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Associations Between Diffusion Dynamics and Functional Outcome in Acute and Early Subacute Ischemic Stroke

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Abstract

Purpose

The current study aimed to investigate the associations between diffusion dynamics of ischemic lesions and clinical functional outcome of acute and early subacute stroke.

Material and Methods

A total of 80 patients with first ever infarcts in the territory of the middle cerebral artery underwent multi-b-values diffusion-weighted imaging and diffusion kurtosis imaging. Multiple diffusion parameters were generated in postprocessing using different diffusion models. Long-term functional outcome was evaluated with modified Rankin scale (mRS) at 6 months post-stroke. Good functional outcome was defined as mRS score ≤ 2 and poor functional outcome was defined as mRS score ≥ 3. Univariate analysis was used to compare the diffusion parameters and clinical features between patients with poor and good functional outcome. Significant parameters were further analyzed for correlations with functional outcome using partial correlation.

Results

In univariate analyses, standard-b-values apparent diffusion coefficient (ADCst) ratio and fractional anisotropy (FA) ratio of acute stroke, ADCst ratio and mean kurtosis (MK) ratio of early subacute stroke were statistically different between patients with poor outcome and good outcome (P < 0.05). When the potential confounding factor of lesion volume was controlled, only FA ratio of acute stroke, ADCst ratio and MK ratio of early subacute stroke remained correlated with the functional outcome (P < 0.05).

Conclusion

Diffusion dynamics are correlated with the clinical functional outcome of ischemic stroke. This correlation is independent of the effect of lesion volume and is specific to the time period between symptom onset and imaging. More effort is needed to further investigate the predictive value of diffusion-weighted imaging.

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References

  1. Counsell C, Dennis M. Systematic review of prognostic models in patients with acute stroke. Cerebrovasc Dis. 2001;12:159–70.

    Article  CAS  Google Scholar 

  2. Veerbeek JM, Kwakkel G, van Wegen EE, Ket JC, Heymans MW. Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke. 2011;42:1482–8.

    Article  Google Scholar 

  3. Heiss WD, Kidwell CS. Imaging for prediction of functional outcome and assessment of recovery in ischemic stroke. Stroke. 2014;45:1195–201.

    Article  Google Scholar 

  4. Wardlaw JM, Keir SL, Bastin ME, Armitage PA, Rana AK. Is diffusion imaging appearance an independent predictor of outcome after ischemic stroke? Neurology. 2002;59:1381–7.

    Article  CAS  Google Scholar 

  5. van Everdingen KJ, van der Grond J, Kappelle LJ, Ramos LM, Mali WP. Diffusion-weighted magnetic resonance imaging in acute stroke. Stroke. 1998;29:1783–90.

    Article  Google Scholar 

  6. Hand PJ, Wardlaw JM, Rivers CS, Armitage PA, Bastin ME, Lindley RI, Dennis MS. MR diffusion-weighted imaging and outcome prediction after ischemic stroke. Neurology. 2006;66:1159–63.

    Article  CAS  Google Scholar 

  7. Farr TD, Wegener S. Use of magnetic resonance imaging to predict outcome after stroke: a review of experimental and clinical evidence. J Cereb Blood Flow Metab. 2010;30:703–17.

    Article  Google Scholar 

  8. Sevick RJ, Kanda F, Mintorovitch J, Arieff AI, Kucharczyk J, Tsuruda JS, Norman D, Moseley ME. Cytotoxic brain edema: assessment with diffusion-weighted MR imaging. Radiology. 1992;185:687–90.

    Article  CAS  Google Scholar 

  9. Ford JC, Hackney DB. Numerical model for calculation of apparent diffusion coefficients (ADC) in permeable cylinders—comparison with measured ADC in spinal cord white matter. Magn Reson Med. 1997;37:387–94.

    Article  CAS  Google Scholar 

  10. Budde MD, Frank JA. Neurite beading is sufficient to decrease the apparent diffusion coefficient after ischemic stroke. Proc Natl Acad Sci USA. 2010;107:14472–7.

    Article  CAS  Google Scholar 

  11. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168:497–505.

    Article  Google Scholar 

  12. Xueying L, Zhongping Z, Zhoushe Z, Li G, Yongjin T, Changzheng S, Zhifeng Z, Peihao C, Hao X, Li H. Investigation of apparent diffusion coefficient from ultra-high b‑values in parkinson’s disease. Eur Radiol. 2015;25:2593–600.

    Article  Google Scholar 

  13. Meyer JR, Gutierrez A, Mock B, Hebron D, Prager JM, Gorey MT, Homer D. High-b-value diffusion-weighted MR imaging of suspected brain infarction. AJNR Am J Neuroradiol. 2000;21:1821–9.

    CAS  PubMed  Google Scholar 

  14. Le Bihan D, Turner R. The capillary network: a link between IVIM and classical perfusion. Magn Reson Med. 1992;27:171–8.

    Article  Google Scholar 

  15. Lettau M, Laible M. 3‑T high-b-value diffusion-weighted MR imaging in hyperacute ischemic stroke. J Neuroradiol. 2013;40:149–57.

    Article  Google Scholar 

  16. Toyoda K, Kitai S, Ida M, Suga S, Aoyagi Y, Fukuda K. Usefulness of high-b-value diffusion-weighted imaging in acute cerebral infarction. Eur Radiol. 2007;17:1212–20.

    Article  Google Scholar 

  17. Suo S, Cao M, Zhu W, Li L, Li J, Shen F, Zu J, Zhou Z, Zhuang Z, Qu J, Chen Z, Xu J. Stroke assessment with intravoxel incoherent motion diffusion-weighted MRI. NMR Biomed. 2016;29:320–8.

    Article  Google Scholar 

  18. Nilsson M, van Westen D, Ståhlberg F, Sundgren PC, Lätt J. The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter. Magma. 2013;26:345–70.

    Article  CAS  Google Scholar 

  19. Sotak CH. The role of diffusion tensor imaging in the evaluation of ischemic brain injury—a review. NMR Biomed. 2002;15:561–9.

    Article  Google Scholar 

  20. Yang Q, Tress BM, Barber PA, Desmond PM, Darby DG, Gerraty RP, Li T, Davis SM. Serial study of apparent diffusion coefficient and anisotropy in patients with acute stroke. Stroke. 1999;30:2382–90.

    Article  CAS  Google Scholar 

  21. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53:1432–40.

    Article  Google Scholar 

  22. Yu M, Yang D, Wang M, Wei X, Li W. Early stage of diffusional kurtosis imaging and dynamic contrast-enhanced magnetic resonance imaging correlated with long-term neurocognitive function after experimental traumatic brain injury. Neurosci Lett. 2019;705:206–11.

    Article  CAS  Google Scholar 

  23. Jang SH, Kim K, Kim SH, Son SM, Jang WH, Kwon HG. The relation between motor function of stroke patients and diffusion tensor imaging findings for the corticospinal tract. Neurosci Lett. 2014;572:1–6.

    Article  CAS  Google Scholar 

  24. Puig J, Blasco G, Daunis-I-Estadella J, Thomalla G, Castellanos M, Figueras J, Remollo S, van Eendenburg C, Sánchez-González J, Serena J, Pedraza S. Decreased corticospinal tract fractional anisotropy predicts long-term motor outcome after stroke. Stroke. 2013;44:2016–8.

    Article  Google Scholar 

  25. Doughty C, Wang J, Feng W, Hackney D, Pani E, Schlaug G. Detection and predictive value of fractional anisotropy changes of the corticospinal tract in the acute phase of a stroke. Stroke. 2016;47:1520–6.

    Article  Google Scholar 

  26. Zhang M, Lin Q, Lu J, Rong D, Zhao Z, Ma Q, Liu H, Shu N, He Y, Li K. Pontine infarction: diffusion-tensor imaging of motor pathways—a longitudinal study. Radiology. 2015;274:841–50.

    Article  Google Scholar 

  27. Schlaug G, Siewert B, Benfield A, Edelman RR, Warach S. Time course of the apparent diffusion coefficient (ADC) abnormality in human stroke. Neurology. 1997;49:113–9.

    Article  CAS  Google Scholar 

  28. Muñoz Maniega S, Bastin ME, Armitage PA, Farrall AJ, Carpenter TK, Hand PJ, Cvoro V, Rivers CS, Wardlaw JM. Temporal evolution of water diffusion parameters is different in grey and white matter in human ischaemic stroke. J Neurol Neurosurg Psychiatr. 2004;75:1714–8.

    Article  CAS  Google Scholar 

  29. Hui ES, Du F, Huang S, Shen Q, Duong TQ. Spatiotemporal dynamics of diffusional kurtosis, mean diffusivity and perfusion changes in experimental stroke. Brain Res. 2012;1451:100–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fiebach JB, Jansen O, Schellinger PD, Heiland S, Hacke W, Sartor K. Serial analysis of the apparent diffusion coefficient time course in human stroke. Neuroradiology. 2002;44:294–8.

    Article  CAS  Google Scholar 

  31. Yao Y, Zhang S, Tang X, Zhang S, Shi J, Zhu W, Zhu W. Intravoxel incoherent motion diffusion-weighted imaging in stroke patients: initial clinical experience. Clin Radiol. 2016;71:938.e11-6.

    Google Scholar 

  32. Jensen JH, Falangola MF, Hu C, Tabesh A, Rapalino O, Lo C, Helpern JA. Preliminary observations of increased diffusional kurtosis in human brain following recent cerebral infarction. NMR Biomed. 2010;24:452–7.

    Article  Google Scholar 

  33. Hui ES, Fieremans E, Jensen JH, Tabesh A, Feng W, Bonilha L, Spampinato MV, Adams R, Helpern JA. Stroke assessment with diffusional kurtosis imaging. Stroke. 2012;43:2968–73.

    Article  Google Scholar 

  34. Federau C, Sumer S, Becce F, Maeder P, O’Brien K, Meuli R, Wintermark M. Intravoxel incoherent motion perfusion imaging in acute stroke: initial clinical experience. Neuroradiology. 2014;56:629–35.

    Article  CAS  Google Scholar 

  35. Tang L, Zhou XJ. Diffusion MRI of cancer: From low to high b‑values. J Magn Reson Imaging. 2019;49:23–40.

    Article  Google Scholar 

  36. Albers GW, Thijs VN, Wechsler L, Kemp S, Schlaug G, Skalabrin E, Bammer R, Kakuda W, Lansberg MG, Shuaib A, Coplin W, Hamilton S, Moseley M, Marks MP; DEFUSE Investigators. Magnetic resonance imaging profiles predict clinical response to early reperfusion: the diffusion and perfusion imaging evaluation for understanding stroke evolution (DEFUSE) study. Ann Neurol. 2006;60:508–17.

    Article  Google Scholar 

  37. Burdette JH, Elster AD. Diffusion-weighted imaging of cerebral infarctions: are higher B values better? J Comput Assist Tomogr. 2002;26:622–7.

    Article  Google Scholar 

  38. Cheung JS, Wang E, Lo EH, Sun PZ. Stratification of heterogeneous diffusion MRI ischemic lesion with kurtosis imaging: evaluation of mean diffusion and kurtosis MRI mismatch in an animal model of transient focal ischemia. Stroke. 2012;43:2252–4.

    Article  Google Scholar 

  39. Lee CY, Bennett KM, Debbins JP. Sensitivities of statistical distribution model and diffusion kurtosis model in varying microstructural environments: a Monte Carlo study. J Magn Reson. 2013;230:19-26.

    Google Scholar 

  40. Shelton FN, Reding MJ. Effect of lesion location on upper limb motor recovery after stroke. Stroke. 2001;32:107–12.

    Article  CAS  Google Scholar 

  41. Yoon B, Shim YS, Lee KS, Shon YM, Yang DW. Region-specific changes of cerebral white matter during normal aging: a diffusion-tensor analysis. Arch Gerontol Geriatr. 2008;47:129–38.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Wenhua Liu from Huazhong University of Science and Technology in China for her assistance with statistics and Zhongping Zhang and Yang Fan from GE healthcare China for technical support.

Funding

This work was supported by the National Program of the Ministry of Science and Technology of China (No. 2011BA108B10) and the National Natural Science Foundation of China (No. 81401389, 81570462, 81730049, 81801666).

Author information

Authors and Affiliations

  1. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, 430030, Wuhan, China

    Chengxia Liu, Shun Zhang, Yihao Yao, Changliang Su, Zhenxiong Wang & Wenzhen Zhu

  2. Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China

    Minghuan Wang

Authors
  1. Chengxia Liu
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  2. Shun Zhang
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  3. Yihao Yao
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  4. Changliang Su
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  5. Zhenxiong Wang
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  7. Wenzhen Zhu
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Corresponding author

Correspondence to Wenzhen Zhu.

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Conflict of interest

C. Liu, S. Zhang, Y. Yao, C. Su, Z. Wang, M. Wang and W. Zhu declare that they have no competing interests.

Ethical standards

This study was approved by the local institutional review board and written informed consent was obtained from all patients.

Additional information

This study has been orally presented in the 102nd Scientific Assembly and Annual Meeting of the Radiological Society of North America in Chicago on 29 November 2016.

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62_2019_812_MOESM1_ESM.docx

Tables 1 and 2: Diffusion dynamics between the stroke areas and contralateral normal areas in patients of acute stroke and early subacute stroke. Table 3: Inter-observer variability of diffusion measurements

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Liu, C., Zhang, S., Yao, Y. et al. Associations Between Diffusion Dynamics and Functional Outcome in Acute and Early Subacute Ischemic Stroke. Clin Neuroradiol 30, 517–524 (2020). https://doi.org/10.1007/s00062-019-00812-1

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  • DOI: https://doi.org/10.1007/s00062-019-00812-1

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