Slide 1
An Anatomically Curated Fiber Clustering White Matter Atlas for Consistent White Matter Tract Parcellation across the Lifespan
Slide 2
An Immersive Virtual Reality Environment for Diagnostic Imaging
Slide 3
Inter-site and Inter-scanner Diffusion MRI Data Harmonization
Slide 4
The Open Anatomy Browser: A Collaborative Web-Based Viewer for Interoperable Anatomy Atlases
Slide 5
Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort
Slide 6
Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability
Slide 7
Supra-Threshold Fiber Cluster Statistics for Data-Driven Whole Brain Tractography Analysis
Slide 8
Free Water Modeling of Peritumoral Edema using Multi-fiber Tractography
Slide 8
Estimation of Bounded and Unbounded Trajectories in Diffusion MRI
Slide 9
Principal Gradient of Macroscale Cortical Organization
Slide 10
Evolution of a Simultaneous Segmentation and Atlas Registration
Slide 11
Multi-modality MRI-based Atlas of the Brain
Slide 12
Intracranial Fluid Redistribution
Slide 13
Corticospinal Tract Modeling for Neurosurgical Planning by Tracking through Regions of Peritumoral Edema and Crossing Fibers
Slide 14
Automated White Matter Fiber Tract Identification in Patients with Brain Tumors
Slide 15
State-space Models of Mental Processes from fMRI
Slide 16
Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach
Slide 17
Tractography-driven Groupwise Multi-Scale Parcellation of the Cortex
Slide 18
Gray Matter Alterations in Early Aging
Slide 19
Statistical Shape Analysis: From Landmarks to Diffeomorphisms
Slide 20
A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation
Slide 21
Joint Modeling of Imaging and Genetic Variability
Slide 22
MR-Ultrasound Fusion for Neurosurgery
Slide 23
Diffusion MRI and Tumor Heterogeneity
Slide 24
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

  • Yeo BTT, Sabuncu MR, Desikan R, Fischl B, Golland P. Effects of registration regularization and atlas sharpness on segmentation accuracy. Med Image Comput Comput Assist Interv. 2007;10(Pt 1):683–91.
    In this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of "sharpness" and the joint registration-segmentation of a new brain with these atlases. In non-rigid registration, the tradeoff between warp regularization and image fidelity is typically set empirically. In segmentation, this leads to a probabilistic atlas of arbitrary "sharpness": weak regularization results in well-aligned training images and a "sharp" atlas; strong regularization yields a "blurry" atlas. We study the effects of this tradeoff in the context of cortical surface parcellation by comparing three special cases of our framework, namely: progressive registration-segmentation of a new brain to increasingly "sharp" atlases with increasingly flexible warps; secondly, progressive registration to a single atlas with increasingly flexible warps; and thirdly, registration to a single atlas with fixed constrained warps. The optimal parcellation in all three cases corresponds to a unique balance of atlas "sharpness" and warp regularization that yield statistically significant improvements over the previously demonstrated parcellation results.
  • Melonakos J, Mohan V, Niethammer M, Smith K, Kubicki M, Tannenbaum A. Finsler tractography for white matter connectivity analysis of the cingulum bundle. Med Image Comput Comput Assist Interv. 2007;10(Pt 1):36–43.
    In this paper, we present a novel approach for the segmentation of white matter tracts based on Finsler active contours. This technique provides an optimal measure of connectivity, explicitly segments the connecting fiber bundle, and is equipped with a metric which is able to utilize the directional information of high angular resolution data. We demonstrate the effectiveness of the algorithm for segmenting the cingulum bundle.
  • Hata N, Piper S, Jolesz FA, Tempany CM, Black PM, Morikawa S, Iseki H, Hashizume M, Kikinis R. Application of Open Source Image Guided Therapy Software in MR-guided Therapies. Med Image Comput Comput Assist Interv. 2007;10(Pt 1):491–8.
    We present software engineering methods to provide free open-source software for MR-guided therapy. We report that graphical representation of the surgical tools, interconnectively with the tracking device, patient-to-image registration, and MRI-based thermal mapping are crucial components of MR-guided therapy in sharing such software. Software process includes a network-based distribution mechanism by multi-platform compiling tool CMake, CVS, quality assurance software DART. We developed six procedures in four separate clinical sites using proposed software engineering and process, and found the proposed method is feasible to facilitate multicenter clinical trial of MR-guided therapies. Our future studies include use of the software in non-MR-guided therapies.
  • BACKGROUND AND PURPOSE: Formation of lesions in multiple sclerosis (MS) shows pronounced short-term fluctuation of MR imaging hyperintensity and size, a qualitatively known but poorly characterized phenomenon. With the use of time-series modeling of MR imaging intensity, our study relates the short-term dynamics of new T2 lesion formation to those of contrast enhancement and markers of long-term progression of disease. MATERIALS AND METHODS: We analyzed 915 examinations from weekly to monthly MR imaging in 40 patients with MS using a time-series model, emulating 2 opposing processes of T2 prolongation and shortening, respectively. Patterns of activity, duration, and residual hyperintensity within new T2 lesions were measured and evaluated for relationships to disability, atrophy, and clinical phenotype in long-term follow-up. RESULTS: Significant T2 activity was observed for 8 to 10 weeks beyond contrast enhancement, which suggests that T2 MR imaging is sensitive to noninflammatory processes such as degeneration and repair. Larger lesions showed longer subacute phases but disproportionally more recovery. Patients with smaller average peak lesion size showed trends toward greater disability and proportional residual damage. Higher rates of disability or atrophy were associated with subjects whose lesions showed greater residual hyperintensity. CONCLUSION: Smaller lesions appeared disproportionally more damaging than larger lesions, with lesions in progressive MS smaller and of shorter activity than in relapsing-remitting MS. Associations of lesion dynamics with rates of atrophy and disability and clinical subtype suggest that changes in lesion dynamics may represent a shift from inflammatory toward degenerative disease activity and greater proximity to a progressive stage, possibly allowing staging of the progression of MS earlier, before atrophy or disability develops.
  • Aja-Fernández S, Alberola-López C, Westin CF. Signal LMMSE estimation from multiple samples in MRI and DT-MRI. Med Image Comput Comput Assist Interv. 2007;10(Pt 2):368–75.
    A method to estimate the magnitude MR data from several noisy samples is presented. It is based on the Linear Minimum Mean Squared Error (LMMSE) estimator for the Rician noise model when several scanning repetitions are available. This method gives a closed-form analytical solution that takes into account the probability distribution of the data as well as the existing level of noise, showing a better performance than methods such as the average or the median.