Spatio-temporal prior based kinetic model in dynamic PET reconstruction

2011 ◽  
Author(s):  
Zhang Qingping ◽  
Zhan Jie
Keyword(s):  
Kinetic Model ◽  
Dynamic Pet ◽  
Spatio Temporal ◽  
Pet Reconstruction

scholarly journals Dynamic PET Reconstruction on the GPU

2018 ◽  
Vol 62 (4) ◽  
pp. 134-143 ◽  
Author(s):  
László Szirmay-Kalos ◽  
Ágota Kacsó ◽  
Milán Magdics ◽  
Balázs Tóth
Keyword(s):  
Parallel Architecture ◽  
Concentration Function ◽  
Space Time ◽  
Massively Parallel ◽  
Graphics Processors ◽  
Gamma Photon ◽  
Likelihood Principle ◽  
Positron Emission ◽  
Spatio Temporal ◽  
Pet Reconstruction

Dynamic Positron Emission Tomography (PET) reconstructs the space-time concentration function of a radiotracer by observing the detector hits of gamma-photon pairs born during the radiotracer decay. The computation is based on the maximum likelihood principle, i.e. we look for the space-time function that maximizes the probability of the actual measurements. The number of finite elements representing the spatio-temporal concentration and the number of events detected by the tomograph may be higher than a billion, thus the reconstruction requires supercomputer performance. The enormous computational burden can be handled by graphics processors (GPU) if the algorithm is decomposed to parallel, independent threads, and the storage requirements are kept under control. This paper proposes a scalable dynamic reconstruction system where the algorithm is decomposed to phases where each phase is efficiently mapped onto the massively parallel architecture of the GPU.

Dynamic PET Reconstruction based on a Reaction-Diffusion Model

PAMM ◽  
2012 ◽  
Vol 12 (1) ◽  
pp. 683-684
Author(s):  
Louise Reips ◽  
Ralf Engbers ◽  
Martin Burger
Keyword(s):  
Diffusion Model ◽  
Reaction Diffusion ◽  
Dynamic Pet ◽  
Reaction Diffusion Model ◽  
Pet Reconstruction

FEASIBILITY OF THE NON-CODING RNA GRADIENTS IN CELLS

2009 ◽  
Vol 04 (03) ◽  
pp. 267-272 ◽  
Author(s):  
VLADIMIR P. ZHDANOV
Keyword(s):  
Kinetic Model ◽  
Gene Transcription ◽  
Eukaryotic Cells ◽  
Model Parameters ◽  
Protein Association ◽  
Non Coding Rna ◽  
A Cell ◽  
Non Coding Rnas ◽  
Spatio Temporal

In eukaryotic cells, the gene transcription often results in the formation of non-coding RNAs (ncRNAs). The key function of such RNAs is to bind to and modulate the activity of mRNAs and/or proteins. To scrutinize this ncRNA function in a cell, the author (i) proposes a spatio-temporal kinetic model, including ncRNA–protein association and degradation, (ii) derives a criterion of feasibility of the ncRNA gradients, and (iii) shows that this criterion can be satisfied with physically reasonable values of the model parameters. Thus, the ncRNA gradients are feasible. For the ncRNA–mRNA association and degradation, the situation is similar. The likely biological role of such gradients is open for debate.

Dynamic PET reconstruction using temporal patch-based low rank penalty for ROI-based brain kinetic analysis

2015 ◽  
Vol 60 (5) ◽  
pp. 2019-2046 ◽  
Author(s):  
Kyungsang Kim ◽  
Young Don Son ◽  
Yoram Bresler ◽  
Zang Hee Cho ◽  
Jong Beom Ra ◽  
...  
Keyword(s):  
Kinetic Analysis ◽  
Low Rank ◽  
Dynamic Pet ◽  
Pet Reconstruction
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