Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. [2002]: Neuron 33:341-355; Fischl et al. [2004]: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects.
Pubmed ID: 17295313 RIS Download
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Set of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries.
View all literature mentionsSuite of image analysis tools designed to process magnetic resonance images (MRI) of the human head. BrainSuite provides an automatic sequence to extract genus-zero cortical surface mesh models from the MRI. It also provides a set of viewing tools for exploring image and surface data. The latest release includes graphical user interface and command line versions of the tools. BrainSuite was specifically designed to guide its users through the process of cortical surface extraction. NITRC has written the software to require minimal user interaction and with the goal of completing the entire process of extracting a topologically spherical cortical surface from a raw MR volume within several minutes on a modern workstation. The individual components of BrainSuite may also be used for soft tissue, skull and scalp segmentation and for surface analysis and visualization. BrainSuite was written in Microsoft Visual C using the Microsoft Foundation Classes for its graphical user interface and the OpenGL library for rendering. BrainSuite runs under the Windows 2000 and Windows XP Professional operating systems. BrainSuite features include: * Sophisticated visualization tools, such as MRI visualization in 3 orthogonal views (either separately or in 3D view), and overlayed surface visualization of cortex, skull, and scalp * Cortical surface extraction, using a multi-stage user friendly approach. * Tools including brain surface extraction, bias field correction, voxel classification, cerebellum removal, and surface generation * Topological correction of cortical surfaces, which uses a graph-based approach to remove topological defects (handles and holes) and ensure a tessellation with spherical topology * Parameterization of generated cortical surfaces, minimizing a harmonic energy functional in the p-norm * Skull and scalp surface extraction
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE, documented August 25, 2013 Public curated repository of peer reviewed fMRI studies and their underlying data. This Web-accessible database has data mining capabilities and the means to deliver requested data to the user (via Web, CD, or digital tape). Datasets available: 107 NOTE: The fMRIDC is down temporarily while it moves to a new home at UCLA. Check back again in late Jan 2013! The goal of the Center is to help speed the progress and the understanding of cognitive processes and the neural substrates that underlie them by: * Providing a publicly accessible repository of peer-reviewed fMRI studies. * Providing all data necessary to interpret, analyze, and replicate these fMRI studies. * Provide training for both the academic and professional communities. The Center will accept data from those researchers who are publishing fMRI imaging articles in peer-reviewed journals. The goal is to serve the entire fMRI community.
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