BACKGROUND AND PURPOSE: Talairach-based parcellation (TP) of human brain magnetic resonance imaging (MRI) data has been used increasingly in clinical research to make regional measurements of brain structures in vivo. Recently, TP has been applied to pediatric research to elucidate the changes in regional brain volumes related to several neurological disorders. However, all freely available tools have been designed to parcellate adult brain MRI data. Parcellation of neonatal MRI data is very challenging owing to the lack of strong signal contrast, variability in signal intensity within tissues, and the small size and thus difficulty in identifying small structures used as landmarks for TP. Hence the authors designed and validated a new interactive tool to parcellate brain MRI data from newborns and young infants. METHODS: The authors’ tool was developed as part of a postprocessing pipeline, which includes registration of multichannel MR images, segmentation, and parcellation of the segmented data. The tool employs user-friendly interactive software to visualize and assign the anatomic landmarks required for parcellation, after which the planes and parcels are generated automatically by the algorithm. The authors then performed 3 sets of validation experiments to test the precision and reliability of their tool. RESULTS: Validation experiments of intra-and interrater reliability on data obtained from newborn and 1-year-old children showed a very high sensitivity of >95% and specificity >99.9%. The authors also showed that rotating and reformatting the original MRI data results in a statistically significant difference in parcel volumes, demonstrating the importance of using a tool such as theirs that does not require realignment of the data prior to parcellation. CONCLUSIONS: To the authors’ knowledge, the presented approach is the first TP method that has been developed and validated specifically for neonatal brain MRI data. Their approach would also be valuable for the analysis of brain MRI data from older children and adults.

We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.

PURPOSE: To prospectively investigate the factors—including subject, brain hemisphere, study site, field strength, imaging unit vendor, imaging run, and examination visit—affecting the reproducibility of functional magnetic resonance (MR) imaging activations based on a repeated sensory-motor (SM) task. MATERIALS AND METHODS: The institutional review boards of all participating sites approved this HIPAA-compliant study. All subjects gave informed consent. Functional MR imaging data were repeatedly acquired from five healthy men aged 20-29 years who performed the same SM task at 10 sites. Five 1.5-T MR imaging units, four 3.0-T units, and one 4.0-T unit were used. The subjects performed bilateral finger tapping on button boxes with a 3-Hz audio cue and a reversing checkerboard. In a block design, 15-second epochs of alternating baseline and tasks yielded 85 acquisitions per run. Functional MR images were acquired with block-design echo-planar or spiral gradient-echo sequences. Brain activation maps standardized in a unit-sphere for the left and right hemispheres of each subject were constructed. Areas under the receiver operating characteristic curve, intraclass correlation coefficients, multiple regression analysis, and paired Student t tests were used for statistical analyses.

Inspired by the work by Gomes et al., we describe and analyze a vector distance function approach for the implicit evolution of closed curves of codimension larger than one. The approach is set up in complete generality, and then applied to the evolution of dynamic geometric active contours in [Formula: see text] (codimension three case). In order to carry this out one needs an explicit expression for the zero level set for which we propose a discrete connectivity method. This leads us to make connections with the new theory of cubical homology. We provide some explicit simulation results in order to illustrate the methodology.

In this paper, we describe a first step towards a collaborative extension of the well-known 3D-Slicer; this platform is nowadays used as a standalone tool for both surgical planning and medical intervention. We show how this tool can be easily modified to make it collaborative so that it may constitute an integrated environment for expertise exchange as well as a useful tool for academic purposes.