T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regionsan MRI evaluation study
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Citações na Scopus
9
Tipo de produção
article
Data de publicação
2019
Título da Revista
ISSN da Revista
Título do Volume
Editora
SPRINGER
Autores
BELLER, Ebba
KEESER, Daniel
WEHN, Antonia
MALCHOW, Berend
KARALI, Temmuz
PAPAZOVA, Irina
PAPAZOV, Boris
SCHOEPPE, Franziska
FIGUEIREDO, Giovanna Negrao de
Citação
NEURORADIOLOGY, v.61, n.2, p.129-136, 2019
Resumo
PurposeDevelopment of a warp-based automated brain segmentation approach of 3D fluid-attenuated inversion recovery (FLAIR) images and comparison to 3D T1-based segmentation.Methods3D FLAIR and 3D T1-weighted sequences of 30 healthy subjects (mean age 29.98.3years, 8 female) were acquired on the same 3T MR scanner. Warp-based segmentation was applied for volumetry of total gray matter (GM), white matter (WM), and 116 atlas regions. Segmentation results of both sequences were compared using Pearson correlation (r).ResultsCorrelation of GM segmentation results based on FLAIR and T1 was overall good for cortical structures (mean r across all cortical structures = 0.76). Comparatively weaker results were found in the occipital lobe (r=0.77), central region (mean r=0.58), basal ganglia (mean r=0.59), thalamus (r=0.30), and cerebellum (r=0.73). FLAIR segmentation underestimated volume of the central region compared to T1, but showed a better anatomic concordance with the occipital lobe on visual review and subcortical structures, when also compared to manual segmentation. Visual analysis of FLAIR-based WM segmentation revealed frequent misclassification of regions of high signal intensity as GM.Conclusion p id=Par4 Warp-based FLAIR segmentation yields comparable results to T1 segmentation for most cortical GM structures and may provide anatomically more congruent segmentation of subcortical GM structures. Selected cortical regions, especially the central region and total WM, seem to be underestimated on FLAIR segmentation.
Palavras-chave
Magnetic resonance imaging, Brain, Neuroanatomy, Cohort studies
Referências
- Bamberg F, 2015, RADIOLOGY, V277, P206, DOI 10.1148/radiol.2015142272
- Dale AM, 1999, NEUROIMAGE, V9, P179, DOI 10.1006/nimg.1998.0395
- Dell'Oglio E, 2015, J NEUROIMAGING, V25, P191, DOI 10.1111/jon.12194
- Despotovic I, 2015, COMPUT MATH METHOD M, DOI 10.1155/2015/450341
- Fellhauer I, 2015, PSYCHIAT RES-NEUROIM, V233, P299, DOI 10.1016/j.pscychresns.2015.07.011
- Fischl B, 1999, NEUROIMAGE, V9, P195, DOI 10.1006/nimg.1998.0396
- Fischl B, 2012, NEUROIMAGE, V62, P774, DOI 10.1016/j.neuroimage.2012.01.021
- Gatidis S, 2017, RADIOL MED, V122, P430, DOI 10.1007/s11547-016-0684-8
- Hansen TI, 2015, AM J NEURORADIOL, V36, P1450, DOI 10.3174/ajnr.A4299
- Jenkinson M, 2002, NEUROIMAGE, V17, P825, DOI 10.1006/nimg.2002.1132
- Lindig T, 2018, NEUROIMAGE, V170, P210, DOI 10.1016/j.neuroimage.2017.02.016
- Lorio S, 2016, NEUROIMAGE, V130, P157, DOI 10.1016/j.neuroimage.2016.01.062
- McCarthy CS, 2015, FRONT NEUROSCI-SWITZ, V9, DOI 10.3389/fnins.2015.00379
- Miller KL, 2016, NAT NEUROSCI, V19, P1523, DOI 10.1038/nn.4393
- Nordenskjold R, 2015, PSYCHIAT RES-NEUROIM, V231, P227, DOI [10.1016/j.pscychresns.2014.11.011, 10.1016/j.psychresns.2014.11.011]
- Park B, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0074935
- Pintzka CWS, 2015, FRONT NEUROSCI-SWITZ, V9, DOI 10.3389/fnins.2015.00238
- Qureshi AY, 2014, J NEUROSCI, V34, P11199, DOI 10.1523/JNEUROSCI.1366-14.2014
- Smith SM, 2002, HUM BRAIN MAPP, V17, P143, DOI 10.1002/hbm.10062
- Tzourio-Mazoyer N, 2002, NEUROIMAGE, V15, P273, DOI 10.1006/nimg.2001.0978
- Utter AA, 2008, NEUROSCI BIOBEHAV R, V32, P333, DOI 10.1016/j.neubiorev.2006.11.003
- Visser E, 2016, NEUROIMAGE, V139, P324, DOI 10.1016/j.neuroimage.2016.06.039
- Visser E, 2016, NEUROIMAGE, V125, P479, DOI 10.1016/j.neuroimage.2015.10.013