An Anatomically Curated Fiber Clustering White Matter Atlas for Consistent White Matter Tract Parcellation across the Lifespan
An Immersive Virtual Reality Environment for Diagnostic Imaging
Inter-site and Inter-scanner Diffusion MRI Data Harmonization
The Open Anatomy Browser: A Collaborative Web-Based Viewer for Interoperable Anatomy Atlases
Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort
Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability
Supra-Threshold Fiber Cluster Statistics for Data-Driven Whole Brain Tractography Analysis
Free Water Modeling of Peritumoral Edema using Multi-fiber Tractography
Estimation of Bounded and Unbounded Trajectories in Diffusion MRI
Principal Gradient of Macroscale Cortical Organization
Slide 10
Evolution of a Simultaneous Segmentation and Atlas Registration
Multi-modality MRI-based Atlas of the Brain
Intracranial Fluid Redistribution
Corticospinal Tract Modeling for Neurosurgical Planning by Tracking through Regions of Peritumoral Edema and Crossing Fibers
Automated White Matter Fiber Tract Identification in Patients with Brain Tumors
State-space Models of Mental Processes from fMRI
Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach
Tractography-driven Groupwise Multi-Scale Parcellation of the Cortex
Gray Matter Alterations in Early Aging
Statistical Shape Analysis: From Landmarks to Diffeomorphisms
A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation
Joint Modeling of Imaging and Genetic Variability
MR-Ultrasound Fusion for Neurosurgery
Diffusion MRI and Tumor Heterogeneity
SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research

Neuroimage Analysis Center

The Neuroimaging Analysis Center is a research and technology center with the mission of advancing the role of neuroimaging in health care. The ability to access huge cohorts of patient medical records and radiology data, the emergence of ever-more detailed imaging modalities, and the availability of unprecedented computer processing power marks the possibility for a new era in neuroimaging, disease understanding, and patient treatment. We are excited to present a national resource center with the goal of finding new ways of extracting disease characteristics from advanced imaging and computation, and to make these methods available to the larger medical community through a proven methodology of world-class research, open-source software, and extensive collaboration.

Our Sponsor

The NAC is a Biomedical Technology Resource Center supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) (P41 EB015902). It was supported by the National Center for Research Resources (NCRR) (P41 RR13218) through December 2011.

Contact the Center Directors

Carl-Fredrik Westin, PhD
Laboratory of Mathematics in Imaging
Brigham and Women's Hospital
1249 Boylston St., Room 240
Boston, MA 02215
Phone: +1 617 525-6209
E-mail: westin at bwh.harvard.edu
 

Ron Kikinis, MD
Surgical Planning Laboratory 
Brigham and Women's Hospital 
75 Francis St, L1 Room 050
Boston, MA 02115
Phone: +1 617 732-7389
E-mail: kikinis at bwh.harvard.edu
 

 

Recent Publications

  • Ou W, Golland P, Hämäläinen M. Sources of variability in MEG. Med Image Comput Comput Assist Interv. 2007;10(Pt 2):751–9.
    This paper investigates and characterizes sources of variability in MEG signals in multi-site, multi-subject studies. Understanding these sources will help to develop efficient strategies for comparing and pooling data across repetitions of an experiment, across subjects, and across sites. In this work, we investigated somatosensory MEG data collected at three different sites and applied variance component analysis and nonparametric KL divergence analysis in order to characterize the sources of variability. Our analysis showed that inter-subject differences are the biggest factor in the signal variability. We demonstrated that the timing of the deflections is very consistent in the early somatosensory response, which justifies a direct comparison of deflection peak times acquired from different visits, subjects, and systems. Compared with deflection peak times, deflection magnitudes have larger variation across sites; modeling of this variability is necessary for data pooling.
  • Niethammer M, Bouix S, Aja-Fernández S, Westin CF, Shenton ME. Outlier rejection for diffusion weighted imaging. Med Image Comput Comput Assist Interv. 2007;10(Pt 1):161–8.
    This paper introduces an outlier rejection and signal reconstruction method for high angular resolution diffusion weighted imaging. The approach is based on the thresholding of Laplacian measurements over the sphere of the apparent diffusion coefficient profiles defined for a given set of gradient directions. Exemplary results are presented.
  • Ziyan U, Sabuncu MR, Donnell LJO, Westin CF. Nonlinear registration of diffusion MR images based on fiber bundles. Med Image Comput Comput Assist Interv. 2007;10(Pt 1):351–8.
    In this paper, we explore the use of fiber bundles extracted from diffusion MR images for a nonlinear registration algorithm. We employ a white matter atlas to automatically label major fiber bundles and to establish correspondence between subjects. We propose a polyaffine framework to calculate a smooth and invertible nonlinear warp field based on these correspondences, and derive an analytical solution for the reorientation of the tensor fields under the polyaffine transformation. We demonstrate our algorithm on a group of subjects and show that it performs comparable to a higher dimensional nonrigid registration algorithm.
  • Bhalerao A, Westin CF. Hyperspherical von Mises-Fisher mixture (HvMF) modelling of high angular resolution diffusion MRI. Med Image Comput Comput Assist Interv. 2007;10(Pt 1):236–43.
    A mapping of unit vectors onto a 5D hypersphere is used to model and partition ODFs from HARDI data. This mapping has a number of useful and interesting properties and we make a link to interpretation of the second order spherical harmonic decompositions of HARDI data. The paper presents the working theory and experiments of using a von Mises-Fisher mixture model for directional samples. The MLE of the second moment of the HvMF pdf can also be related to fractional anisotropy. We perform error analysis of the estimation scheme in single and multi-fibre regions and then show how a penalised-likelihood model selection method can be employed to differentiate single and multiple fibre regions.
  • Kindlmann G, Estepar RSJ, Niethammer M, Haker S, Westin CF. Geodesic-loxodromes for diffusion tensor interpolation and difference measurement. Med Image Comput Comput Assist Interv. 2007;10(Pt 1):1–9.
    In algorithms for processing diffusion tensor images, two common ingredients are interpolating tensors, and measuring the distance between them. We propose a new class of interpolation paths for tensors, termed geodesic-loxodromes, which explicitly preserve clinically important tensor attributes, such as mean diffusivity or fractional anisotropy, while using basic differential geometry to interpolate tensor orientation. This contrasts with previous Riemannian and Log-Euclidean methods that preserve the determinant. Path integrals of tangents of geodesic-loxodromes generate novel measures of over-all difference between two tensors, and of difference in shape and in orientation.