Quantifying brain volumes for Multiple Sclerosis patients follow-up in clinical practice – comparison of 1.5 and 3 Tesla magnetic resonance imaging

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Introduction:
There is emerging evidence that brain atrophy is a part of the pathophysiology of Multiple Sclerosis (MS) and correlates with several clinical outcomes of the disease, both physical and cognitive. Consequently, brain atrophy is becoming an important parameter in patients' follow-up. Since in clinical practice both 1.5Tesla (T) and 3T magnetic resonance imaging (MRI) systems are used for MS patients follow-up, questions arise regarding compatibility and a possible need for standardization.

Methods:
Therefore, in this study 18 MS patients were scanned on the same day on a 1.5T and a 3T scanner. For each scanner, a 3D T1 and a 3D FLAIR were acquired. As no atrophy is expected within 1 day, these datasets can be used to evaluate the median percentage error of the brain volume measurement for gray matter (GM) volume and parenchymal volume (PV) between 1.5T and 3T scanners. The results are obtained with MSmetrix, which is developed especially for use in the MS clinical care path, and compared to Siena (FSL), a widely used software for research purposes.

Results:
The MSmetrix median percentage error of the brain volume measurement between a 1.5T and a 3T scanner is 0.52% for GM and 0.35% for PV. For Siena this error equals 2.99%. When data of the same scanner are compared, the error is in the order of 0.06–0.08% for both MSmetrix and Siena.

Conclusions:
MSmetrix appears robust on both the 1.5T and 3T systems and the measurement error becomes an order of magnitude higher between scanners with different field strength.

Keywords:
Brain atrophy; brain volume; MRI; MSmetrix; Multiple Sclerosis

Autoři: Andreas P. Lysandropoulos ¹; Julie Absil ²; Thierry Metens ²; Nicolas Mavroudakis ¹; Francois Guisset ¹; Eline Van Vlierberghe ³; Dirk Smeets ³; Philippe David ²; Anke Maertens & Wim Van Hecke ^{3 3}
Působiště autorů: Department of Neurology, Hôpital Erasme, Universite´ Libre de Bruxelles, Anderlecht, Belgium ¹; Department of Radiology, Hôpital Erasme, Universite´ Libre de Bruxelles, Anderlecht, Belgium ²; Icometrix, Leuven, Belgium ³
Vyšlo v časopise: Brain and Behavior, 6, 2016, č. 2, s. 11-18
prolekare.web.journal.doi_sk: https://doi.org/10.1002/brb3.422

© 2016 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Souhrn

Conclusions:
MSmetrix appears robust on both the 1.5T and 3T systems and the measurement error becomes an order of magnitude higher between scanners with different field strength.

Keywords:
Brain atrophy; brain volume; MRI; MSmetrix; Multiple Sclerosis

Zdroje

1. Amato, M. P., E. Portaccio, B. Goretti, V. Zipoli, M. Battaglini, M. L. Bartolozzi, et al. 2007. Association of neocortical volume changes with cognitive deterioration in relapsingremitting multiple sclerosis. Arch. Neurol. 64:1157–1161.

2. Battaglini, M., M. Jenkinson, and N. De Stefano. 2012. Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum. Brain Mapp. 33:2062–2071.

3. Cardoso, M. J. 2012. NiftySeg: Statistical Segmentation and Label Fusion Software Package. Available at http://niftyseg.sourceforge.net/index.html (accessed 30 January 2015).

4. Chard, D. T., J. S. Jackson, D. H. Miller, and C. A. Wheeler-Kingshott. 2010. Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J. Magn. Reson. Imaging 32:223–228.

5. Christodoulou, C., L. B. Krupp, Z. Liang, W. Huang, P. Melville, C. Roque, et al. 2003. Cognitive performance and MR markers of cerebral injury in cognitively impaired MS patients. Neurology 10:1793–1798.

6. Cover, K. S., R. A. van Schijndel, V. Popescu, B. W. van Dijk, A. Redolfi, D. L. Knol, et al., neuGRID, Alzheimer ׳s Disease Neuroimaging Initiative. 2014. The SIENA/FSL whole brain atrophy algorithm is no more reproducible at 3T than 1.5 T for Alzheimer’s disease. Psychiatry Res. 224:14–21.

7. Filippi, M., and F. Agosta. 2010. Imaging biomarkers in multiple sclerosis. J. Magn. Reson. Imaging 4:770–788.

8. Filippi, M., M. A. Rocca, F. Barkhof, W. Br€uck, J. T. Chen, G. Comi, et al., Attendees of the Correlation between Pathological MRI findings in MS workshop. 2012. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 11:349–360.

9. Giorgio, A., and N. De Stefano. 2010. Cognition in multiple sclerosis: relevance of lesions, brain atrophy and proton MR spectroscopy. Neurol. Sci. 31(Suppl 2):S245–S248.

10. Giorgio, A., M. Battaglini, S. M. Smith, and N. De Stefano. 2008. Brain atrophy assessment in multiple sclerosis: importance and limitations. Neuroimaging Clin. N. Am. 18:675–686.

11. Giovannoni, G., B. Turner, S. Gnanapavan, C. Offiah, K. Schmierer, and M. Marta. 2015. Is it time to target no evident disease activity (NEDA) in multiple sclerosis? Mult. Scler. Relat. Disord. 4:329–333.

12. Gonza´lez Ballester, M. A., A. Zisserman, and M. Brady. 2000. Segmentation and measurement of brain structures in MRI including confidence bounds. Med. Image Anal. 4:189–200.

13. Houtchens, M. K., R. H. Benedict, R. Killiany, J. Sharma, Z. Jaisani, B. Singh, et al. 2007. Thalamic atrophy and cognition in multiple sclerosis. Neurology 18:1213–1223.

14. Jain, S., D. M. Sima, A. Ribbens, M. Cambron, A. Maertens, W. Van Hecke, et al. 2015. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin. 16:367–375.

15. Jenkinson, M., and S. Smith. 2001. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5:143–156.

16. Jenkinson, M., P. Bannister, M. Brady, and S. Smith. 2002. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17:825–841.

17.

Kutzelnigg, A., and H. Lassmann. 2014. Pathology of multiple sclerosis and related inflammatory demyelinating diseases. Handb. Clin. Neurol. 122:15–58.

18. Markovic-Plese, S., and H. F. McFarland. 2001. Immunopathogenesis of the multiple sclerosis lesion. Curr. Neurol. Neurosci. Rep. 1:257–262.

19. Morgen, K., G. Sammer, S. M. Courtney, T. Wolters, H. Melchior, C. R. Blecker, et al. 2006. Evidence for a direct association between cortical atrophy and cognitive impairment in relapsing-remitting MS. NeuroImage 15:891–898.

20. Nakamura, K., R. A. Brown, D. Araujo, S. Narayanan, and D. L. Arnold. 2014. Correlation between brain volume change

21. and T2 relaxation time induced by dehydration and rehydration: implications for monitoring atrophy in clinical studies. Neuroimage Clin. 23:166–170.

22. Popescu, V., M. Battaglini, W. S. Hoogstrate, S. C. Verfaillie, I. C. Sluimer, R. A. van Schijndel, et al., MAGNIMS Study Group. 2012. Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. NeuroImage 61:1484–94.

23. Popescu, V., F. Agosta, H. E. Hulst, I. C. Sluimer, D. L. Knol, M. P. Sormani, et al., MAGNIMS Study Group. 2013. Brain atrophy and lesion load predict long term disability in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 84:1082–91.

24. Popescu, V., N. C. Ran, F. Barkhof, D. T. Chard, C. A. Wheeler-Kingshott, and H. Vrenken. 2014. Accurate GM atrophy quantification in MS using lesion-filling with co-registered 2D lesion masks. Neuroimage Clin. 18: 366–373.

25. Siffrin, V., J. Vogt, H. Radbruch, R. Nitsch, and F. Zipp. 2010. Multiple sclerosis – candidate mechanisms underlying CNS atrophy. Trends Neurosci. 33:202–210.

26. Smirniotopoulos, J. G., F. M. Murphy, E. J. Rushing, J. H. Rees, and J. W. Schroeder. 2007. Patterns of contrast enhancement in the brain and meninges. Radiographics 27:525–551.

27. Smith, S. M., N. De Stefano, M. Jenkinson, and P. M. Matthews. 2001a. Normalized accurate measurement of longitudinal brain change. J. Comput. Assist. Tomogr. 25:466–475.

28. Smith, S. M., N. De Stefano, M. Jenkinson, and P. M. Matthews. 2001b. Normalized accurate measurement of longitudinal brain change. J. Comput. Assist. Tomogr. 25:466–475.

29. Smith, S. M., Y. Zhang, M. Jenkinson, J. Chen, P. M. Matthews, A. Federico, et al. 2002. Accurate, robust, and

30. automated longitudinal and cross-sectional brain change analysis. NeuroImage 17:479–489.

31. Van Leemput, K., F. Maes, D. Vandermeulen, A. Colchester, and P. Suetens. 2001. Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20:677–688.