15,091 research outputs found
Volumetric three-dimensional intravascular ultrasound visualization using shape-based nonlinear interpolation
BACKGROUND: Intravascular ultrasound (IVUS) is a standard imaging modality for identification of plaque formation in the coronary and peripheral arteries. Volumetric three-dimensional (3D) IVUS visualization provides a powerful tool to overcome the limited comprehensive information of 2D IVUS in terms of complex spatial distribution of arterial morphology and acoustic backscatter information. Conventional 3D IVUS techniques provide sub-optimal visualization of arterial morphology or lack acoustic information concerning arterial structure due in part to low quality of image data and the use of pixel-based IVUS image reconstruction algorithms. In the present study, we describe a novel volumetric 3D IVUS reconstruction algorithm to utilize IVUS signal data and a shape-based nonlinear interpolation. METHODS: We developed an algorithm to convert a series of IVUS signal data into a fully volumetric 3D visualization. Intermediary slices between original 2D IVUS slices were generated utilizing the natural cubic spline interpolation to consider the nonlinearity of both vascular structure geometry and acoustic backscatter in the arterial wall. We evaluated differences in image quality between the conventional pixel-based interpolation and the shape-based nonlinear interpolation methods using both virtual vascular phantom data and in vivo IVUS data of a porcine femoral artery. Volumetric 3D IVUS images of the arterial segment reconstructed using the two interpolation methods were compared. RESULTS: In vitro validation and in vivo comparative studies with the conventional pixel-based interpolation method demonstrated more robustness of the shape-based nonlinear interpolation algorithm in determining intermediary 2D IVUS slices. Our shape-based nonlinear interpolation demonstrated improved volumetric 3D visualization of the in vivo arterial structure and more realistic acoustic backscatter distribution compared to the conventional pixel-based interpolation method. CONCLUSIONS: This novel 3D IVUS visualization strategy has the potential to improve ultrasound imaging of vascular structure information, particularly atheroma determination. Improved volumetric 3D visualization with accurate acoustic backscatter information can help with ultrasound molecular imaging of atheroma component distribution
Binary morphological shape-based interpolation applied to 3-D tooth reconstruction
In this paper we propose an interpolation algorithm using a mathematical morphology morphing approach. The aim of this algorithm is to reconstruct the -dimensional object from a group of (n-1)-dimensional sets representing sections of that object. The morphing transformation modifies pairs of consecutive sets such that they approach in shape and size. The interpolated set is achieved when the two consecutive sets are made idempotent by the morphing transformation. We prove the convergence of the morphological morphing. The entire object is modeled by successively interpolating a certain number of intermediary sets between each two consecutive given sets. We apply the interpolation algorithm for 3-D tooth reconstruction
Tomographic Image Reconstruction of Fan-Beam Projections with Equidistant Detectors using Partially Connected Neural Networks
We present a neural network approach for tomographic imaging problem using interpolation methods and fan-beam projections. This approach uses a partially connected neural network especially assembled for solving tomographic\ud
reconstruction with no need of training. We extended the calculations to perform reconstruction with interpolation and to allow tomography of fan-beam geometry. The main goal is to aggregate speed while maintaining or improving the quality of the tomographic reconstruction process
Xampling: Signal Acquisition and Processing in Union of Subspaces
We introduce Xampling, a unified framework for signal acquisition and
processing of signals in a union of subspaces. The main functions of this
framework are two. Analog compression that narrows down the input bandwidth
prior to sampling with commercial devices. A nonlinear algorithm then detects
the input subspace prior to conventional signal processing. A representative
union model of spectrally-sparse signals serves as a test-case to study these
Xampling functions. We adopt three metrics for the choice of analog
compression: robustness to model mismatch, required hardware accuracy and
software complexities. We conduct a comprehensive comparison between two
sub-Nyquist acquisition strategies for spectrally-sparse signals, the random
demodulator and the modulated wideband converter (MWC), in terms of these
metrics and draw operative conclusions regarding the choice of analog
compression. We then address lowrate signal processing and develop an algorithm
for that purpose that enables convenient signal processing at sub-Nyquist rates
from samples obtained by the MWC. We conclude by showing that a variety of
other sampling approaches for different union classes fit nicely into our
framework.Comment: 16 pages, 9 figures, submitted to IEEE for possible publicatio
GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging
Tomography has made a radical impact on diverse fields ranging from the study
of 3D atomic arrangements in matter to the study of human health in medicine.
Despite its very diverse applications, the core of tomography remains the same,
that is, a mathematical method must be implemented to reconstruct the 3D
structure of an object from a number of 2D projections. In many scientific
applications, however, the number of projections that can be measured is
limited due to geometric constraints, tolerable radiation dose and/or
acquisition speed. Thus it becomes an important problem to obtain the
best-possible reconstruction from a limited number of projections. Here, we
present the mathematical implementation of a tomographic algorithm, termed
GENeralized Fourier Iterative REconstruction (GENFIRE). By iterating between
real and reciprocal space, GENFIRE searches for a global solution that is
concurrently consistent with the measured data and general physical
constraints. The algorithm requires minimal human intervention and also
incorporates angular refinement to reduce the tilt angle error. We demonstrate
that GENFIRE can produce superior results relative to several other popular
tomographic reconstruction techniques by numerical simulations, and by
experimentally by reconstructing the 3D structure of a porous material and a
frozen-hydrated marine cyanobacterium. Equipped with a graphical user
interface, GENFIRE is freely available from our website and is expected to find
broad applications across different disciplines.Comment: 18 pages, 6 figure
Calculating Gravitational Wave Signatures from Binary Black Hole Mergers
Calculations of the final merger stage of binary black hole evolution can
only be carried out using full scale numerical relativity simulations. This
article provides a general overview of these calculations, highlighting recent
progress and current challenges.Comment: 12 pages, to appear in "The Astrophysics of Gravitational Wave
Sources," Proceedings of a Workshop held at the University of Maryland in
April 2003, ed. J. Centrella, AIP, in press (2003
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