Paper Info
Title
Statistical reconstruction using dual formulation of subband-wise total variation regularization (SDST) for limited angle tomography
Abstract
In this paper, a novel reconstruction algorithm for limited angle tomography using total variation (TV) regularization is presented. Inspired by duality-based TV minimization in denoising and deblurring applications, we derived a TV regularized statistical reconstruction algorithm composed of relatively simple and structured operations such as discrete gradient and divergence calculations, which presents an effective way to introduce TV regularization to the statistical reconstruction. In initial tests with real data from a digital breast tomosynthesis system, the proposed algorithm showed reliable reconstructions for low dose conditions.
Year
DOI
Venue
2011
10.1109/ISBI.2011.5872747
ISBI
Keywords
Field
DocType
gynaecology,digital breast tomosynthesis system,diagnostic radiography,computerised tomography,divergence calculation,limited angle tomography,discrete gradient,statistical analysis,x-ray radiographic tomography,total variation,data analysis,image denoising,image restoration,x-ray detection,image deblurring,tomosynthesis,statistical reconstruction,subband-wise total variation regularization,dual formulation,biological organs,x-ray detector,medical image processing,parallel computation,tv,image reconstruction,x ray detector,parallel computer,total variation regularization,minimization
Noise reduction,Iterative reconstruction,Computer vision,Tomosynthesis,Deblurring,Pattern recognition,Computer science,Regularization (mathematics),Total variation denoising,Reconstruction algorithm,Artificial intelligence,Image restoration
Conference
ISSN
ISBN
Citations 
1945-7928 E-ISBN : 978-1-4244-4128-0
978-1-4244-4128-0
0
PageRank 
References 
Authors
0.34
2
5
Name
Order
Citations
PageRank
Kwang Eun Jang1856.57
Younghun Sung293.05
Kangeui Lee301.01
Jong-Ha Lee4626.51
Seungryong Cho5145.34