Joint longitudinal modeling of brain appearance in multimodal MRI for the characterization of early brain developmental processes

Avantika Vardhan, Marcel Prastawa, Neda Sadeghi, Clement Vachet, Joseph Piven, Guido Gerig

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Early brain maturational processes such as myelination manifest as changes in the relative appearance of white-gray matter tissue classes in MR images. Imaging modalities such as T1W (T1-Weighted) and T2W (T2-Weighted) MRI each display specific patterns of appearance change associated with distinct neurobiological components of these maturational processes. In this paper we present a framework to jointly model multimodal appearance changes across time for a longitudinal imaging dataset, resulting in quantitative assessment of the patterns of early brain maturation not yet available to clinicians. We measure appearance by quantifying contrast between white and gray matter in terms of the distance between their intensity distributions, a method demonstrated to be relatively stable to interscan variability. A multivariate nonlinear mixed effects (NLME) model is used for joint statistical modeling of this contrast measure across multiple imaging modalities. The multivariate NLME procedure considers correlations between modalities in addition to intra-modal variability. The parameters of the logistic growth function used in NLME modeling provide useful quantitative information about the timing and progression of contrast change in multimodal datasets. Inverted patterns of relative white-gray matter intensity gradient that are observable in T1W scans with respect to T2W scans are characterized by the SIR (Signal Intensity Ratio). The CONTDIR (Contrast Direction) which measures the direction of the gradient at each time point relative to that in the adult-like scan adds a directional attribute to contrast. The major contribution of this paper is a framework for joint multimodal temporal modeling of white-gray matter MRI contrast change and estimation of subject-specific and population growth trajectories. Results confirm qualitative descriptions of growth patterns in pediatric radiology studies and our new quantitative modeling scheme has the potential to advance understanding of variability of brain tissue maturation and to eventually differentiate normal from abnormal growth for early diagnosis of pathology.

    Original languageEnglish (US)
    Title of host publicationSpatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - 3rd International Workshop, STIA 2014 Held in Conjunction with MICCAI 2014, Revised Selected Papers
    PublisherSpringer Verlag
    Pages49-63
    Number of pages15
    Volume8682
    ISBN (Print)9783319149042
    DOIs
    StatePublished - 2015
    Event3rd International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2014 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014 - Boston, United States
    Duration: Sep 18 2014Sep 18 2014

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume8682
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other3rd International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2014 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014
    CountryUnited States
    CityBoston
    Period9/18/149/18/14

    Fingerprint

    Magnetic resonance imaging
    Brain
    Modality
    Mixed Effects
    Imaging
    Nonlinear Effects
    Imaging techniques
    Modeling
    Tissue
    Nonlinear Mixed Effects Model
    Gradient
    Logistic Growth
    Joint Modeling
    Growth Function
    Time Change
    Pediatrics
    Radiology
    Population Growth
    Statistical Modeling
    Pathology

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Vardhan, A., Prastawa, M., Sadeghi, N., Vachet, C., Piven, J., & Gerig, G. (2015). Joint longitudinal modeling of brain appearance in multimodal MRI for the characterization of early brain developmental processes. In Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - 3rd International Workshop, STIA 2014 Held in Conjunction with MICCAI 2014, Revised Selected Papers (Vol. 8682, pp. 49-63). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8682). Springer Verlag. https://doi.org/10.1007/978-3-319-14905-9_5

    Joint longitudinal modeling of brain appearance in multimodal MRI for the characterization of early brain developmental processes. / Vardhan, Avantika; Prastawa, Marcel; Sadeghi, Neda; Vachet, Clement; Piven, Joseph; Gerig, Guido.

    Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - 3rd International Workshop, STIA 2014 Held in Conjunction with MICCAI 2014, Revised Selected Papers. Vol. 8682 Springer Verlag, 2015. p. 49-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8682).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Vardhan, A, Prastawa, M, Sadeghi, N, Vachet, C, Piven, J & Gerig, G 2015, Joint longitudinal modeling of brain appearance in multimodal MRI for the characterization of early brain developmental processes. in Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - 3rd International Workshop, STIA 2014 Held in Conjunction with MICCAI 2014, Revised Selected Papers. vol. 8682, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8682, Springer Verlag, pp. 49-63, 3rd International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2014 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, Boston, United States, 9/18/14. https://doi.org/10.1007/978-3-319-14905-9_5
    Vardhan A, Prastawa M, Sadeghi N, Vachet C, Piven J, Gerig G. Joint longitudinal modeling of brain appearance in multimodal MRI for the characterization of early brain developmental processes. In Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - 3rd International Workshop, STIA 2014 Held in Conjunction with MICCAI 2014, Revised Selected Papers. Vol. 8682. Springer Verlag. 2015. p. 49-63. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-14905-9_5
    Vardhan, Avantika ; Prastawa, Marcel ; Sadeghi, Neda ; Vachet, Clement ; Piven, Joseph ; Gerig, Guido. / Joint longitudinal modeling of brain appearance in multimodal MRI for the characterization of early brain developmental processes. Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - 3rd International Workshop, STIA 2014 Held in Conjunction with MICCAI 2014, Revised Selected Papers. Vol. 8682 Springer Verlag, 2015. pp. 49-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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