Bayesian Image Reconstruction in Quantitative Photoacoustic Tomography

2013 ◽  
Vol 32 (12) ◽  
pp. 2287-2298 ◽  
Author(s):  
Tanja Tarvainen ◽  
Aki Pulkkinen ◽  
Ben T. Cox ◽  
Jari P. Kaipio ◽  
Simon R. Arridge
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Tomography ◽  
Bayesian Image Reconstruction

High-resolution 3D Bayesian image reconstruction using the microPET small-animal scanner

1998 ◽  
Vol 43 (4) ◽  
pp. 1001-1013 ◽  
Author(s):  
Jinyi Qi ◽  
Richard M Leahy ◽  
Simon R Cherry ◽  
Arion Chatziioannou ◽  
Thomas H Farquhar
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Small Animal ◽  
Bayesian Image Reconstruction

Half-Time Image Reconstruction in Photoacoustic Tomography

2017 ◽  
pp. 155-164
Author(s):  
Mark A. Anastasio ◽  
Jin Zhang ◽  
Xiaochuan Pan ◽  
Lihong V. Wang
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Tomography ◽  
Half Time ◽  
Time Image

Multispectral Bayesian reconstruction technique for real-time two color fluorescence microscopy

RSC Advances ◽  
2015 ◽  
Vol 5 (17) ◽  
pp. 13175-13183 ◽  
Author(s):  
Shilpa Dilipkumar ◽  
Ravi Manjithaya ◽  
Partha Pratim Mondal
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Real Time ◽  
Spectral Imaging ◽  
Reconstruction Technique ◽  
Imaging Method ◽  
Combined Approach ◽  
Bayesian Reconstruction ◽  
Image Reconstruction Technique ◽  
Bayesian Image Reconstruction

We have developed a real-time imaging method for two-color widefield fluorescence microscopy using a combined approach that integrates multi-spectral imaging and Bayesian image reconstruction technique.

An ergodic L 2-theorem for simulated annealing in bayesian image reconstruction

1990 ◽  
Vol 27 (04) ◽  
pp. 779-791 ◽  
Author(s):  
Gerhard Winkler
Keyword(s):  
Simulated Annealing ◽  
Image Reconstruction ◽  
Markov Chains ◽  
Bayesian Image Reconstruction ◽  
Stochastic Relaxation

An ergodic L 2-theorem for inhomogeneous Markov chains covering simulated annealing with or without constraints and stochastic relaxation with or without constraints arising in Bayesian image reconstruction is proved. The derivation is self-contained.

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