Florencia Jacobacci1, Jorge Jovicich2, Gonzalo Lerner1, Edson Amaro3, Jorge L Armony4, Julien Doyon5, Valeria Della-Maggiore1. 1. 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. 2. Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy. 3. PISA, LIM-44, Instituto de Radiologia, FMUSP, University of Sao Paulo, Sao Paulo, Brazil. 4. Douglas Mental Health University Institute and McGill University, Montreal, Quebec, Canada. 5. McConnell Brain Imaging Center, Montreal Neurological Institute and Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
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;52:766-775.
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;52:766-775.
Authors: Ioannis Pappas; Henrik Hector; Kari Haws; Brian Curran; Andrew S Kayser; Mark D'Esposito Journal: Hum Brain Mapp Date: 2021-06-18 Impact factor: 5.038