Absfracl-The standard Capon beamformer (SCB) is known to have better resolution and much better interference rejection capability than the standard data-independent beamformer when the array steering vector is accurately known. However, the major problem of SCB is that it lacks robustness in the presence of array steering vector errors. In this paper, we provide a natural extension of SCB, obtained via covariance matrix fitting, to the case of uncertain steering vectors by enforcing a double constraint on the array steering vector, v i z a constant norm constraint and a spherical uncertainty set constraint, which we refer to as the doubly constrained robust Capon beamformer (DCRCB). DCRCB can be efficiently computed at a comparable cost with that of SCB. Performance comparisons of DCRCB and our previously proposed robust Capon beamformer (RCB) are also presented via a number of numerical examples.
Diffusion tensor imaging (DTI) can provide the fundamental information required for viewing structural connectivity. However, robust and accurate acquisition and processing algorithms are needed to accurately map the nerve connectivity. In this paper, we present a novel algorithm for extracting and visualizing the fiber tracts in the CNS specifically, the spinal cord. The automatic fiber tract mapping problem will be solved in two phases, namely a data smoothing phase and a fiber tract mapping phase. In the former, smoothing is achieved via a weighted TV-norm minimization which strives to smooth while retaining all relevant detail. For the fiber tract mapping, a smooth 3D vector field indicating the dominant anisotropic direction at each spatial location is computed from the smoothed data. Visualization of the fiber tracts is achieved by adapting a known Computer Graphics technique called the line integral convolution, which has the advantage of being able to cope with singularities in the vector field and is a resolution independent way of visualizing the 3D vector field corresponding to the dominant eigen vectors of the diffusion tensor field. Examples are presented to depict the performance of the visualization scheme on three DT-MR data sets, one from a normal and another from an injured rat spinal cord and a third from a rat brain.
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