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

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


Recent Publications

  • Westin CF, Maier SE, Mamata H, Nabavi A, Jolesz FA, Kikinis R. Processing and Visualization for Diffusion Tensor MRI. Med Image Anal. 2002;6(2):93–108.
    This paper presents processing and visualization techniques for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). In DT-MRI, each voxel is assigned a tensor that describes local water diffusion. The geometric nature of diffusion tensors enables us to quantitatively characterize the local structure in tissues such as bone, muscle, and white matter of the brain. This makes DT-MRI an interesting modality for image analysis. In this paper we present a novel analytical solution to the Stejskal-Tanner diffusion equation system whereby a dual tensor basis, derived from the diffusion sensitizing gradient configuration, eliminates the need to solve this equation for each voxel. We further describe decomposition of the diffusion tensor based on its symmetrical properties, which in turn describe the geometry of the diffusion ellipsoid. A simple anisotropy measure follows naturally from this analysis. We describe how the geometry or shape of the tensor can be visualized using a coloring scheme based on the derived shape measures. In addition, we demonstrate that human brain tensor data when filtered can effectively describe macrostructural diffusion, which is important in the assessment of fiber-tract organization. We also describe how white matter pathways can be monitored with the methods introduced in this paper. DT-MRI tractography is useful for demonstrating neural connectivity (in vivo) in healthy and diseased brain tissue.
  • Wei X, Warfield SK, Zou KH, Wu Y, Li X, Guimond A, Mugler JP, Benson RR, Wolfson L, Weiner HL, Guttmann CRG. Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy. J Magn Reson Imaging. 2002;15(2):203–9.
    PURPOSE: To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA). MATERIALS AND METHODS: WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method. RESULTS: The segmentation method combining expectation-maximization (EM) tissue segmentation, template-driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z-score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects). CONCLUSION: The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility.
  • Benson RR, Guttmann CRG, Wei X, Warfield SK, Hall C, Schmidt JA, Kikinis R, Wolfson LI. Older People with Impaired Mobility have Specific Loci of Periventricular Abnormality on MRI. Neurology. 2002;58(1):48–55.
    BACKGROUND: Recent investigations using MRI suggest that older persons with mobility impairment have a greater volume of abnormal cerebral white matter compared with persons with normal mobility, thus raising the possibility that those with impairment have lesions in areas critical for the control of mobility. OBJECTIVE: To utilize automated image analysis methods to localize the specific regions of abnormal white matter that distinguish subjects with lower mobility from subjects with higher mobility. METHODS: Tissue classification was performed on subjects’ dual-echo long repetition time spin-echo MRI using computer algorithms operating on intensity criteria integrated with anatomic information. Statistical analysis of group differences was obtained after spatially normalizing each brain to a standard reference brain. RESULTS: Four discrete periventricular regions, including bilaterally symmetric frontal and bilateral occipitoparietal regions, were identified as being sensitive (frontal) or specific (occipitoparietal) in discriminating the subjects with lower mobility from subjects with higher mobility. The symmetry of these lesions in individual subjects suggested pathology other than arteriolar infarction. CONCLUSIONS: These results suggest that damage to discrete frontal and occipitoparietal periventricular white matter locations may be associated with a mobility disorder of aging.
  • Mamata H, Mamata Y, Westin CF, Shenton ME, Kikinis R, Jolesz FA, Maier SE. High-resolution line scan diffusion tensor MR imaging of white matter fiber tract anatomy. AJNR Am J Neuroradiol. 2002;23(1):67–75.
    BACKGROUND AND PURPOSE: MR diffusion tensor imaging permits detailed visualization of white matter fiber tracts. This technique, unlike T2-weighted imaging, also provides information about fiber direction. We present findings of normal white matter fiber tract anatomy at high resolution obtained by using line scan diffusion tensor imaging. METHODS: Diffusion tensor images in axial, coronal, and sagittal sections covering the entire brain volume were obtained with line scan diffusion imaging in six healthy volunteers. Images were acquired for b factors 5 and 1000 s/mm(2) at an imaging resolution of 1.7 x 1.7 x 4 mm. For selected regions, images were obtained at a reduced field of view with a spatial resolution of 0.9 x 0.9 x 3 mm. For each pixel, the direction of maximum diffusivity was computed and used to display the course of white matter fibers. RESULTS: Fiber directions derived from diffusion tensor imaging were consistent with known white matter fiber anatomy. The principal fiber tracts were well observed in all cases. The tracts that were visualized included the following: the arcuate fasciculus; superior and inferior longitudinal fasciculus; uncinate fasciculus; cingulum; external and extreme capsule; internal capsule; corona radiata; auditory and optic radiation; anterior commissure; corpus callosum; pyramidal tract; gracile and cuneatus fasciculus; medial longitudinal fasciculus; rubrospinal, tectospinal, central tegmental, and dorsal trigeminothalamic tract; superior, inferior, and middle cerebellar peduncle; pallidonigral and strionigral fibers; and root fibers of the oculomotor and trigeminal nerve. CONCLUSION: We obtained a complete set of detailed white matter fiber anatomy maps of the normal brain by means of line scan diffusion tensor imaging at high resolution. Near large bone structures, line scan produces images with minimal susceptibility artifacts.
  • Ruiz-Alzola J, Westin CF, Warfield SK, Alberola-López C, Maier SE, Kikinis R. Nonrigid Registration of 3D Tensor Medical Data. Med Image Anal. 2002;6(2):143–61.
    New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automated algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data using local matching. The paper extends the usual concept of similarity between two pieces of data to be matched, commonly used with scalar (intensity) data, to the general tensor case. Our approach to registration is based on a multiresolution scheme, where the deformation field estimated in a coarser level is propagated to provide an initial deformation in the next finer one. In each level, local matching of areas with a high degree of local structure and subsequent interpolation are performed. Consequently, we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator, which provides a novel framework for the interpolation of sparse vector fields in medical applications. The feasibility of the approach is illustrated by results on synthetic and clinical data.