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

  • Bergmann O, Kindlmann G, Lundervold A, Westin CF. Diffusion k-tensor estimation from Q-ball imaging using discretized principal axes. Med Image Comput Comput Assist Interv. 2006;9(Pt 2):268–75.
    A reoccurring theme in the diffusion tensor imaging literature is the per-voxel estimation of a symmetric 3 x 3 tensor describing the measured diffusion. In this work we attempt to generalize this approach by calculating 2 or 3 or up to k diffusion tensors for each voxel. We show that our procedure can more accurately describe the diffusion particularly when crossing fibers or fiber-bundles are present in the datasets.
  • Dauguet J, Peled S, Berezovskii V, Delzescaux T, Warfield SK, Born R, Westin CF. 3D histological reconstruction of fiber tracts and direct comparison with diffusion tensor MRI tractography. Med Image Comput Comput Assist Interv. 2006;9(Pt 1):109–16.
    A classical neural tract tracer, WGA-HRP, was injected at multiple sites within the brain of a macaque monkey. Histological sections of the labeled fiber tracts were reconstructed in 3D, and the fibers were segmented and registered with the anatomical post-mortem MRI from the same animal. Fiber tracing along the same pathways was performed on the DTI data using a classical diffusion tracing technique. The fibers derived from the DTI were compared with those segmented from the histology in order to evaluate the performance of DTI fiber tracing. While there was generally good agreement between the two methods, our results reveal certain limitations of DTI tractography, particularly at regions of fiber tract crossing or bifurcation.
  • Estepar RSJ, Washko GG, Silverman EK, Reilly JJ, Kikinis R, Westin CF. Accurate airway wall estimation using phase congruency. Med Image Comput Comput Assist Interv. 2006;9(Pt 2):125–34.
    Quantitative analysis of computed tomographic (CT) images of the lungs is becoming increasingly useful in the medical and surgical management of subjects with Chronic Obstructive Pulmonary Disease (COPD). Current methods for the assessment of airway wall work well in idealized models of the airway. We propose a new method for airway wall detection based on phase congruency. This method does not rely on either a specific model of the airway or the point spread function of the scanner. Our results show that our method gives a better localization of the airway wall than "full width at a half max" and is less sensitive to different reconstruction kernels and radiation doses.
  • Kuroki N, Shenton ME, Salisbury DF, Hirayasu Y, Onitsuka T, Ersner-Hershfield H, Yurgelun-Todd D, Kikinis R, Jolesz FA, McCarley RW. Middle and inferior temporal gyrus gray matter volume abnormalities in first-episode schizophrenia: an MRI study. Am J Psychiatry. 2006;163(12):2103–10.
    OBJECTIVE: Magnetic resonance imaging (MRI) studies of schizophrenia reveal temporal lobe structural brain abnormalities in the superior temporal gyrus and the amygdala-hippocampal complex. However, the middle and inferior temporal gyri have received little investigation, especially in first-episode schizophrenia. METHOD: High-spatial-resolution MRI was used to measure gray matter volume in the inferior, middle, and superior temporal gyri in 20 patients with first-episode schizophrenia, 20 patients with first-episode affective psychosis, and 23 healthy comparison subjects. RESULTS: Gray matter volume in the middle temporal gyrus was smaller bilaterally in patients with first-episode schizophrenia than in comparison subjects and in patients with first-episode affective psychosis. Posterior gray matter volume in the inferior temporal gyrus was smaller bilaterally in both patient groups than in comparison subjects. Among the superior, middle, and inferior temporal gyri, the left posterior superior temporal gyrus gray matter in the schizophrenia group had the smallest volume, the greatest percentage difference, and the largest effect size in comparisons with healthy comparison subjects and with affective psychosis patients. CONCLUSIONS: Smaller gray matter volumes in the left and right middle temporal gyri and left posterior superior temporal gyrus were present in schizophrenia but not in affective psychosis at first hospitalization. In contrast, smaller bilateral posterior inferior temporal gyrus gray matter volume is present in both schizophrenia and affective psychosis at first hospitalization. These findings suggest that smaller gray matter volumes in the dorsal temporal lobe (superior and middle temporal gyri) may be specific to schizophrenia, whereas smaller posterior inferior temporal gyrus gray matter volumes may be related to pathology common to both schizophrenia and affective psychosis.
  • Goldberg-Zimring D, Warfield SK. Novel image processing techniques to better understand white matter disruption in multiple sclerosis. Autoimmun Rev. 2006;5(8):544–8.
    In Multiple Sclerosis (MS) patients, conventional magnetic resonance imaging (MRI) shows a pattern of white matter (WM) disruption but may also overlook some WM damage. Diffusion tensor MRI (DT-MRI) can provide important in-vivo information about fiber direction that is not provided by conventional MRI. The geometry of diffusion tensors can quantitatively characterize the local structure in tissues. The integration of both conventional MRI and DT-MRI measures together with connectivity-based regional assessment provide a better understanding of the nature and the location of WM abnormalities. Image processing and visualization techniques have been developed and applied to study conventional MRI and DT-MRI of MS patients. These include methods of: Image Segmentation for identifying the different areas of the brain as well as to discriminate normal from abnormal WM, Computerized Atlases, which include structural information obtained from a set of subjects, and Tractographies which can aid in the delineation of WM fiber tracts by tracking connected diffusion tensors. These new techniques hold out the promise of improving our understanding of WM architecture and its disruption in diseases such as MS. In the present study, we review the work that has been done in the development of these techniques and illustrate their applications.