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Improving Spatial Normalization of Brain Diffusion MRI to Measure Longitudinal Changes of Tissue Microstructure in the Cortex and White Matter.
Jacobacci, Florencia; Jovicich, Jorge; Lerner, Gonzalo; Amaro, Edson; Armony, Jorge L; Doyon, Julien; Della-Maggiore, Valeria.
Afiliação
  • Jacobacci F; Universidad de Buenos Aires. Facultad de Medicina. Departamento de fisiología y biofísica. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Fisiología y Biofísica (IFIBIO) Houssay, Buenos Aires, Argentina.
  • Jovicich J; Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy.
  • Lerner G; Universidad de Buenos Aires. Facultad de Medicina. Departamento de fisiología y biofísica. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Fisiología y Biofísica (IFIBIO) Houssay, Buenos Aires, Argentina.
  • Amaro E; PISA, LIM-44, Instituto de Radiologia, FMUSP, University of Sao Paulo, Sao Paulo, Brazil.
  • Armony JL; Douglas Mental Health University Institute and McGill University, Montreal, Quebec, Canada.
  • Doyon J; McConnell Brain Imaging Center, Montreal Neurological Institute and Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
  • Della-Maggiore V; Universidad de Buenos Aires. Facultad de Medicina. Departamento de fisiología y biofísica. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Fisiología y Biofísica (IFIBIO) Houssay, Buenos Aires, Argentina.
J Magn Reson Imaging ; 52(3): 766-775, 2020 09.
Article em En | MEDLINE | ID: mdl-32061044
ABSTRACT

BACKGROUND:

Fractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter (WM) microstructure. Recently, there has been a growing interest in identifying experience-dependent plasticity in gray matter using MD. Improving registration has thus become a major goal to enhance the detection of subtle longitudinal changes in cortical microstructure.

PURPOSE:

To optimize normalization of diffusion tensor images (DTI) to improve registration in gray matter and reduce variability associated with multisession registrations. STUDY TYPE Prospective longitudinal study.

SUBJECTS:

Twenty-one healthy subjects (18-31 years old) underwent nine MRI scanning sessions each. FIELD STRENGTH/SEQUENCE 3.0T, diffusion-weighted multiband-accelerated sequence, MP2RAGE sequence. ASSESSMENT Diffusion-weighted images were registered to standard space using different pipelines that varied in the features used for normalization, namely, the nonlinear registration algorithm (FSL vs. ANTs), the registration target (FA-based vs. T1 -based templates), and the use of intermediate individual (FA-based or T1 -based) targets. We compared the across-session test-retest reproducibility error of these normalization approaches for FA and MD in white and gray matter. STATISTICAL TESTS Reproducibility errors were compared using a repeated-measures analysis of variance with pipeline as the within-subject factor.

RESULTS:

The registration of FA data to the FMRIB58 FA atlas using ANTs yielded lower reproducibility errors in white matter (P < 0.0001) with respect to FSL. Moreover, using the MNI152 T1 template as the target of registration resulted in lower reproducibility errors for MD (P < 0.0001), whereas the FMRIB58 FA template performed better for FA (P < 0.0001). Finally, the use of an intermediate individual template improved reproducibility when registration of the FA images to the MNI152 T1 was carried out within modality (FA-FA) (P < 0.05), but not via a T1 -based individual template. DATA

CONCLUSION:

A normalization approach using ANTs to register FA images to the MNI152 T1 template via an individual FA template minimized test-retest reproducibility errors both for gray and white matter. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE 1 J. Magn. Reson. Imaging 2020;52766-775.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Substância Branca Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Substância Branca Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article