Paper Info
Title
Progress In Mesh Based Spatio-Temporal Reconstruction
Abstract
In this paper, we present a new methodology for calculation of a 2D projection operator for emission tomography using a content-adaptive mesh model (CAMM). A CAMM is an efficient image representation based on adaptive sampling and linear interpolation, wherein non-uniform image samples are placed most densely in regions having fine detail. We have studied CAMM in recent years and shown that a CAMM is an efficient too] for data representation and tomographic reconstruction. In addition, it can also provide a unified framework for tomographic reconstruction of organs (e.g., the heart) that undergo non-rigid deformation. In this work we develop a projection operator model suitable for a CAMM representation such that it accounts for several major degradation factors in data acquisition, namely object attenuation and depth-dependent blur in detector-collimator response. The projection operator is calculated using a ray-tracing algorithm. We tested the developed projection operator by using Monte Carlo simulation for single photon emission tomography (SPECT). The methodology presented here can also be extended to transmission tomography.
Year
DOI
Venue
2008
10.1117/12.776951
COMPUTATIONAL IMAGING VI
Keywords
Field
DocType
monte carlo simulation,projection operator,tomographic reconstruction,linear interpolation,data representation,heart,sensors,data visualization,tomography,ray tracing,data acquisition
Iterative reconstruction,Computer vision,Tomographic reconstruction,External Data Representation,Projection (linear algebra),Tomography,Projection method,Artificial intelligence,Linear interpolation,Point cloud,Mathematics
Conference
Volume
ISSN
Citations 
6814
0277-786X
0
PageRank 
References 
Authors
0.34
7
5
Name
Order
Citations
PageRank
Jovan G. Brankov18212.09
Ricard Delgado-Gonzalo29913.43
Yongyi Yang31409140.74
Mingwu Jin4286.92
Miles N. Wernick559561.13