Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 5 Articles
Cerebral blood flow (CBF) is a well-established correlate of brain function and therefore an essential parameter for studying the\r\nbrain at both normal and diseased states. Arterial spin labeling (ASL) is a noninvasive fMRI technique that uses arterial water\r\nas an endogenous tracer to measure CBF. ASL provides reliable absolute quantification of CBF with higher spatial and temporal\r\nresolution than other techniques. And yet, the routine application of ASL has been somewhat limited. In this review, we start by\r\nhighlighting theoretical complexities and technical challenges of ASL fMRI for basic and clinical research.While underscoring the\r\nmain advantages of ASL versus other techniques such as BOLD, we also expound on inherent challenges and confounds in ASL\r\nperfusion imaging. In closing, we expound on several exciting developments in the field that we believe will make ASL reach its\r\nfull potential in neuroscience research....
Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the\r\nhuman brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction\r\napproaches are typically optimized for standard resolution (~1mm) data and are not directly applicable to higher resolution\r\nimages. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and\r\nscales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The\r\nmethod uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping,\r\nhas nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow\r\nsulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained\r\nby topology-preserving level set approach. The methodââ?¬â?¢s performance is illustrated on exvivo images with 0.25ââ?¬â??0.35mm isotropic\r\nvoxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data\r\nsets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects....
Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine\r\nactivation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI.\r\nA major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has\r\nnot been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using\r\nthe equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference\r\nof general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without\r\nreestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients\r\nof CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data\r\nthan the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from\r\nfMRI data were used to demonstrate the advantage of this novel test statistic....
Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical\r\nimage registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks\r\non the source (image to be transformed) and target (reference image). Point landmarks are placed at regular intervals on contours\r\nof anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity\r\nand displacements of the homologous landmarks. The method was evaluated in two cases (n = 5 each). In one, MRI was registered\r\nto histological sections; in the second, geometric distortions in EPI MRI were corrected.Normalizedmutual information and target\r\nregistration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks.\r\nResults. Statistical analyses demonstrated significant improvement (P < 0.05) in registration accuracy by landmark optimization\r\nin most data sets and trends towards improvement (P < 0.1) in others as compared to manual landmark selection....
Multiple sclerosis (MS) is a complicated disease characterized by heterogeneous pathology that varies across individuals. Accurate\r\nidentification and quantification of pathological changes may facilitate a better understanding of disease pathogenesis and\r\nprogression and help identify novel therapies for MS patients. Texture analysis evaluates interpixel relationships that generate\r\ncharacteristic organizational patterns in an image, many of which are beyond the ability of visual perception. Given its promise\r\ndetecting subtle structural alterations texture analysis may be an attractive means to evaluate disease activity and evolution. It\r\nmay also become a new tool to assess therapeutic efficacy if technique issues are resolved and pathological correlates are further\r\nconfirmed. This paper describes the concept, strategies, and considerations of MRI texture analysis; summarizes applications of\r\ntexture analysis in MS as a measure of tissue integrity and its clinical relevance; then discusses potentially future directions of\r\ntexture analysis in MS....
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