Name
Abstract | ||
|---|---|---|
We propose a new reconstruction procedure for X-ray computed tomography (CT) based on Bayesian modeling. We utilize the knowledge that the human body is composed of only a limited number of materials whose CT values are roughly known in advance. Although the exact Bayesian inference of our model is intractable, we propose an efficient algorithm based on the variational Bayes technique. Experiments show that the proposed method performs better than the existing methods in severe situations where samples are limited or metal is inserted into the body. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5495195 | Acoustics Speech and Signal Processing |
Keywords | Field | DocType |
belief networks,computerised tomography,diagnostic radiography,image reconstruction,inference mechanisms,medical image processing,Bayesian inference,Bayesian modeling,X-ray computed tomography,human body,material class knowledge,reconstruction procedure,variational Bayes technique,Computed tomography,image reconstruction,metal artifact reduction,variational Bayes | Iterative reconstruction,Bayesian inference,Pattern recognition,Biological materials,Computer science,Computed tomography,Reconstruction procedure,Artificial intelligence,Pixel,Bayesian probability,Bayes' theorem | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4244-4296-6 | 978-1-4244-4296-6 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wataru Fukuda | 1 | 0 | 0.34 |
| Shin-ichi Maeda | 2 | 26 | 8.11 |
| Atsunori Kanemura | 3 | 0 | 0.68 |
| Shin Ishii | 4 | 239 | 34.39 |