Skip to main content
SpringerLink
Log in

Estimation of the Ischemic Lesion in the Experimental Stroke Studies Using Magnetic Resonance Imaging (Review)

  • CELL TECHNOLOGIES IN BIOLOGY AND MEDICINE
  • Published:
Bulletin of Experimental Biology and Medicine Aims and scope

In translational animal study aimed at evaluation of the effectiveness of innovative methods for treating cerebral stroke, including regenerative cell technologies, of particular importance is evaluation of the dynamics of changes in the volume of the cerebral infarction in response to therapy. Among the methods for assessing the focus of infarction, MRI is the most effective and convenient tool for use in preclinical studies. This review provides a description of MR pulse sequences used to visualize cerebral ischemia at various stages of its development, and a detailed description of the MR semiotics of cerebral infarction. A comparison of various methods for morphometric analysis of the focus of a cerebral infarction, including systems based on artificial intelligence for a more objective measurement of the volume of the lesion, is also presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kim J, Thayabaranathan T, Donnan GA, Howard G, Howard VJ, Rothwell PM, Feigin V, Norrving B, Owolabi M, Pandian J, Liu L, Cadilhac DA, Thrift AG. Global Stroke Statistics 2019. Int. J. Stroke. 2020;15(8):819-838. doi: https://doi.org/10.1177/1747493020909545

    Article  PubMed  Google Scholar 

  2. Pu L, Wang L, Zhang R, Zhao T, Jiang Y, Han L. Projected global trends in ischemic stroke incidence, deaths and disability-adjusted life years from 2020 to 2030. Stroke. 2023;54(5):1330-1339. doi: https://doi.org/10.1161/STROKEAHA.122.040073

    Article  PubMed  Google Scholar 

  3. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, Biller J, Brown M, Demaerschalk BM, Hoh B, Jauch EC, Kidwell CS, Leslie-Mazwi TM, Ovbiagele B, Scott PA, Sheth KN, Southerland AM, Summers DV, Tirschwell DL. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2019;50(12):e344-e418. doi: https://doi.org/10.1161/STR.0000000000000211

    Article  PubMed  Google Scholar 

  4. Zhang XL, Zhang XG, Huang YR, Zheng YY, Ying PJ, Zhang XJ, Lu X, Wang YJ, Zheng GQ. Stem cell-based therapy for experimental ischemic stroke: a preclinical systematic review. Front. Cell. Neurosci. 2021;15:628908. doi: https://doi.org/10.3389/fncel.2021.628908

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Namestnikova DD, Cherkashova EA, Sukhinich KK, Gubskiy IL, Leonov GE, Gubsky LV, Majouga AG, Yarygin KN. Combined cell therapy in the treatment of neurological disorders. Biomedicines. 2020;8(12):613. doi: https://doi.org/10.3390/biomedicines8120613

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rascón-Ramírez FJ, Esteban-García N, Barcia JA, Trondin A, Nombela C, Sánchez-Sánchez-Rojas L. Are we ready for cell therapy to treat stroke? Front. Cell Dev. Biol. 2021;9:621645. doi: https://doi.org/10.3389/fcell.2021.621645

    Article  PubMed  PubMed Central  Google Scholar 

  7. Namestnikova DD, Tairova RT, Sukhinich KK, Cherkashova EA, Gubskiy IL, Gubskiy LV, Yarygin KN. Cell therapy for ischemic stroke. Stem cell types and results of pre-clinical trials. Zh. Nevrol. Psikhiatr. Im. S.S.Korsakova. 2018;118(9-2):69-75. Russian. doi: https://doi.org/10.17116/jnevro201811809269

  8. Cherkashova EA, Namestnikova DD, Gubskiy IL, RevkovaVA, Sukhinich KK, Mel’nikov PA, Chekhonin VP, Gubsky LV, Yarygin KN. Dose-dependent effects of intravenous mesenchymal stem cell transplantation in rats with acute focal cerebral ischemia. Bull. Exp. Biol. Med. 2022;173(4):514-518. doi: https://doi.org/10.1007/s10517-022-05573-5

  9. Cherkashova EA, Namestnikova DD, Gubskiy IL, Revkova VA, Sukhinich KK, Melnikov PA, Abakumov MA, Savina GD, Chekhonin VP, Gubsky LV, Yarygin KN. Dynamic MRI of the mesenchymal stem cells distribution during intravenous transplantation in a rat model of ischemic stroke. Life (Basel). 2023;13(2):288. doi: https://doi.org/10.3390/life13020288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Namestnikova DD, Gubskiy IL, Revkova VA, Sukhinich KK, Melnikov PA, Gabashvili AN, Cherkashova EA, Vishnevskiy DA, Kurilo VV, Burunova VV, Semkina AS, Abakumov MA, Gubsky LV, Chekhonin VP, Ahlfors JE, Baklaushev VP, Yarygin KN. Intra-arterial stem cell transplantation in experimental stroke in rats: real-time MR visualization of transplanted cells starting with their first pass through the brain with regard to the therapeutic action. Front. Neurosci. 2021;15:641970. doi: https://doi.org/10.3389/fnins.2021.641970

    Article  PubMed  PubMed Central  Google Scholar 

  11. Namestnikova DD, Gubskiy IL, Cherkashova EA, Sukhinich KK, Melnikov PA, Gabashvili AN, Kurilo VV, Chekhonin VP, Gubsky LV, Yarygin KN. Therapeutic efficacy and migration of mesenchymal stem cells after intracerebral transplantation in rats with experimental ischemic stroke. Bull. Exp. Biol. Med. 2023;175(1):116-125. doi: https://doi.org/10.1007/s10517-023-05822-1

    Article  CAS  PubMed  Google Scholar 

  12. Fauzi AA, Thamrin AMH, Permana AT, Ranuh IGMAR, Hidayati HB, Hamdan M, Wahyuhadi J, Suroto NS, Lestari P, Chandra PS. Comparison of the administration route of stem cell therapy for ischemic stroke: a systematic review and meta-analysis of the clinical outcomes and safety. J. Clin. Med. 2023;12(7):2735. doi: https://doi.org/10.3390/jcm12072735

  13. He JQ, Sussman ES, Steinberg GK. Revisiting stem cell-based clinical trials for ischemic stroke. Front. Aging Neurosci. 2020;12:575990. doi: https://doi.org/10.3389/fnagi.2020.575990

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kawabori M, Shichinohe H, Kuroda S, Houkin K. Clinical trials of stem cell therapy for cerebral ischemic stroke. Int. J. Mol. Sci. 2020;21(19):7380. doi: https://doi.org/10.3390/ijms21197380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Namestnikova DD, Tairova RT, Cherkashova EA, Sukhinich KK, Gubskiy IL, Gubskiy LV, Yarygin KN. Cell therapy for ischemic stroke. Results of clinical trials and perspectives of clinical application in the Russian Federation. Zh. Nevrol. Psikhiatr. Im. S.S.Korsakova. 2018;118(12-2):94-104. Russian. doi: https://doi.org/10.17116/jnevro201811812294

  16. Hess DC, Wechsler LR, Clark WM, Savitz SI, Ford GA, Chiu D, Yavagal DR, Uchino K, Liebeskind DS, Auchus AP, Sen S, Sila CA, Vest JD, Mays RW. Safety and efficacy of multipotent adult progenitor cells in acute ischaemic stroke (MASTERS): a randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Neurol. 2017;16(5):360-368. doi: https://doi.org/10.1016/S1474-4422(17)30046-7

    Article  PubMed  Google Scholar 

  17. Savitz SI, Yavagal D, Rappard G, Likosky W, Rutledge N, Graffagnino C, Alderazi Y, Elder JA, Chen PR, Budzik RF Jr, Tarrel R, Huang DY, Hinson JM Jr. A Phase 2 randomized, sham-controlled trial of internal carotid artery infusion of autologous bone marrow-derived ALD-401 cells in patients with recent stable ischemic stroke (RECOVER-Stroke). Circulation. 2019;139(2):192-205. doi: https://doi.org/10.1161/CIRCULATIONAHA.117.030659

    Article  PubMed  Google Scholar 

  18. Yamaguchi S, Yoshida M, Horie N, Satoh K, Fukuda Y, Ishizaka S, Ogawa K, Morofuji Y, Hiu T, Izumo T, Kawakami S, Nishida N, Matsuo T. Stem cell therapy for acute/subacute ischemic stroke with a focus on intraarterial stem cell transplantation: from basic research to clinical trials. Bioengineering (Basel). 2022;10(1):33. doi: https://doi.org/10.3390/bioengineering10010033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Adams HP Jr, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, Marsh EE 3rd. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in acute stroke treatment. Stroke. 1993;24(1):35-41. doi: https://doi.org/10.1161/01.str.24.1.35

  20. Shamalov NA, Stakhovskaya LV, Klochihina OA, Polunina OS, Polunina EA. An analysis of the dynamics of the main types of stroke and pathogenetic variants of ischemic stroke. Zh. Nevrol. Psikhiatr. Im. S.S.Korsakova. 2019;119(3-2):5-10. Russian. doi: https://doi.org/10.17116/jnevro20191190325

  21. Bersano A, Kraemer M, Burlina A, Mancuso M, Finsterer J, Sacco S, Salvarani C, Caputi L, Chabriat H, Oberstein SL, Federico A, Lasserve ET, Hunt D, Dichgans M, Arnold M, Debette S, Markus HS. Heritable and non-heritable uncommon causes of stroke. J. Neurol. 2021;268(8):2780-2807. doi: https://doi.org/10.1007/s00415-020-09836-x

    Article  CAS  PubMed  Google Scholar 

  22. Liu F, McCullough LD. Middle cerebral artery occlusion model in rodents: methods and potential pitfalls. J. Biomed. Biotechnol. 2011;2011:464701. doi: https://doi.org/10.1155/2011/464701

    Article  PubMed  PubMed Central  Google Scholar 

  23. Traystman RJ. Animal models of focal and global cerebral ischemia. ILAR J. 2003;44(2):85-95. doi: https://doi.org/10.1093/ilar.44.2.85

    Article  CAS  PubMed  Google Scholar 

  24. Lee RM. Morphology of cerebral arteries. Pharmacol. Ther. 1995;66(1):149-173. doi: https://doi.org/10.1016/0163-7258(94)00071-a

    Article  CAS  PubMed  Google Scholar 

  25. Koizumi J, Yoshida Y, Nakazawa T, Ooneda G. Experimental studies of ischemic brain edema. Jpn J. Stroke. 1986;8(1):1-8. doi: https://doi.org/10.3995/jstroke.8.1

    Article  Google Scholar 

  26. Longa EZ, Weinstein PR, Carlson S, Cummins R. Reversible middle cerebral artery occlusion without craniectomy in rats. Stroke. 1989;20(1):84-91. doi: https://doi.org/10.1161/01.str.20.1.84

    Article  CAS  PubMed  Google Scholar 

  27. Fluri F, Schuhmann MK, Kleinschnitz C. Animal models of ischemic stroke and their application in clinical research. Drug Des. Devel. Ther. 2015;9:3445-3454. doi: https://doi.org/10.2147/DDDT.S56071

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shimamura N, Matchett G, Tsubokawa T, Ohkuma H, Zhang J. Comparison of silicon-coated nylon suture to plain nylon suture in the rat middle cerebral artery occlusion model. J. Neurosci. Methods. 2006;156(1-2):161-165. doi: https://doi.org/10.1016/j.jneumeth.2006.02.017

    Article  CAS  PubMed  Google Scholar 

  29. Zhao H, Mayhan WG, Sun H. A modified suture technique produces consistent cerebral infarction in rats. Brain Res. 2008;1246:158-166. doi: https://doi.org/10.1016/j.brainres.2008.08.096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gubskiy IL, Namestnikova DD, Cherkashova EA, Chekhonin VP, Baklaushev VP, Gubsky LV, Yarygin KN. MRI guiding of the middle cerebral artery occlusion in rats aimed to improve stroke modeling. Transl. Stroke Res. 2018;9(4):417-425. doi: https://doi.org/10.1007/s12975-017-0590-y

    Article  CAS  PubMed  Google Scholar 

  31. Ng YS, Stein J, Ning M, Black-Schaffer RM. Comparison of clinical characteristics and functional outcomes of ischemic stroke in different vascular territories. Stroke. 2007;38(8):2309-2314. doi: https://doi.org/10.1161/STROKEAHA.106.475483

    Article  PubMed  Google Scholar 

  32. Prado R, Wang-Fischer Y, Koetzner L. Anatomy and cerebral circulation of the rat. Manual of Stroke Models in Rats. Wang-Fischer Y, ed. CRC Press, 2008. P. 13-23. doi: https://doi.org/10.1201/9781420009521.ch4

  33. Li W, Shi L, Hu B, Hong Y, Zhang H, Li X, Zhang Y. Mesenchymal stem cell-based therapy for stroke: current understanding and challenges. Front. Cell Neurosci. 2021;15:628940. doi: https://doi.org/10.3389/fncel.2021.628940

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wu Q, Wang Y, Demaerschalk BM, Ghimire S, Wellik KE, Qu W. Bone marrow stromal cell therapy for ischemic stroke: A meta-analysis of randomized control animal trials. Int. J. Stroke. 2017;12(3):273-284. doi: https://doi.org/10.1177/1747493016676617

    Article  PubMed  Google Scholar 

  35. Hietamies TM, Ostrowski C, Pei Z, Feng L, McCabe C, Work LM, Quinn TJ. Variability of functional outcome measures used in animal models of stroke and vascular cognitive impairment — a review of contemporary studies. J. Cereb. Blood Flow Metab. 2018;38(11):1872-1884. doi: https://doi.org/10.1177/0271678X18799858

    Article  PubMed  PubMed Central  Google Scholar 

  36. Popp A, Jaenisch N, Witte OW, Frahm C. Identification of ischemic regions in a rat model of stroke. PLoS One. 2009;4(3):e4764. doi: https://doi.org/10.1371/journal.pone.0004764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Weber RZ, Bernardoni D, Rentsch NH, Buil BA, Halliday S, Augath MA, Razansky D, Tackenberg C, Rust R. Visualization and estimation of stroke infarct volumes in rodents. bioRxiv. 2023. doi: https://doi.org/10.1101/2023.07.14.547245

  38. Bay V, Iversen NK, Shiadeh SMJ, Tasker RA, Wegener G, Ardalan M. Tissue processing and optimal visualization of cerebral infarcts following sub-acute focal ischemia in rats. J. Chem. Neuroanat. 2021;118:102034. doi: https://doi.org/10.1016/j.jchemneu.2021.102034

    Article  PubMed  Google Scholar 

  39. Isayama K, Pitts LH, Nishimura MC. Evaluation of 2,3,5-triphenyltetrazolium chloride staining to delineate rat brain infarcts. Stroke. 1991;22(11):1394-1398. doi: https://doi.org/10.1161/01.str.22.11.1394

    Article  CAS  PubMed  Google Scholar 

  40. Saunders DE, Clifton AG, Brown MM. Measurement of infarct size using MRI predicts prognosis in middle cerebral artery infarction. Stroke. 1995;26(12):2272-2276. doi: https://doi.org/10.1161/01.str.26.12.2272

    Article  CAS  PubMed  Google Scholar 

  41. González RG. Clinical MRI of acute ischemic stroke. J. Magn. Reson. Imaging. 2012;36(2):259-271. doi: https://doi.org/10.1002/jmri.23595

    Article  PubMed  PubMed Central  Google Scholar 

  42. Milidonis X, Marshall I, Macleod MR, Sena ES. Magnetic resonance imaging in experimental stroke and comparison with histology: systematic review and meta-analysis. Stroke. 2015;46(3):843-851. doi: https://doi.org/10.1161/STROKEAHA.114.007560

    Article  CAS  PubMed  Google Scholar 

  43. Allen LM, Hasso AN, Handwerker J, Farid H. Sequence-specific MR imaging findings that are useful in dating ischemic stroke. Radiographics. 2012;32(5):1285-1297; discussion 1297-1299. doi: https://doi.org/10.1148/rg.325115760

  44. Yao Y, Zhang Y, Liao X, Yang R, Lei Y, Luo J. Potential therapies for cerebral edema after ischemic stroke: a mini review. Front. Aging Neurosci. 2021;12:618819. doi: https://doi.org/10.3389/fnagi.2020.618819

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Brant-Zawadzki M, Atkinson D, Detrick M, Bradley WG, Scidmore G. Fluid-attenuated inversion recovery (FLAIR) for assessment of cerebral infarction. Initial clinical experience in 50 patients. Stroke. 1996;27(7):1187-1191. doi: https://doi.org/10.1161/01.str.27.7.1187

  46. Macintosh BJ, Graham SJ. Magnetic resonance imaging to visualize stroke and characterize stroke recovery: a review. Front. Neurol. 2013;4:60. doi: https://doi.org/10.3389/fneur.2013.00060

    Article  PubMed  PubMed Central  Google Scholar 

  47. Hsu LM, Wang S, Ranadive P, Ban W, Chao TH, Song S, Cerri DH, Walton LR, Broadwater MA, Lee SH, Shen D, Shih YI. Automatic skull stripping of rat and mouse brain MRI data using U-Net. Front. Neurosci. 2020;14:568614. doi: https://doi.org/10.3389/fnins.2020.568614

    Article  PubMed  PubMed Central  Google Scholar 

  48. Hsu LM, Wang S, Walton L, Wang TW, Lee SH, Shih YI. 3D U-Net improves automatic brain extraction for isotropic rat brain magnetic resonance imaging data. Front. Neurosci. 2021;15:801008. doi: https://doi.org/10.3389/fnins.2021.801008

    Article  PubMed  PubMed Central  Google Scholar 

  49. Handbook of Medical Image Computing and Computer Assisted Intervention. Zhou SK, Rueckert D, Fichtinger G, eds. Academic Press, 2019.

  50. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging. 2012;30(9):1323-1341. doi: https://doi.org/10.1016/j.mri.2012.05.001

    Article  PubMed  PubMed Central  Google Scholar 

  51. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116-1128. doi: https://doi.org/10.1016/j.neuroimage.2006.01.015

    Article  PubMed  Google Scholar 

  52. Gibbs P, Buckley DL, Blackband SJ, Horsman A. Tumour volume determination from MR images by morphological segmentation. Phys. Med. Biol. 1996;41(11):2437-2446. doi: https://doi.org/10.1088/0031-9155/41/11/014

    Article  CAS  PubMed  Google Scholar 

  53. Letteboer MM, Olsen OF, Dam EB, Willems PW, Viergever MA, Niessen WJ. Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm. Acad. Radiol. 2004;11(10):1125-1138. doi: https://doi.org/10.1016/j.acra.2004.05.020

    Article  PubMed  Google Scholar 

  54. Angelini ED, Clatz O, Mandonnet E, Konukoglu E, Capelle L, Duffau H. Glioma dynamics and computational models: a review of segmentation, registration, and in silico growth algorithms and their clinical applications. Curr. Med. Imaging Rev. 2007;3(4):262-276. doi: https://doi.org/10.2174/157340507782446241

    Article  Google Scholar 

  55. Vaidyanathan M, Clarke LP, Velthuizen RP, Phuphanich S, Bensaid AM, Hall LO, Bezdek JC, Greenberg H, Trotti A, Silbiger M. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Magn. Reson. Imaging. 1995;13(5):719-728. doi: https://doi.org/10.1016/0730-725x(95)00012-6

    Article  CAS  PubMed  Google Scholar 

  56. Venkatesh, Leo J. MRI brain image segmentation and detection using K-NN classification. J. Phys. Conf. Ser. 2019;1362(1):012073. doi: https://doi.org/10.1088/1742-6596/1362/1/012073

  57. Sudharani K, Sarma TC, Prasad K. Brain stroke detection using K-Nearest Neighbor and Minimum Mean Distance technique. 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, India, 2015. P. 770-776. doi: https://doi.org/10.1109/ICCICCT.2015.7475383.

  58. Tsai YF, Chiang IJ, Lee YC, Liao CC, Wang KL. Automatic MRI meningioma segmentation using estimation maximization. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2005;2005:3074-3077. doi: https://doi.org/10.1109/IEMBS.2005.1617124

  59. Qiao J, Cai X, Xiao Q, Chen Z, Kulkarni P, Ferris C, Kamarthi S, Sridhar S. Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization. Data Brief. 2019;27:104628. doi: https://doi.org/10.1016/j.dib.2019.104628

    Article  PubMed  PubMed Central  Google Scholar 

  60. Kaus MR, Warfield SK, Nabavi A, Chatzidakis E, Black PM, Jolesz FA, Kikinis R. Segmentation of meningiomas and low grade gliomas in MRI. Medical Image Computing and Computer-Assisted Intervention — MICCAI’99. Taylor C, Colchester F, eds. MICCAI 1999. Lecture Notes in Computer Science, Vol. 1679. Berlin, 1999. doi: https://doi.org/10.1007/10704282_1

  61. Tomita N, Jiang S, Maeder ME, Hassanpour S. Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network. Neuroimage Clin. 2020;27:102276. doi: https://doi.org/10.1016/j.nicl.2020.102276

    Article  PubMed  PubMed Central  Google Scholar 

  62. Valverde JM, Shatillo A, De Feo R, Tohka J. Automatic cerebral hemisphere segmentation in rat MRI with ischemic lesions via attention-based convolutional neural networks. Neuroinformatics. 2023;21(1):57-70. doi: https://doi.org/10.1007/s12021-022-09607-1

    Article  PubMed  Google Scholar 

  63. McGrath H, Li P, Dorent R, Bradford R, Saeed S, Bisdas S, Ourselin S, Shapey J, Vercauteren T. Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI. Int. J. Comput. Assist. Radiol. Surg. 2020;15(9):1445-1455. doi: https://doi.org/10.1007/s11548-020-02222-y

    Article  PubMed  PubMed Central  Google Scholar 

  64. Langan MT, Smith DA, Verma G, Khegai O, Saju S, Rashid S, Ranti D, Markowitz M, Belani P, Jette N, Mathew B, Goldstein J, Kirsch CFE, Morris LS, Becker JH, Delman BN, Balchandani P. Semi-automated segmentation and quantification of perivascular spaces at 7 Tesla in COVID-19. Front. Neurol. 2022;13:846957. doi: https://doi.org/10.3389/fneur.2022.846957

    Article  PubMed  PubMed Central  Google Scholar 

  65. Feo R, Giove F. Towards an efficient segmentation of small rodents brain: A short critical review. J. Neurosci. Methods. 2019;323:82-89. doi: https://doi.org/10.1016/j.jneumeth.2019.05.003

    Article  PubMed  Google Scholar 

  66. Oguz I, Zhang H, Rumple A, Sonka M. RATS: Rapid Automatic Tissue Segmentation in rodent brain MRI. J. Neurosci. Methods. 2014;221:175-182. doi: https://doi.org/10.1016/j.jneumeth.2013.09.021

    Article  PubMed  Google Scholar 

  67. Gaser C, Schmidt S, Metzler M, Herrmann KH, Krumbein I, Reichenbach JR, Witte OW. Deformation-based brain morphometry in rats. Neuroimage. 2012;63(1):47-53. doi: https://doi.org/10.1016/j.neuroimage.2012.06.066

    Article  PubMed  Google Scholar 

  68. Choi CH, Yi KS, Lee SR, Lee Y, Jeon CY, Hwang J, Lee C, Choi SS, Lee HJ, Cha SH. A novel voxel-wise lesion segmentation technique on 3.0-T diffusion MRI of hyperacute focal cerebral ischemia at 1 h after permanent MCAO in rats. J. Cereb. Blood Flow Metab. 2018;38(8):1371-1383. doi: https://doi.org/10.1177/0271678X17714179

  69. Mulder IA, Khmelinskii A, Dzyubachyk O, de Jong S, Rieff N, Wermer MJ, Hoehn M, Lelieveldt BP, van den Maagdenberg AM. Automated ischemic lesion segmentation in MRI mouse brain data after transient middle cerebral artery occlusion. Front. Neuroinform. 2017;11:3. doi: https://doi.org/10.3389/fninf.2017.00003

    Article  PubMed  PubMed Central  Google Scholar 

  70. Valverde JM, Shatillo A, De Feo R, Gröhn O, Sierra A, Tohka J. RatLesNetv2: a fully convolutional network for rodent brain lesion segmentation. Front. Neurosci. 2020;14:610239. doi: https://doi.org/10.3389/fnins.2020.610239

    Article  PubMed  PubMed Central  Google Scholar 

  71. Gerriets T, Stolz E, Walberer M, Müller C, Kluge A, Bachmann A, Fisher M, Kaps M, Bachmann G. Noninvasive quantification of brain edema and the space-occupying effect in rat stroke models using magnetic resonance imaging. Stroke. 2004;35(2):566-571. doi: https://doi.org/10.1161/01.STR.0000113692.38574.57

    Article  CAS  PubMed  Google Scholar 

  72. Iskander A, Knight RA, Zhang ZG, Ewing JR, Shankar A, Varma NR, Bagher-Ebadian H, Ali MM, Arbab AS, Janic B. Intravenous administration of human umbilical cord blood-derived AC133+ endothelial progenitor cells in rat stroke model reduces infarct volume: magnetic resonance imaging and histological findings. Stem Cells Transl. Med. 2013;2(9):703-714. doi: https://doi.org/10.5966/sctm.2013-0066

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Helsper S, Bagdasarian FA, Yuan X, Xu K, Lee JY, Rosenberg JT, Borlongan CV, Ma T, Grant SC. Extended ischemic recovery after implantation of human mesenchymal stem cell aggregates indicated by sodium MRI at 21.1 T. Transl. Stroke Res. 2022;13(4):543-555. doi: https://doi.org/10.1007/s12975-021-00976-4

Download references

Author information

Authors and Affiliations

  1. Federal Center of Brain Research and Neurotechnologies, Federal Medical-Biological Agency of Russia, Moscow, Russia

    D. D. Namestnikova, E. A. Cherkashova, I. S. Gumin & I. L. Gubskiy

  2. Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, Moscow, Russia

    D. D. Namestnikova, E. A. Cherkashova, V. P. Chekhonin & I. L. Gubskiy

  3. V. N. Orekhovich Research Institute of Biomedical Chemistry, Moscow, Russia

    K. N. Yarygin

  4. V. P. Serbsky National Medical Research Center of Psychiatry and Narcology, Ministry of Health of the Russian Federation, Moscow, Russia

    V. P. Chekhonin

  5. Russian Medical Academy of Continuous Professional Education, Ministry of Health of the Russian Federation, Moscow, Russia

    K. N. Yarygin

Authors
  1. D. D. Namestnikova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. A. Cherkashova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. S. Gumin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. P. Chekhonin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. K. N. Yarygin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. I. L. Gubskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. L. Gubskiy.

Additional information

Translated from Kletochnye Tekhnologii v Biologii i Meditsine, No. 4, pp. 223-232, December, 2023

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Namestnikova, D.D., Cherkashova, E.A., Gumin, I.S. et al. Estimation of the Ischemic Lesion in the Experimental Stroke Studies Using Magnetic Resonance Imaging (Review). Bull Exp Biol Med 176, 649–657 (2024). https://doi.org/10.1007/s10517-024-06086-z

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10517-024-06086-z

Keywords

Search

Navigation