SU-E-I-29: A Prior Information Based Total-Variation Digital Tomosynthesis Reconstruction Algorithm

2011 ◽  
Vol 38 (6Part4) ◽  
pp. 3402-3402
Author(s):  
Y Jian ◽  
J Cai ◽  
F Yin
Keyword(s):  
Total Variation ◽  
Prior Information ◽  
Reconstruction Algorithm ◽  
Digital Tomosynthesis

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

Development of a total variation noise reduction algorithm for chest digital tomosynthesis

Optik ◽  
2019 ◽  
Vol 176 ◽  
pp. 384-393 ◽  
Author(s):  
In-Hyung Lee ◽  
Dae-Ung Kang ◽  
Sung-Wook Shin ◽  
Ryun-Gyeong Lee ◽  
Jung-Kyun Park ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Total Variation ◽  
Digital Tomosynthesis ◽  
Reduction Algorithm ◽  
Noise Reduction Algorithm

scholarly journals Distributed Reconstruction via Alternating Direction Method

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Linyuan Wang ◽  
Ailong Cai ◽  
Hanming Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Considerable Research ◽  
Alternating Direction ◽  
Ct Image Reconstruction

With the development of compressive sensing theory, image reconstruction from few-view projections has received considerable research attentions in the field of computed tomography (CT). Total-variation- (TV-) based CT image reconstruction has been shown to be experimentally capable of producing accurate reconstructions from sparse-view data. In this study, a distributed reconstruction algorithm based on TV minimization has been developed. This algorithm is very simple as it uses the alternating direction method. The proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm without losing accuracy.

Sign in / Sign up

Export Citation Format

Share Document