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Sampling Pattern (sampling + pattern)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Resampling Feature and Blend Regions in Polygonal Meshes for Surface Anti-Aliasing

COMPUTER GRAPHICS FORUM, Issue 3 2001
Mario Botsch
Efficient surface reconstruction and reverse engineering techniques are usually based on a polygonal mesh representation of the geometry: the resulting models emerge from piecewise linear interpolation of a set of sample points. The quality of the reconstruction not only depends on the number and density of the sample points but also on their alignment to sharp and rounded features of the original geometry. Bad alignment can lead to severe alias artifacts. In this paper we present a sampling pattern for feature and blend regions which minimizes these alias errors. We show how to improve the quality of a given polygonal mesh model by resampling its feature and blend regions within an interactive framework. We further demonstrate sophisticated modeling operations that can be implemented based on this resampling technique. [source]

Improving k - t SENSE by adaptive regularization

MAGNETIC RESONANCE IN MEDICINE, Issue 5 2007
Dan Xu
Abstract The recently proposed method known as k - t sensitivity encoding (SENSE) has emerged as an effective means of improving imaging speed for several dynamic imaging applications. However, k - t SENSE uses temporally averaged data as a regularization term for image reconstruction. This may not only compromise temporal resolution, it may also make some of the temporal frequency components irrecoverable. To address that issue, we present a new method called spatiotemporal domain-based unaliasing employing sensitivity encoding and adaptive regularization (SPEAR). Specifically, SPEAR provides an improvement over k - t SENSE by generating adaptive regularization images. It also uses a variable-density (VD), sequentially interleaved k - t space sampling pattern with reference frames for data acquisition. Simulations based on experimental data were performed to compare SPEAR, k - t SENSE, and several other related methods, and the results showed that SPEAR can provide higher temporal resolution with significantly reduced image artifacts. Ungated 3D cardiac imaging experiments were also carried out to test the effectiveness of SPEAR, and real-time 3D short-axis images of the human heart were produced at 5.5 frames/s temporal resolution and 2.4 × 1.2 × 8 mm3 spatial resolution with eight slices. Magn Reson Med 57:918,930, 2007. © 2007 Wiley-Liss, Inc. [source]

SYSTEMS WITH NONEQUIDISTANT SAMPLING: CONTROLLABLE?

ASIAN JOURNAL OF CONTROL, Issue 4 2005
OBSERVABLE?
ABSTRACT Some qualitative properties of systems with nonequidistant sampling are investigated. First, it is proved that the nonequidistant sampling pattern mentioned in [1] does not affect the controllability and observability of time-varying linear systems during discretization. The result is claimed to be true for linear systems with periodic behavior and time-varying sampling. Second, closed-loop stability conditions are established, respectively, for linear and nonlinear sampled-data systems consisting of continuous plants and linear digital feedback controllers. The stability results are extended to general systems consisting of nonlinear continuous plants and nonlinear digital controllers with time-varying sampling periods. [source]

Encoding and reconstruction in parallel MRI

NMR IN BIOMEDICINE, Issue 3 2006
Klaas P. Pruessmann
Abstract The advent of parallel MRI over recent years has prompted a variety of concepts and techniques for performing parallel imaging. A main distinguishing feature among these is the specific way of posing and solving the problem of image reconstruction from undersampled multiple-coil data. The clearest distinction in this respect is that between k -space and image-domain methods. The present paper reviews the basic reconstruction approaches, aiming to emphasize common principles along with actual differences. To this end the treatment starts with an elaboration of the encoding mechanisms and sampling strategies that define the reconstruction task. Based on these considerations a formal framework is developed that permits the various methods to be viewed as different solutions of one common problem. Besides the distinction between k -space and image-domain approaches, special attention is given to the implications of general vs lattice sampling patterns. The paper closes with remarks concerning noise propagation and control in parallel imaging and an outlook upon key issues to be addressed in the future. Copyright © 2006 John Wiley & Sons, Ltd. [source]