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

NIBIB

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

Westin

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

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

  • Maddah M, Wells WM, Warfield SK, Westin CF, Grimson EL. Probabilistic clustering and quantitative analysis of white matter fiber tracts. Inf Process Med Imaging. 2007;20:372–83.
    A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires point correspondence determined along the fiber pathways. This knowledge is also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, and point correspondences along the trajectories. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. Probabilistic assignment of the trajectories to clusters is controlled by imposing a minimum threshold on the membership probabilities, to remove outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.
  • Larsen S, Kikinis R, Talos IF, Weinstein D, Wells W, Golby A. Quantitative comparison of functional MRI and direct electrocortical stimulation for functional mapping. Int J Med Robot. 2007;3(3):262–70.
    BACKGROUND: Mapping functional areas of the brain is important for planning tumour resections. With the increased use of functional magnetic resonance imaging (fMRI) for presurgical planning, there is a need to validate that fMRI activation mapping is consistent with the mapping obtained during surgery using direct electrocortical stimulation (DECS). METHODS: A quantitative comparison of DECS and fMRI mapping techniques was performed, using a patient-specific conductivity model to find the current distribution resulting from each stimulation site. The resulting DECS stimulation map was compared to the fMRI activation map, using the maximal Dice similarity coefficient (MDSC). RESULTS: Our results show some agreement between these two mapping techniques—the stimulation site with the largest MOSC was the only site that demonstrated intra-operative effect. CONCLUSIONS: There is a substantial effort to improve the techniques used to map functional areas, particularly using fMRI. It seems likely that fMRI will eventually provide a valid non-invasive means for functional mapping.
  • Pohl KM, Fisher J, Bouix S, Shenton M, McCarley RW, Grimson EL, Kikinis R, Wells WM. Using the logarithm of odds to define a vector space on probabilistic atlases. Med Image Anal. 2007;11(5):465–77.
    The logarithm of the odds ratio (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology, as an alternative representation of probabilities. Here, we use LogOdds to place probabilistic atlases in a linear vector space. This representation has several useful properties for medical imaging. For example, it not only encodes the shape of multiple anatomical structures but also captures some information concerning uncertainty. We demonstrate that the resulting vector space operations of addition and scalar multiplication have natural probabilistic interpretations. We discuss several examples for placing label maps into the space of LogOdds. First, we relate signed distance maps, a widely used implicit shape representation, to LogOdds and compare it to an alternative that is based on smoothing by spatial Gaussians. We find that the LogOdds approach better preserves shapes in a complex multiple object setting. In the second example, we capture the uncertainty of boundary locations by mapping multiple label maps of the same object into the LogOdds space. Third, we define a framework for non-convex interpolations among atlases that capture different time points in the aging process of a population. We evaluate the accuracy of our representation by generating a deformable shape atlas that captures the variations of anatomical shapes across a population. The deformable atlas is the result of a principal component analysis within the LogOdds space. This atlas is integrated into an existing segmentation approach for MR images. We compare the performance of the resulting implementation in segmenting 20 test cases to a similar approach that uses a more standard shape model that is based on signed distance maps. On this data set, the Bayesian classification model with our new representation outperformed the other approaches in segmenting subcortical structures.
  • Hershkovits E, Tannenbaum A, Tannenbaum R. Polymer Adsorption on Curved Surfaces: A Geometric Approach. J Phys Chem C Nanomater Interfaces. 2007;111(33):12369–12375.
    In this article, we have developed a simple model that describes the adsorption of polymer chains from a solution having a good solvent onto a reactive surface of varying curvatures. In order to evaluate the impact of particle size on the adsorption process, we have probed the adsorption of poly(methyl methacrylate) (PMMA) on aluminum oxide (Al2O3) surfaces belonging to particles of different sizes. The basic approach assumed that the details of the chemisorption mechanism of PMMA on aluminum oxide surfaces are independent of surface curvature. The combination of the experimental results with the theoretical approach that we have developed show the existence of three different regimes of adsorption of polymer chains onto the surfaces of metal nanoparticles.
  • Pohl KM, Bouix S, Nakamura M, Rohlfing T, McCarley RW, Kikinis R, Grimson EL, Shenton ME, Wells WM. A hierarchical algorithm for MR brain image parcellation. IEEE Trans Med Imaging. 2007;26(9):1201–12.
    We introduce an algorithm for segmenting brain magnetic resonance (MR) images into anatomical compartments such as the major tissue classes and neuro-anatomical structures of the gray matter. The algorithm is guided by prior information represented within a tree structure. The tree mirrors the hierarchy of anatomical structures and the subtrees correspond to limited segmentation problems. The solution to each problem is estimated via a conventional classifier. Our algorithm can be adapted to a wide range of segmentation problems by modifying the tree structure or replacing the classifier. We evaluate the performance of our new segmentation approach by revisiting a previously published statistical group comparison between first-episode schizophrenia patients, first-episode affective psychosis patients, and comparison subjects. The original study is based on 50 MR volumes in which an expert identified the brain tissue classes as well as the superior temporal gyrus, amygdala, and hippocampus. We generate analogous segmentations using our new method and repeat the statistical group comparison. The results of our analysis are similar to the original findings, except for one structure (the left superior temporal gyrus) in which a trend-level statistical significance (p = 0.07) was observed instead of statistical significance.