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Gadolinium effect on thalamus and whole brain tissue segmentation

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

Gadolinium-based contrast agent (GBCA) effect on automated segmentation algorithms of subcortical gray matter (GM) is not fully known. The aim of this study is to determine gadolinium effect on the segmentation of the thalamus and whole brain tissue using different automated segmentation techniques.

Methods

Eighty-four multiple sclerosis (MS) patients underwent an MRI acquisition of two 3DT1-weighted sequences with and without gadolinium injection among which 10 were excluded after image quality check. Manual thalamic segmentation considered as gold standard was performed on unenhanced T1 images. volBrain and FSL-Anat were used to automatically segment the thalamus on both enhanced and unenhanced T1 and the degree of similitude (DICE) values were compared between manual and automatic segmentations. Whole brain tissue segmentation (GM, white matter (WM), and lateral ventricles (LV)) was also performed using SIENAX. A paired samples t test was applied to test the significance of DICE value differences between the thalamic manual and automatic segmentations of both enhanced and unenhanced T1 images.

Results

Significant differences (FSL-Anat 1.474% p < 0.001 and volBrain 1.990% p < 0.001) in DICE between thalamic manual and automatic segmentations on both enhanced and unenhanced images were observed. Automatic tissue segmentation showed a mean DICE of 81.5%, with LV having the lowest DICE value (74.2%). When compared to tissue segmentations, automatic thalamic segmentations by FSL-Anat or volBrain demonstrated a higher degree of similitude (FSL-Anat = 91.7% and volBrain = 90.7%).

Conclusion

Gadolinium has a significant effect on subcortical GM segmentation. Although significant, the observed subtle changes could be considered acceptable when used for region-based analysis in perfusion or diffusion imaging.

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Abbreviations

BBB:

Brain blood barrier

BPF:

Brain parenchymal fraction

CSF:

Cerebro-spinal fluid

DICE:

Degree of similitude

DTI:

Diffusion tensor imaging

DWI:

Diffusion weighted imaging

FLAIR:

Fluid-attenuated inversion recovery

GBCA:

Gadolinium-based contrast agent

GM:

Gray matter

LV:

Lateral ventricles

MRI:

Magnetic resonance imaging

MS:

Multiple sclerosis

WM:

White matter

References

  1. Durand-Dubief F, Belaroussi B, Armspach JPP et al (2012) Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques. Am J Neuroradiol 33:1918–1924. https://doi.org/10.3174/ajnr.A3107

    Article  CAS  PubMed  Google Scholar 

  2. Zhou Z, Lu Z-R (2013) Gadolinium-based contrast agents for magnetic resonance cancer imaging. Wiley Interdiscip Rev Nanomedicine Nanobiotechnology 5:1–18. https://doi.org/10.1002/wnan.1198

    Article  CAS  PubMed  Google Scholar 

  3. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O'Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69:292–302. https://doi.org/10.1002/ana.22366

    Article  PubMed  PubMed Central  Google Scholar 

  4. McDonald WI, Compston A, Edan G et al (2001) Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 50:121–127

    Article  CAS  Google Scholar 

  5. Calabrese M, Agosta F, Rinaldi F, Mattisi I, Grossi P, Favaretto A, Atzori M, Bernardi V, Barachino L, Rinaldi L, Perini P, Gallo P, Filippi M (2009) Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol 66:1144–1150. https://doi.org/10.1001/archneurol.2009.174

    Article  PubMed  Google Scholar 

  6. Štecková T, Hluštík P, Sládková V, Odstrčil F, Mareš J, Kaňovský P (2014) Thalamic atrophy and cognitive impairment in clinically isolated syndrome and multiple sclerosis. J Neurol Sci 342:62–68. https://doi.org/10.1016/j.jns.2014.04.026

    Article  PubMed  Google Scholar 

  7. Riccitelli G, Rocca MA, Pagani E, Rodegher ME, Rossi P, Falini A, Comi G, Filippi M (2011) Cognitive impairment in multiple sclerosis is associated to different patterns of gray matter atrophy according to clinical phenotype. Hum Brain Mapp 32:1535–1543. https://doi.org/10.1002/hbm.21125

    Article  PubMed  Google Scholar 

  8. Jovicich J, Czanner S, Han X, Salat D, van der Kouwe A, Quinn B, Pacheco J, Albert M, Killiany R, Blacker D, Maguire P, Rosas D, Makris N, Gollub R, Dale A, Dickerson BC, Fischl B (2009) MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 46:177–192. https://doi.org/10.1016/j.neuroimage.2009.02.010

    Article  PubMed  PubMed Central  Google Scholar 

  9. Warntjes JBM, Tisell A, Landtblom AM, Lundberg P (2014) Effects of gadolinium contrast agent administration on automatic brain tissue classification of patients with multiple sclerosis. Am J Neuroradiol 35:1330–1336. https://doi.org/10.3174/ajnr.A3890

    Article  CAS  PubMed  Google Scholar 

  10. Cotton F, Kremer S, Hannoun S, Vukusic S, Dousset V (2015) OFSEP, a nationwide cohort of people with multiple sclerosis: consensus minimal MRI protocol. J Neuroradiol 42:133–140. https://doi.org/10.1016/j.neurad.2014.12.001

    Article  CAS  PubMed  Google Scholar 

  11. Manjón JV, Coupé P (2016) volBrain: an online MRI brain volumetry system. Front Neuroinform 10:30. https://doi.org/10.3389/fninf.2016.00030

    Article  PubMed  PubMed Central  Google Scholar 

  12. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, Bannister PR, de Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, de Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051

    Article  PubMed  Google Scholar 

  13. Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, de Stefano N (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17:479–489. https://doi.org/10.1006/nimg.2002.1040

    Article  PubMed  Google Scholar 

  14. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302. https://doi.org/10.2307/1932409

    Article  Google Scholar 

  15. Rotge J-Y, Guehl D, Dilharreguy B, Tignol J, Bioulac B, Allard M, Burbaud P, Aouizerate B (2009) Meta-analysis of brain volume changes in obsessive-compulsive disorder. Biol Psychiatry 65:75–83. https://doi.org/10.1016/j.biopsych.2008.06.019

    Article  PubMed  Google Scholar 

  16. González-Villà S, Valverde S, Cabezas M, Pareto D, Vilanova JC, Ramió-Torrentà L, Rovira À, Oliver A, Lladó X (2017) Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation. NeuroImage Clin 15:228–238. https://doi.org/10.1016/j.nicl.2017.05.003

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zivadinov R, Cox JL (2007) Neuroimaging in multiple sclerosis. Int Rev Neurobiol 79:449–474. https://doi.org/10.1016/S0074-7742(07)79020-7

    Article  PubMed  Google Scholar 

  18. Valverde S, Oliver A, Lladó X (2014) A white matter lesion-filling approach to improve brain tissue volume measurements. NeuroImage Clin 6:86–92. https://doi.org/10.1016/j.nicl.2014.08.016

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bishop CA, Newbould RD, Lee JS et al (2017) Analysis of ageing-associated grey matter volume in patients with multiple sclerosis shows excess atrophy in subcortical regions. NeuroImage Clin 13:9–15. https://doi.org/10.1016/j.nicl.2016.11.005

    Article  PubMed  Google Scholar 

  20. Doche E, Lecocq A, Maarouf A, Duhamel G, Soulier E, Confort-Gouny S, Rico A, Guye M, Audoin B, Pelletier J, Ranjeva JP, Zaaraoui W (2017) Hypoperfusion of the thalamus is associated with disability in relapsing remitting multiple sclerosis. J Neuroradiol 44:158–164. https://doi.org/10.1016/j.neurad.2016.10.001

    Article  PubMed  Google Scholar 

  21. Hannoun S, Durand-Dubief F, Confavreux C, Ibarrola D, Streichenberger N, Cotton F, Guttmann CRG, Sappey-Marinier D (2012) Diffusion tensor-MRI evidence for extra-axonal neuronal degeneration in caudate and thalamic nuclei of patients with multiple sclerosis. AJNR Am J Neuroradiol 33:1363–1368. https://doi.org/10.3174/ajnr.A2983

    Article  CAS  PubMed  Google Scholar 

  22. Vågberg M, Lindqvist T, Ambarki K, Warntjes JBM, Sundström P, Birgander R, Svenningsson A (2013) Automated determination of brain parenchymal fraction in multiple sclerosis. AJNR Am J Neuroradiol 34:498–504. https://doi.org/10.3174/ajnr.A3262

    Article  PubMed  Google Scholar 

  23. Battaglini M, Jenkinson M, De Stefano N (2012) Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 33:2062–2071. https://doi.org/10.1002/hbm.21344

    Article  PubMed  Google Scholar 

  24. Montagne A, Toga AW, Zlokovic BV (2016) Blood-brain barrier permeability and gadolinium: benefits and potential pitfalls in research. JAMA Neurol 73:13–14. https://doi.org/10.1001/jamaneurol.2015.2960

    Article  PubMed  PubMed Central  Google Scholar 

  25. Khonsary S (2017) Guyton and hall: textbook of medical physiology. Surg Neurol Int 8:275. https://doi.org/10.4103/sni.sni_327_17

    Article  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank the Nehme and Therese Tohme Multiple Sclerosis Center at the American University of Beirut Medical Center for funding this study.

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Authors and Affiliations

  1. Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, Beirut, Lebanon

    Salem Hannoun, Marwa Baalbaki, Ribal Haddad, Nabil K. El Ayoubi, Bassem I. Yamout & Samia J. Khoury

  2. Abu-Haidar Neuroscience Institute, Faculty of Medicine, American University of Beirut Medical Center, Beirut, Lebanon

    Salem Hannoun & Samia J. Khoury

  3. Department of Diagnostic Radiology, American University of Beirut Medical Center, Riad El Solh 1107 2020, P.O.Box: 11-0236, Beirut, Lebanon

    Stephanie Saaybi & Roula Hourani

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  1. Salem Hannoun
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Correspondence to Roula Hourani.

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Funding

This study was funded by the Nehme and Therese Thome Multiple Sclerosis Center, American University of Beirut Medical Center.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

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For this type of retrospective study formal consent is not required.

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Hannoun, S., Baalbaki, M., Haddad, R. et al. Gadolinium effect on thalamus and whole brain tissue segmentation. Neuroradiology 60, 1167–1173 (2018). https://doi.org/10.1007/s00234-018-2082-5

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  • DOI: https://doi.org/10.1007/s00234-018-2082-5

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