Andres Guesalaga

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Three-dimensional (3D) k-space trajectories are needed to acquire volumetric images in MRI. While scan time is determined by the trajectory efficiency, image quality and distortions depend on the shape of the trajectories. There are several 3D trajectory strategies for sampling the k-space using rectilinear or curve schemes. Since there is no evidence about(More)
Magnetic resonance imaging (MRI) provides bidimensional images with high definition and selectivity. Selective excitations are achieved applying a gradient and a radio frequency (RF) pulse simultaneously. They are modeled by the Bloch differential equation, which has no closed-form solution. Most methods for designing RF pulses are derived from(More)
In 3D MRI, sampling k-space with traditional trajectories can be excessively time-consuming. Fast imaging trajectories are used in an attempt to efficiently cover the k-space and reduce the scan time without significantly affecting the image quality. In many applications, further reductions in scan time can be achieved via undersampling of the k-space;(More)
Magnetic resonance spectroscopic imaging (MRSI) is a noninvasive technique for producing spatially localized spectra. MRSI presents the important challenge of reducing the scan time while maintaining the spatial resolution. The preferred approach for this is to use time-varying readout gradients to collect the spatial and chemical-shift information. Fast,(More)
This paper describes a novel technique to obtain radar biases estimates that can effectively reduce mismatches in track association algorithms. This is accomplished by matching ship-borne radar images to geo-referenced satellite images. The matching is performed through the minimization of the averaged partial Hausdorff distance between data points in each(More)
This paper presents a robust method for localization of mobile robots in environments that may be cluttered and that not necessarily have a polygonal structure. The estimation of the position and orientation of the robot relies on the minimization of the modified Hausdorff distance between ladar range measurements and a map of the environment. The approach(More)
The paper describes a technique to match satellite and radar images using the Hausdorff distance (HD). Minimization of the average of a truncated array of sorted Hausdorff distances is used to get estimates for the radar location, together with sensor bias errors and its platform speed vector. The technique is applied to maritime navigation, where(More)