| pubmed-article:19770900 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:19770900 | lifeskim:mentions | umls-concept:C0162404 | lld:lifeskim |
| pubmed-article:19770900 | lifeskim:mentions | umls-concept:C0040395 | lld:lifeskim |
| pubmed-article:19770900 | lifeskim:mentions | umls-concept:C0449445 | lld:lifeskim |
| pubmed-article:19770900 | lifeskim:mentions | umls-concept:C1705938 | lld:lifeskim |
| pubmed-article:19770900 | lifeskim:mentions | umls-concept:C0450363 | lld:lifeskim |
| pubmed-article:19770900 | lifeskim:mentions | umls-concept:C1527178 | lld:lifeskim |
| pubmed-article:19770900 | pubmed:issue | 19 | lld:pubmed |
| pubmed-article:19770900 | pubmed:dateCreated | 2009-9-22 | lld:pubmed |
| pubmed-article:19770900 | pubmed:abstractText | Bioluminescence tomography (BLT) poses a typical ill-posed inverse problem with a large number of unknowns and a relatively limited number of boundary measurements. It is indispensable to incorporate a priori information into the inverse problem formulation in order to obtain viable solutions. In the paper, Bayesian approach has been firstly suggested to incorporate multiple types of a priori information for BLT reconstruction. Meanwhile, a generalized adaptive Gaussian Markov random field (GAGMRF) prior model for unknown source density estimation is developed to further reduce the ill-posedness of BLT on the basis of finite element analysis. Then the distribution of bioluminescent source can be acquired by maximizing the log posterior probability with respect to a noise parameter and the unknown source density. Furthermore, the use of finite element method makes the algorithm appropriate for complex heterogeneous phantom. The algorithm was validated by numerical simulation of a 3-D micro-CT mouse atlas and physical phantom experiment. The reconstructed results suggest that we are able to achieve high computational efficiency and accurate localization of bioluminescent source. | lld:pubmed |
| pubmed-article:19770900 | pubmed:language | eng | lld:pubmed |
| pubmed-article:19770900 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19770900 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:19770900 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:19770900 | pubmed:month | Sep | lld:pubmed |
| pubmed-article:19770900 | pubmed:issn | 1094-4087 | lld:pubmed |
| pubmed-article:19770900 | pubmed:author | pubmed-author:ZhangXingX | lld:pubmed |
| pubmed-article:19770900 | pubmed:author | pubmed-author:TianJieJ | lld:pubmed |
| pubmed-article:19770900 | pubmed:author | pubmed-author:LiuJuntingJ | lld:pubmed |
| pubmed-article:19770900 | pubmed:author | pubmed-author:FengJinchaoJ | lld:pubmed |
| pubmed-article:19770900 | pubmed:author | pubmed-author:QinChenghuC | lld:pubmed |
| pubmed-article:19770900 | pubmed:author | pubmed-author:JiaKebinK | lld:pubmed |
| pubmed-article:19770900 | pubmed:author | pubmed-author:YanGuoruiG | lld:pubmed |
| pubmed-article:19770900 | pubmed:author | pubmed-author:ZhuShoupingS | lld:pubmed |
| pubmed-article:19770900 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:19770900 | pubmed:day | 14 | lld:pubmed |
| pubmed-article:19770900 | pubmed:volume | 17 | lld:pubmed |
| pubmed-article:19770900 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:19770900 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:19770900 | pubmed:pagination | 16834-48 | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:meshHeading | pubmed-meshheading:19770900... | lld:pubmed |
| pubmed-article:19770900 | pubmed:year | 2009 | lld:pubmed |
| pubmed-article:19770900 | pubmed:articleTitle | Three-dimensional bioluminescence tomography based on Bayesian approach. | lld:pubmed |
| pubmed-article:19770900 | pubmed:affiliation | The College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100190, China. | lld:pubmed |
| pubmed-article:19770900 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:19770900 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:19770900 | lld:pubmed |
