Automatic estimation of brain parenchymal fraction in patients with multple sclerosis: a comparison between synthetic MRI and an established automated brain segmentation software based on FSL.

Brain parenchymal fraction MRI Multiple sclerosis SIENAX SyMRI

Journal

Neuroradiology
ISSN: 1432-1920
Titre abrégé: Neuroradiology
Pays: Germany
ID NLM: 1302751

Informations de publication

Date de publication:
18 Dec 2023
Historique:
received: 16 05 2023
accepted: 04 12 2023
medline: 19 12 2023
pubmed: 19 12 2023
entrez: 19 12 2023
Statut: aheadofprint

Résumé

We aimed to validate the estimation of the brain parenchymal fraction (BPF) in patients with multiple sclerosis (MS) using synthetic magnetic resonance imaging (SyMRI) by comparison with software tools of the FMRIB Software Library (FSL). In addition to a cross-sectional method comparison, longitudinal volume changes were assessed to further elucidate the suitability of SyMRI for quantification of disease-specific changes. MRI data from 216 patients with MS and 28 control participants were included for volume estimation by SyMRI and FSL-SIENAX. Moreover, longitudinal data from 35 patients with MS were used to compare registration-based percentage brain volume changes estimated using FSL-SIENA to difference-based calculations of volume changes using SyMRI. We observed strong correlations of estimated brain volumes between the two methods. While SyMRI overestimated grey matter and BPF compared to FSL-SIENAX, indicating a systematic bias, there was excellent agreement according to intra-class correlation coefficients for grey matter and good agreement for BPF and white matter. Bland-Altman plots suggested that the inter-method differences in BPF were smaller in patients with brain atrophy compared to those without atrophy. Longitudinal analyses revealed a tendency for higher atrophy rates for SyMRI than for SIENA, but SyMRI had a robust correlation and a good agreement with SIENA. In summary, BPF based on data from SyMRI and FSL-SIENAX is not directly transferable because an overestimation and higher variability of SyMRI values were observed. However, the consistency and correlations between the two methods were satisfactory, and SyMRI was suitable to quantify disease-specific atrophy in MS.

Identifiants

pubmed: 38110539
doi: 10.1007/s00234-023-03264-0
pii: 10.1007/s00234-023-03264-0
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023. The Author(s).

Références

Wallin MT et al (2019) Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the Global Burden of Disease Study. Lancet Neurol 18:269–285. https://doi.org/10.1016/S1474-4422(18)30443-5
doi: 10.1016/S1474-4422(18)30443-5
Marciniewicz E, Bladowska J, Podgórski P, Sąsiadek M (2019) The role of MR volumetry in brain atrophy assessment in multiple sclerosis: A review of the literature. Adv Clin Exp Med 28(7):989–999. https://doi.org/10.17219/acem/94137
doi: 10.17219/acem/94137 pubmed: 30729761
Dalton CM (2004) Early development of multiple sclerosis is associated with progressive grey matter atrophy in patients presenting with clinically isolated syndromes. Brain 127(5):1101–1107. https://doi.org/10.1093/brain/awh126
doi: 10.1093/brain/awh126 pubmed: 14998914
Ceccarelli A et al (2008) A voxel-based morphometry study of grey matter loss in Patients with MS with different clinical phenotypes. Neuroimage 42(1):315–322. https://doi.org/10.1016/j.neuroimage.2008.04.173
doi: 10.1016/j.neuroimage.2008.04.173 pubmed: 18501636
Radü E, Bendfeldt K, Mueller-Lenke N, Magon S, Sprenger T (2013) Brain atrophy: an in-vivo measure of disease activity in multiple sclerosis. Swiss Med Wkly 143:w13887. https://doi.org/10.4414/smw.2013.13887
doi: 10.4414/smw.2013.13887 pubmed: 24264439
Kappos L et al (2016) Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing–remitting multiple sclerosis. Multiple Sclerosis J 22(10):1297–1305. https://doi.org/10.1177/1352458515616701
doi: 10.1177/1352458515616701
Allanach JR, Farrell JW, Mésidor M, Karimi-Abdolrezaee S (2022) Current status of neuroprotective and neuroregenerative strategies in multiple sclerosis: A systematic review. Multiple Sclerosis J 28(1):29–48. https://doi.org/10.1177/13524585211008760
doi: 10.1177/13524585211008760
NC Fox, PA (1997) Freeborough, Brain atrophy progression measured from registered serial MRI: Validation and application to alzheimer’s disease. J Magn Resonance Imaging 7(6):1069–1075 https://doi.org/10.1002/jmri.1880070620
Rocca MA et al (2017) Brain MRI atrophy quantification in MS. Neurology 88(4):403–413. https://doi.org/10.1212/WNL.0000000000003542
doi: 10.1212/WNL.0000000000003542 pubmed: 27986875 pmcid: 5272969
Manjón JV, Coupé P (2016) volBrain: An online MRI brain volumetry system. Front Neuroinform 10:30. https://doi.org/10.3389/fninf.2016.00030
doi: 10.3389/fninf.2016.00030 pubmed: 27512372 pmcid: 4961698
McAllister A, Leach J, West H, Jones B, Zhang B, Serai S (2017) Quantitative synthetic MRI in children: Normative intracranial tissue segmentation values during development. Am J Neuroradiol 38(12):2364–2372. https://doi.org/10.3174/ajnr.A5398
doi: 10.3174/ajnr.A5398 pubmed: 28982788 pmcid: 7963732
Parlak S et al (2022) Reduced myelin in patients with isolated hippocampal sclerosis as assessed by SyMRI. Neuroradiol 64(1):99–107. https://doi.org/10.1007/s00234-021-02824-6
doi: 10.1007/s00234-021-02824-6
Hagiwara A et al (2021) Age-related changes in relaxation times, proton density, myelin, and tissue volumes in adult brain analyzed by 2-dimensional quantitative synthetic magnetic resonance imaging. Invest Radiol 56(3):163–172. https://doi.org/10.1097/RLI.0000000000000720
doi: 10.1097/RLI.0000000000000720 pubmed: 32858581
Lou B et al (2021) quantitative analysis of synthetic magnetic resonance imaging in alzheimer’s disease. Front Aging Neurosci 13:638731. https://doi.org/10.3389/fnagi.2021.638731
doi: 10.3389/fnagi.2021.638731 pubmed: 33912023 pmcid: 8072384
Granberg T et al (2016) Clinical feasibility of synthetic MRI in multiple sclerosis: A diagnostic and volumetric validation study. Am J Neuroradiol 37(6):1023–1029. https://doi.org/10.3174/ajnr.A4665
doi: 10.3174/ajnr.A4665 pubmed: 26797137 pmcid: 7963550
de Stefano N et al (2015) Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J Neurol Neurosurg Psychiatry 87(1):93–99. https://doi.org/10.1136/jnnp-2014-309903
doi: 10.1136/jnnp-2014-309903 pubmed: 25904813
Pichler A et al (2016) Combined analysis of global and compartmental brain volume changes in early multiple sclerosis in clinical practice. Multiple Sclerosis J 22(3):340–346. https://doi.org/10.1177/1352458515593405
doi: 10.1177/1352458515593405
Ghione E et al (2019) Aging and brain atrophy in multiple sclerosis. J Neuroimaging 29(4):527–535. https://doi.org/10.1111/jon.12625
doi: 10.1111/jon.12625 pubmed: 31074192
Langeskov-Christensen M et al (2021) Efficacy of high-intensity aerobic exercise on brain MRI measures in multiple sclerosis. Neurology 96(2):e203–e213. https://doi.org/10.1212/WNL.0000000000011241
doi: 10.1212/WNL.0000000000011241 pubmed: 33262230
de Stefano N et al (2010) Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology 74(23):1868–1876. https://doi.org/10.1212/WNL.0b013e3181e24136
doi: 10.1212/WNL.0b013e3181e24136 pubmed: 20530323
Thompson AJ et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17(2):162–173. https://doi.org/10.1016/S1474-4422(17)30470-2
doi: 10.1016/S1474-4422(17)30470-2 pubmed: 29275977
Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 33(11):1444. https://doi.org/10.1212/WNL.33.11.1444
doi: 10.1212/WNL.33.11.1444 pubmed: 6685237
Cuschieri S (2019) The STROBE guidelines. Saudi J Anaesth 13(5):31. https://doi.org/10.4103/sja.SJA_543_18
doi: 10.4103/sja.SJA_543_18
Warntjes JBM, Dahlqvist O, Lundberg P (2007) Novel method for rapid, simultaneousT1, T*2, and proton density quantification. Magn Reson Med 57(3):528–537. https://doi.org/10.1002/mrm.21165
doi: 10.1002/mrm.21165 pubmed: 17326183
Warntjes JB, Leinhard OD, West J, Lundberg P (2008) Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magn Reson Med 60(2):320–329. https://doi.org/10.1002/mrm.21635
doi: 10.1002/mrm.21635 pubmed: 18666127
West J, Warntjes JB, Lundberg P (2012) Novel whole brain segmentation and volume estimation using quantitative MRI. Eur Radiol 22(5):998–1007. https://doi.org/10.1007/s00330-011-2336-7
doi: 10.1007/s00330-011-2336-7 pubmed: 22113264
Popescu V, Battaglini M, Hoogstrate WS, Verfaillie SC, Sluimer IC, van Schijndel RA, van Dijk BW, Cover KS, Knol DL, Jenkinson M, Barkhof F, de Stefano N, Vrenken H, MAGNIMS Study Group (2012) Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. Neuroimage. 61(4):1484–94. https://doi.org/10.1016/j.neuroimage.2012.03.074
doi: 10.1016/j.neuroimage.2012.03.074 pubmed: 22484407
Smith SM, de Stefano N, Jenkinson M, Matthews PM (2001) Normalized Accurate Measurement of Longitudinal Brain Change. J Comput Assist Tomogr 25(3):466–475. https://doi.org/10.1097/00004728-200105000-00022
doi: 10.1097/00004728-200105000-00022 pubmed: 11351200
Smith SM et al (2002) Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis. Neuroimage 17(1):479–489. https://doi.org/10.1006/nimg.2002.1040
doi: 10.1006/nimg.2002.1040 pubmed: 12482100
Smith SM et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1):S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051
doi: 10.1016/j.neuroimage.2004.07.051 pubmed: 15501092
Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155. https://doi.org/10.1002/hbm.10062
doi: 10.1002/hbm.10062 pubmed: 12391568 pmcid: 6871816
Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156. https://doi.org/10.1016/s1361-8415(01)00036-6
doi: 10.1016/s1361-8415(01)00036-6 pubmed: 11516708
Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2):825–841. https://doi.org/10.1016/s1053-8119(02)91132-8
doi: 10.1016/s1053-8119(02)91132-8 pubmed: 12377157
Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45–57. https://doi.org/10.1109/42.906424
doi: 10.1109/42.906424 pubmed: 11293691
Lee SM et al (2022) Clinical adaptation of synthetic MRI-based whole brain volume segmentation in children at 3 T: comparison with modified SPM segmentation methods. Neuroradiology 64(2):381–392. https://doi.org/10.1007/s00234-021-02779-8
doi: 10.1007/s00234-021-02779-8 pubmed: 34382095
Cicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6(4):284–290. https://doi.org/10.1037/1040-3590.6.4.284
doi: 10.1037/1040-3590.6.4.284
Andica C et al (2018) Automated brain tissue and myelin volumetry based on quantitative MR imaging with various in-plane resolutions. J Neuroradiol 45(3):164–168. https://doi.org/10.1016/j.neurad.2017.10.002
doi: 10.1016/j.neurad.2017.10.002 pubmed: 29132939
Saccenti L et al (2019) Brain tissue and myelin volumetric analysis in multiple sclerosis at 3T MRI with various in-plane resolutions using synthetic MRI. Neuroradiology 61(11):1219–1227. https://doi.org/10.1007/s00234-019-02241-w
doi: 10.1007/s00234-019-02241-w pubmed: 31209528
Durand-Dubief F 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(10):1918–1924. https://doi.org/10.3174/ajnr.A3107
doi: 10.3174/ajnr.A3107 pubmed: 22790248 pmcid: 7964600
Fujita S et al (2021) Accelerated isotropic multiparametric imaging by high spatial resolution 3D-QALAS with compressed sensing. Invest Radiol 56(5):292–300. https://doi.org/10.1097/RLI.0000000000000744
doi: 10.1097/RLI.0000000000000744 pubmed: 33273376

Auteurs

Ilyas Yazici (I)

Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Gudrunstrasse 56, 44791, Bochum, Germany.

Britta Krieger (B)

Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Gudrunstrasse 56, 44791, Bochum, Germany.

Barbara Bellenberg (B)

Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Gudrunstrasse 56, 44791, Bochum, Germany.

Theodoros Ladopoulos (T)

Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Gudrunstr. 56, 44791, Bochum, Germany.

Ralf Gold (R)

Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Gudrunstr. 56, 44791, Bochum, Germany.

Ruth Schneider (R)

Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Gudrunstr. 56, 44791, Bochum, Germany.

Carsten Lukas (C)

Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Gudrunstrasse 56, 44791, Bochum, Germany. carsten.lukas@rub.de.
Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Gudrunstr. 56, 44791, Bochum, Germany. carsten.lukas@rub.de.

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