Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Differentiation of the Infarct Core from Ischemic Penumbra within the First 4.5 Hours, Using Diffusion Tensor Imaging-Derived Metrics: A Rat Model

  • Kuo, Duen-Pang (Department of Electrical Engineering, National Taiwan University) ;
  • Lu, Chia-Feng (Research Center of Translational Imaging, College of Medicine, Taipei Medical University) ;
  • Liou, Michelle (Institute of Statistical Science, Academia Sinica) ;
  • Chen, Yung-Chieh (Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University) ;
  • Chung, Hsiao-Wen (Graduate Institute of Biomedical Electrics and Bioinformatics, National Taiwan University) ;
  • Chen, Cheng-Yu (Research Center of Translational Imaging, College of Medicine, Taipei Medical University)
  • Received : 2016.07.02
  • Accepted : 2016.09.02
  • Published : 2017.04.01
⟨ Previous Next ⟩

Abstract

Objective: To investigate whether the diffusion tensor imaging-derived metrics are capable of differentiating the ischemic penumbra (IP) from the infarct core (IC), and determining stroke onset within the first 4.5 hours. Materials and Methods: All procedures were approved by the local animal care committee. Eight of the eleven rats having permanent middle cerebral artery occlusion were included for analyses. Using a 7 tesla magnetic resonance system, the relative cerebral blood flow and apparent diffusion coefficient maps were generated to define IP and IC, half hour after surgery and then every hour, up to 6.5 hours. Relative fractional anisotropy, pure anisotropy (rq) and diffusion magnitude (rL) maps were obtained. One-way analysis of variance, receiver operating characteristic curve and nonlinear regression analyses were performed. Results: The evolutions of tensor metrics were different in ischemic regions (IC and IP) and topographic subtypes (cortical, subcortical gray matter, and white matter). The rL had a significant drop of 40% at 0.5 hour, and remained stagnant up to 6.5 hours. Significant differences (p < 0.05) in rL values were found between IP, IC, and normal tissue for all topographic subtypes. Optimal rL threshold in discriminating IP from IC was about -29%. The evolution of rq showed an exponential decrease in cortical IC, from -26.9% to -47.6%; an rq reduction smaller than 44.6% can be used to predict an acute stroke onset in less than 4.5 hours. Conclusion: Diffusion tensor metrics may potentially help discriminate IP from IC and determine the acute stroke age within the therapeutic time window.

Keywords

Acknowledgement

Supported by : Ministry of Science and Technology, Taiwan

References

  1. Hacke W, Kaste M, Bluhmki E, Brozman M, Davalos A, Guidetti D, et al. Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke. N Engl J Med 2008;359:1317-1329 https://doi.org/10.1056/NEJMoa0804656
  2. Kang DW, Kwon JY, Kwon SU, Kim JS. Wake-up or unclearonset strokes: are they waking up to the world of thrombolysis therapy? Int J Stroke 2012;7:311-320 https://doi.org/10.1111/j.1747-4949.2012.00779.x
  3. Bhagat YA, Hussain MS, Stobbe RW, Butcher KS, Emery DJ, Shuaib A, et al. Elevations of diffusion anisotropy are associated with hyper-acute stroke: a serial imaging study. Magn Reson Imaging 2008;26:683-693 https://doi.org/10.1016/j.mri.2008.01.015
  4. Sakai K, Yamada K, Nagakane Y, Mori S, Nakagawa M, Nishimura T. Diffusion tensor imaging may help the determination of time at onset in cerebral ischaemia. J Neurol Neurosurg Psychiatry 2009;80:986-990 https://doi.org/10.1136/jnnp.2008.163584
  5. Puig J, Blasco G, Daunis-I-Estadella J, Thomalla G, Castellanos M, Soria G, et al. Increased corticospinal tract fractional anisotropy can discriminate stroke onset within the first 4.5 hours. Stroke 2013;44:1162-1165 https://doi.org/10.1161/STROKEAHA.111.678110
  6. Shereen A, Nemkul N, Yang D, Adhami F, Dunn RS, Hazen ML, et al. Ex vivo diffusion tensor imaging and neuropathological correlation in a murine model of hypoxia-ischemia-induced thrombotic stroke. J Cereb Blood Flow Metab 2011;31:1155-1169 https://doi.org/10.1038/jcbfm.2010.212
  7. Chiang T, Messing RO, Chou WH. Mouse model of middle cerebral artery occlusion. J Vis Exp 2011;(48):2761
  8. Bratane BT, Walvick RP, Corot C, Lancelot E, Fisher M. Characterization of gadolinium-based dynamic susceptibility contrast perfusion measurements in permanent and transient MCAO models with volumetric based validation by CASL. J Cereb Blood Flow Metab 2010;30:336-342 https://doi.org/10.1038/jcbfm.2009.218
  9. Henninger N, Bouley J, Nelligan JM, Sicard KM, Fisher M. Normobaric hyperoxia delays perfusion/diffusion mismatch evolution, reduces infarct volume, and differentially affects neuronal cell death pathways after suture middle cerebral artery occlusion in rats. J Cereb Blood Flow Metab 2007;27:1632-1642 https://doi.org/10.1038/sj.jcbfm.9600463
  10. Yao X, Yu T, Liang B, Xia T, Huang Q, Zhuang S. Effect of increasing diffusion gradient direction number on diffusion tensor imaging fiber tracking in the human brain. Korean J Radiol 2015;16:410-418 https://doi.org/10.3348/kjr.2015.16.2.410
  11. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 1996;36:715-725 https://doi.org/10.1002/mrm.1910360510
  12. Lee EK, Choi SH, Yun TJ, Kang KM, Kim TM, Lee SH, et al. Prediction of response to concurrent chemoradiotherapy with temozolomide in glioblastoma: application of immediate post-operative dynamic susceptibility contrast and diffusionweighted MR imaging. Korean J Radiol 2015;16:1341-1348 https://doi.org/10.3348/kjr.2015.16.6.1341
  13. Wang W, Steward CE, Desmond PM. Diffusion tensor imaging in glioblastoma multiforme and brain metastases: the role of p, q, L, and fractional anisotropy. AJNR Am J Neuroradiol 2009;30:203-208 https://doi.org/10.3174/ajnr.A1303
  14. Meng X, Fisher M, Shen Q, Sotak CH, Duong TQ. Characterizing the diffusion/perfusion mismatch in experimental focal cerebral ischemia. Ann Neurol 2004;55:207-212 https://doi.org/10.1002/ana.10803
  15. Shen Q, Meng X, Fisher M, Sotak CH, Duong TQ. Pixel-bypixel spatiotemporal progression of focal ischemia derived using quantitative perfusion and diffusion imaging. J Cereb Blood Flow Metab 2003;23:1479-1488 https://doi.org/10.1097/01.WCB.0000100064.36077.03
  16. Bhagat YA, Emery DJ, Shuaib A, Sher F, Rizvi NH, Akhtar N, et al. The relationship between diffusion anisotropy and time of onset after stroke. J Cereb Blood Flow Metab 2006;26:1442-1450 https://doi.org/10.1038/sj.jcbfm.9600294
  17. Carano RA, Li F, Irie K, Helmer KG, Silva MD, Fisher M, et al. Multispectral analysis of the temporal evolution of cerebral ischemia in the rat brain. J Magn Reson Imaging 2000;12:842-858 https://doi.org/10.1002/1522-2586(200012)12:6<842::AID-JMRI7>3.0.CO;2-5
  18. Papp EA, Leergaard TB, Calabrese E, Johnson GA, Bjaalie JG. Waxholm space atlas of the Sprague Dawley rat brain. Neuroimage 2014;97:374-386 https://doi.org/10.1016/j.neuroimage.2014.04.001
  19. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17:825-841 https://doi.org/10.1006/nimg.2002.1132
  20. Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. J Soc Indust Appl Math 1963;11:431-441 https://doi.org/10.1137/0111030
  21. Henninger N, Bratane BT, Bastan B, Bouley J, Fisher M. Normobaric hyperoxia and delayed tPA treatment in a rat embolic stroke model. J Cereb Blood Flow Metab 2009;29:119-129 https://doi.org/10.1038/jcbfm.2008.104
  22. Singhal AB, Benner T, Roccatagliata L, Koroshetz WJ, Schaefer PW, Lo EH, et al. A pilot study of normobaric oxygen therapy in acute ischemic stroke. Stroke 2005;36:797-802 https://doi.org/10.1161/01.STR.0000158914.66827.2e
  23. Kim HY, Singhal AB, Lo EH. Normobaric hyperoxia extends the reperfusion window in focal cerebral ischemia. Ann Neurol 2005;57:571-575 https://doi.org/10.1002/ana.20430
  24. Flynn EP, Auer RN. Eubaric hyperoxemia and experimental cerebral infarction. Ann Neurol 2002;52:566-572 https://doi.org/10.1002/ana.10322
  25. Yam PS, Dewar D, McCulloch J. Axonal injury caused by focal cerebral ischemia in the rat. J Neurotrauma 1998;15:441-450 https://doi.org/10.1089/neu.1998.15.441
  26. Dewar D, Dawson DA. Changes of cytoskeletal protein immunostaining in myelinated fibre tracts after focal cerebral ischaemia in the rat. Acta Neuropathol 1997;93:71-77 https://doi.org/10.1007/s004010050584
  27. Kuroiwa T, Nagaoka T, Ueki M, Yamada I, Miyasaka N, Akimoto H. Different apparent diffusion coefficient: water content correlations of gray and white matter during early ischemia. Stroke 1998;29:859-865 https://doi.org/10.1161/01.STR.29.4.859
  28. Pantoni L, Garcia JH, Gutierrez JA. Cerebral white matter is highly vulnerable to ischemia. Stroke 1996;27:1641-1646; discussion 1647 https://doi.org/10.1161/01.STR.27.9.1641
  29. Armitage GA, Todd KG, Shuaib A, Winship IR. Laser speckle contrast imaging of collateral blood flow during acute ischemic stroke. J Cereb Blood Flow Metab 2010;30:1432-1436 https://doi.org/10.1038/jcbfm.2010.73
  30. Zhang H, Prabhakar P, Sealock R, Faber JE. Wide genetic variation in the native pial collateral circulation is a major determinant of variation in severity of stroke. J Cereb Blood Flow Metab 2010;30:923-934 https://doi.org/10.1038/jcbfm.2010.10
  31. Cowper SE, Robin HS, Steinberg SM, Su LD, Gupta S, LeBoit PE. Scleromyxoedema-like cutaneous diseases in renaldialysis patients. Lancet 2000;356:1000-1001 https://doi.org/10.1016/S0140-6736(00)02694-5
  32. Takasawa M, Jones PS, Guadagno JV, Christensen S, Fryer TD, Harding S, et al. How reliable is perfusion MR in acute stroke? Validation and determination of the penumbra threshold against quantitative PET. Stroke 2008;39:870-877 https://doi.org/10.1161/STROKEAHA.107.500090
  33. Calamante F, Gadian DG, Connelly A. Quantification of perfusion using bolus tracking magnetic resonance imaging in stroke: assumptions, limitations, and potential implications for clinical use. Stroke 2002;33:1146-1151 https://doi.org/10.1161/01.STR.0000014208.05597.33
  34. Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG. Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 2003;50:164-174 https://doi.org/10.1002/mrm.10522
  35. Sakai K, Yamada K, Oouchi H, Nishimura T. Numerical simulation model of hyperacute/acute stage white matter infarction. Magn Reson Med Sci 2008;7:187-194 https://doi.org/10.2463/mrms.7.187
  36. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996;36:893-906 https://doi.org/10.1002/mrm.1910360612
  37. Kim SJ, Choi CG, Kim JK, Yun SC, Jahng GH, Jeong HK, et al. Effects of MR parameter changes on the quantification of diffusion anisotropy and apparent diffusion coefficient in diffusion tensor imaging: evaluation using a diffusional anisotropic phantom. Korean J Radiol 2015;16:297-303 https://doi.org/10.3348/kjr.2015.16.2.297

Cited by

  1. Diffusion Tensor-Derived Properties of Benign Oligemia, True “at Risk” Penumbra, and Infarct Core during the First Three Hours of Stroke Onset: A Rat Model vol.19, pp.6, 2017, https://doi.org/10.3348/kjr.2018.19.6.1161
  2. Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model vol.27, pp.1, 2017, https://doi.org/10.1186/s12929-020-00672-9
  3. Early neuroimaging and ultrastructural correlates of injury outcome after neonatal hypoxic-ischaemia vol.3, pp.2, 2017, https://doi.org/10.1093/braincomms/fcab048