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

  • Hata N, Nabavi A, Wells WM III, Warfield SK, Kikinis R, Black PM, Jolesz FA. Three-dimensional Optical Flow Method for Measurement of Volumetric Brain Deformation from Intraoperative MR Images. J Comput Assist Tomogr. 2000;24(4):531–8.
    A three-dimensional optical flow method to measure volumetric brain deformation from sequential intraoperative MR images and preliminary clinical results from five cases are reported. Intraoperative MR images were scanned before and after dura opening, twice during tumor resection, and immediately after dura closure. The maximum cortical surface shift measured was 11 mm and subsurface shift was 4 mm. The computed deformation field was most satisfactory when the skin was segmented and removed from the images before the optical flow computation.
  • Guttmann CRG, Benson RR, Warfield SK, Wei X, Anderson MC, Hall CB, Abu-Hasaballah K, Mugler JP, Wolfson LI. White Matter Abnormalities in Mobility-impaired Older Persons. Neurology. 2000;54(6):1277–83.
    OBJECTIVE: To investigate the relationship between white matter abnormalities and impairment of gait and balance in older persons. METHODS: Quantitative MRI was used to evaluate the brain tissue compartments of 28 older individuals separated into normal and impaired groups on the basis of mobility performance testing using the Short Physical Performance Battery. In addition, individuals were tested on six indices of gait and balance. For imaging data, segmentation of intracranial volume into four tissue classes was performed using template-driven segmentation, in which signal-intensity-based statistical tissue classification is refined using a digital brain atlas as anatomic template. RESULTS: Both decreased white matter volume, which was age-related, and increased white matter signal abnormalities, which were not age-related, were observed in the mobility-impaired group compared with the control subjects. The average volume of white matter signal abnormalities for impaired individuals was nearly double that of control subjects. CONCLUSIONS: This cross-sectional study suggests that decreased white matter volume is age-related, whereas increased white matter signal abnormalities are most likely to occur as a result of disease. Both of these changes are independently associated with impaired mobility in older persons and therefore likely to be additive factors of motor disability.
  • Westin CF, Richolt J, Moharir V, Kikinis R. Affine Adaptive Filtering of CT Data. Med Image Anal. 2000;4(2):161–77.
    A novel method for resampling and enhancing image data using multidimensional adaptive filters is presented. The underlying issue that this paper addresses is segmentation of image structures that are close in size to the voxel geometry. Adaptive filtering is used to reduce both the effects of partial volume averaging by resampling the data to a lattice with higher sample density and to reduce the image noise level. Resampling is achieved by constructing filter sets that have subpixel offsets relative to the original sampling lattice. The filters are also frequency corrected for ansisotropic voxel dimensions. The shift and the voxel dimensions are described by an affine transform and provides a model for tuning the filter frequency functions. The method has been evaluated on CT data where the voxels are in general non cubic. The in-plane resolution in CT image volumes is often higher by a factor of 3-10 than the through-plane resolution. The method clearly shows an improvement over conventional resampling techniques such as cubic spline interpolation and sinc interpolation.
  • Nabavi A, Mamisch TC, Gering D, Kacher DF, Pergolizzi RS, Wells WM, Kikinis R, Black PM, Jolesz FA. Image-guided Therapy and Intraoperative MRI in Neurosurgery. Minim Invasive Ther Allied Technol. 2000;9(3-4):277–86.
    Computer-assisted 3D planning, navigation and the possibilities offered by intra-operative imaging updates have made a large impact on neurological surgery. Three-dimensional rendering of complex medical image information, as well as co-registration of multimodal sources has reached a highly sophisticated level. When introduced into surgical navigation however, this pre-operative data is unable to account for intra-operative changes, (’brain-shift’). To update structural information during surgery, an open-configured, intra-operative MRI (Signa SP, 0.5 T) was realised at our institution in 1995. The design, advantages, limitations and current applications of this system are discussed, with emphasis on the integration of imaging into procedures. We also introduce our integrated platform for intra-operative visualisation and navigation, the 3D Slicer.
  • Warfield SK, Kaus MR, Jolesz FA, Kikinis R. Adaptive, Template Moderated, Spatially Varying Statistical Classification. Med Image Anal. 2000;4(1):43–55.
    A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy from medical images. The new algorithm is a form of spatially varying statistical classification, in which an explicit anatomical template is used to moderate the segmentation obtained by statistical classification. The algorithm consists of an iterated sequence of spatially varying classification and nonlinear registration, which forms an adaptive, template moderated (ATM), spatially varying statistical classification (SVC). Classification methods and nonlinear registration methods are often complementary, both in the tasks where they succeed and in the tasks where they fail. By integrating these approaches the new algorithm avoids many of the disadvantages of each approach alone while exploiting the combination. The ATM SVC algorithm was applied to several segmentation problems, involving different image contrast mechanisms and different locations in the body. Segmentation and validation experiments were carried out for problems involving the quantification of normal anatomy (MRI of brains of neonates) and pathology of various types (MRI of patients with multiple sclerosis, MRI of patients with brain tumors, MRI of patients with damaged knee cartilage). In each case, the ATM SVC algorithm provided a better segmentation than statistical classification or elastic matching alone.