Structural and diffusion weighted image information for a macaque at different developmental ages.My interest lies in the development of image analysis methods to study neurodevelopment and neuronal structure in general. I am interested in developing analysis methods both to uncover properties on the microscopic level as well as to study macroscopic structure of white matter in the brain. I am also interested in changes occurring throughout neurodevelopment. To this end, data I work with includes MR brain scans of humans and macaques, and mouse and rat models. Of central interest here is to generate normative data, including measures of statistical variance in shape and appearance, to allow for population-based comparisons with disease models.

Diffusion weighted magnetic resonance imaging (DW-MRI) allows to measure tissue properties in vivo. In neuroimaging it is widely used to study properties of white matter and brain connectivity. My interest lies in developing and applying longitudinal atlas-building methods [1] to data from humans and macaques, as well as designing new algorithms to measure brain connectivity from DW-MRIs [2][3]. Since these datasets tend to be noisy robust methods for atlas-building [4], tractography, tensor-estimation and interpolation[5][6], and noise-reduction for DW-MRIs are desirable [7].


  1. DTI Longitudinal Atlas Construction as an Average of Growth Models,
    Hart, G., Shi Y., Zhu H., Sanchez M., Styner M., and Niethammer M.
    , MICCAI, International Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data, (2010)
  2. DTI Connectivity by Segmentation,
    Niethammer, M., Boucharin A., Zach C., Maltbie E., Shi Y., and Styner M.
    , MICCAI, International Workshop on Medical Imaging and Augmented Reality (MIAR), (2010)
  3. Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation.,
    Niethammer, M., Zach C., Melonakos J., and Tannenbaum A.
    , NeuroImage, 2009 Mar, Volume 45, Issue 1 Suppl, p.S123-32, (2009)
  4. Robust model-based transformation and averaging of diffusion weighted images – applied to diffusion weighted atlas construction,
    Niethammer, M., Shi Y., Benzaid S., Sanchez M., and Styner M.
    , MICCAI, International Workshop on Computational Diffusion MRI (CDMRI' 10), (2010)
  5. On Diffusion Tensor Estimation,
    Niethammer, M., Estepar San Jose R., Bouix S., Shenton M., and Westin C. - F.
    , Proceedings of the International Engineering in Medicine and Biology Conference (EMBC), p.2622–2625, (2006)
  6. Geodesic-loxodromes for diffusion tensor interpolation and difference measurement.,
    Kindlmann, G., Estépar RS, Niethammer M., Haker S., and Westin CF
    , Medical image computing and computer-assisted intervention : MICCAI, 2007, Volume 10, Issue Pt 1, p.1-9, (2007)
  7. Restoration of DWI data using a Rician LMMSE estimator.,
    Aja-Fernandez, S., Niethammer M., Kubicki M., Shenton ME, and Westin CF
    , IEEE transactions on medical imaging, 2008 Oct, Volume 27, Issue 10, p.1389-403, (2008)