness and normal motor function were compared. Results: The track number, volume, MD, RD, MSD, QIV, RTAP, RTOP, and RTPP of the entire and peritumoral CSTs changed significantly for the affected side, whereas the AD and FA changed significantly only in the peritumoral CST (p < 0.05). In patients with motor weakness, the relative MSD of the entire CST, QIV of the entire and peritumoral CSTs, and the AD, MD, RD of the peritumoral CST were significantly higher, whereas the RTPP of the entire and peritumoral CSTs and the RTOP of the peritumoral CST were significantly lower than those in patients with normal motor function (p < 0.05 for all). In contrast, no significant changes were found in the CST morphological characteristics, FA, or RTAP (p > 0.05 for all). Conclusion: MAPL-MRI is an effective approach for evaluating microstructural changes after CST injury. Its sensitivity may improve when using the peritumoral CST features.

Keywords

Acknowledgement

This work was supported by grants from the Natural Science Foundation of Fujian Province (No. 2018J05135), Joint Funds for the innovation of science and Technology, Fujian province (Grant number: 2017Y9024), Training project of young talents in health system of Fujian Province (2018-1-37) and Startup Fund for scientific research, Fujian Medical University (No. 2017XQ1040 and 2019QH1034).

References

  1. Lapointe S, Perry A, Butowski NA. Primary brain tumours in adults. Lancet 2018;392:432-446 https://doi.org/10.1016/S0140-6736(18)30990-5
  2. Goodenberger ML, Jenkins RB. Genetics of adult glioma. Cancer Genet 2012;205:613-621 https://doi.org/10.1016/j.cancergen.2012.10.009
  3. Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher JL, Langer CE, et al. The epidemiology of glioma in adults: a "state of the science" review. Neuro Oncol 2014;16:896-913 https://doi.org/10.1093/neuonc/nou087
  4. Min ZG, Niu C, Zhang QL, Zhang M, Qian YC. Optimal factors of diffusion tensor imaging predicting corticospinal tract injury in patients with brain tumors. Korean J Radiol 2017;18:844-851 https://doi.org/10.3348/kjr.2017.18.5.844
  5. Avram AV, Sarlls JE, Barnett AS, Ozarslan E, Thomas C, Irfanoglu MO, et al. Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure. Neuroimage 2016;127:422-434 https://doi.org/10.1016/j.neuroimage.2015.11.027
  6. Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994;103:247-254 https://doi.org/10.1006/jmrb.1994.1037
  7. Zakaria H, Haider S, Lee I. Automated whole brain tractography affects preoperative surgical decision making. Cureus 2017;9:e1656
  8. Kovanlikaya I, Firat Z, Kovanlikaya A, Ulug AM, Cihangiroglu MM, John M, et al. Assessment of the corticospinal tract alterations before and after resection of brainstem lesions using diffusion tensor imaging (DTI) and tractography at 3T. Eur J Radiol 2011;77:383-391 https://doi.org/10.1016/j.ejrad.2009.08.012
  9. Min ZG, Rana N, Niu C, Ji HM, Zhang M. Does diffusion tensor tractography of the corticospinal tract correctly reflect motor function? Med Princ Pract 2014;23:174-176 https://doi.org/10.1159/000353463
  10. Kinoshita M, Yamada K, Hashimoto N, Kato A, Izumoto S, Baba T, et al. Fiber-tracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation. Neuroimage 2005;25:424-429 https://doi.org/10.1016/j.neuroimage.2004.07.076
  11. Ozarslan E, Koay CG, Shepherd TM, Komlosh ME, Irfanoglu, MO, Pierpaoli C, et al. Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. Neuroimage 2013;78:16-32 https://doi.org/10.1016/j.neuroimage.2013.04.016
  12. Fick RHJ, Wassermann D, Caruyer E, Deriche R. MAPL: tissue microstructure estimation using Laplacian-regularized MAP-MRI and its application to HCP data. Neuroimage 2016;134:365-385 https://doi.org/10.1016/j.neuroimage.2016.03.046
  13. Cuthbert SC, Goodheart GJ Jr. On the reliability and validity of manual muscle testing: a literature review. Chiropr Osteopat 2007;15:4
  14. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016;131:803-820 https://doi.org/10.1007/s00401-016-1545-1
  15. Miao HC, Wu MT, Kao EF, Chiu YH, Chou MC. Comparisons of reproducibility and mean values of diffusion tensor imaging-derived indices between unipolar and bipolar diffusion pulse sequences. J Neuroimaging 2015;25:892-899 https://doi.org/10.1111/jon.12231
  16. Xie S, Chen L, Zuo N, Jiang T. DiffusionKit: a light one-stop solution for diffusion MRI data analysis. J Neurosci Methods 2016;273:107-119 https://doi.org/10.1016/j.jneumeth.2016.08.011
  17. Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, et al. Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform 2014;8:8
  18. Fick RHJ, Wassermann D, Sanguinetti G, Deriche R. Laplacian-regularized MAP-MRI: Improving axonal caliber estimation. Proceedings of 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI); April 16-19, 2015; 2015: New York, NY, USA.
  19. Melhem ER, Mori S, Mukundan G, Kraut MA, Pomper MG, van Zijl PC. Diffusion tensor MR imaging of the brain and white matter tractography. AJR Am J Roentgenol 2002;178:3-16 https://doi.org/10.2214/ajr.178.1.1780003
  20. Yeh FC, Liu L, Hitchens TK, Wu YL. Mapping immune cell infiltration using restricted diffusion MRI. Magn Reson Med 2017;77:603-612 https://doi.org/10.1002/mrm.26143
  21. Olson DV, Arpinar VE, Muftuler LT. Optimization of q-space sampling for mean apparent propagator MRI metrics using a genetic algorithm. Neuroimage 2019;199:237-244 https://doi.org/10.1016/j.neuroimage.2019.05.078
  22. Sakai K, Yamada K, Akazawa K, Tazoe J, Yasuike M, Nagano H, et al. Can we shorten the q-space imaging to make it clinically feasible? Jpn J Radiol 2017;35:16-24 https://doi.org/10.1007/s11604-016-0593-8
  23. Reich DS, Smith SA, Jones CK, Zackowski KM, van Zijl PC, Calabresi PA, et al. Quantitative Characterization of the Corticospinal Tract at 3T. AJNR Am J Neuroradiol 2006;27:2168-2178
  24. Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn Reson Med 2008;59:1347-1354 https://doi.org/10.1002/mrm.21577
  25. Ma K, Zhang X, Zhang H, Yan X, Gao A, Song C, et al. Mean apparent propagator-MRI: a new diffusion model which improves temporal lobe epilepsy lateralization. Eur J Radiol 2020;126:108914
  26. Wang M, Ma H, Wang X, Guo Y, Xia X, Xia H, et al. Integration of BOLD-fMRI and DTI into radiation treatment planning for high-grade gliomas located near the primary motor cortexes and corticospinal tracts. Radiat Oncol 2015;10:64
  27. Panara V, Navarra R, Mattei PA, Piccirilli E, Bartoletti V, Uncini A, et al. Correlations between cervical spinal cord magnetic resonance diffusion tensor and diffusion kurtosis imaging metrics and motor performance in patients with chronic ischemic brain lesions of the corticospinal tract. Neuroradiology 2019;61:175-182 https://doi.org/10.1007/s00234-018-2139-5
  28. Cavarsan CF, Malheiros J, Hamani C, Najm I, Covolan L. Is mossy fiber sprouting a potential therapeutic target for epilepsy? Front Neurol 2018;9:1023
  29. Yeh FC, Wedeen VJ, Tseng WY. Generalized q-sampling imaging. IEEE Trans Med Imaging 2010;29:1626-1635 https://doi.org/10.1109/TMI.2010.2045126
  30. Jin Z, Bao Y, Wang Y, Li Z, Zheng X, Long S, et al. Differences between generalized Q-sampling imaging and diffusion tensor imaging in visualization of crossing neural fibers in the brain. Surg Radiol Anat 2019;41:1019-1028 https://doi.org/10.1007/s00276-019-02264-1