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Data fitting and image fine-tuning approach to solve the inverse problem in fluorescence molecular imaging

Gorpas, Dimitris; Politopoulos, Kostas; Yova, Dido and Andersson-Engels, Stefan LU (2008) Conference on Imaging, Manipulation and Analysis of Biomolecules, Cells, and Tissues VI, San Jose, CA, JAN 21-23, 2008 In IMAGING, MANIPULATION, AND ANALYSIS OF BIOMOLECULES, CELLS, AND TISSUES VI 6859.
Abstract
One of the most challenging problems in medical imaging is to ``see{''} a tumour embedded into tissue, which is a turbid medium, by using fluorescent probes for tumour labeling. This problem, despite the efforts made during the last years, has not been fully encountered yet, due to the non-linear nature of the inverse problem and the convergence failures of many optimization techniques. This paper describes a robust solution of the inverse problem, based on data fitting and image fine-tuning techniques. As a forward solver the coupled radiative transfer equation and diffusion approximation model is proposed and compromised via a finite element method, enhanced with adaptive multi-grids for faster and more accurate convergence. A database... (More)
One of the most challenging problems in medical imaging is to ``see{''} a tumour embedded into tissue, which is a turbid medium, by using fluorescent probes for tumour labeling. This problem, despite the efforts made during the last years, has not been fully encountered yet, due to the non-linear nature of the inverse problem and the convergence failures of many optimization techniques. This paper describes a robust solution of the inverse problem, based on data fitting and image fine-tuning techniques. As a forward solver the coupled radiative transfer equation and diffusion approximation model is proposed and compromised via a finite element method, enhanced with adaptive multi-grids for faster and more accurate convergence. A database is constructed by application of the forward model on virtual tumours with known geometry, and thus fluorophore distribution, embedded into simulated tissues. The fitting procedure produces the best matching between the real and virtual data, and thus provides the initial estimation of the fluorophore distribution. Using this information, the coupled radiative transfer equation and diffusion approximation model has the required initial values for a computational reasonable and successful convergence during the image fine-tuning application. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
IMAGING, MANIPULATION, AND ANALYSIS OF BIOMOLECULES, CELLS, AND TISSUES VI
editor
Farkas, DL; Nicolau, DV; Leif, RC; ; and
volume
6859
publisher
The Society of Photo-optical Instrumentation Engineers (SPIE)
conference name
Conference on Imaging, Manipulation and Analysis of Biomolecules, Cells, and Tissues VI, San Jose, CA, JAN 21-23, 2008
external identifiers
  • scopus:78650225884
ISSN
0277-786X
1996-756X
ISBN
978-0-8194-7034-8
DOI
10.1117/12.762968
language
English
LU publication?
yes
id
da0a2287-a6b1-4f49-8475-5a484bb4fd29 (old id 2260228)
date added to LUP
2012-01-26 23:36:38
date last changed
2017-01-01 05:06:20
@inproceedings{da0a2287-a6b1-4f49-8475-5a484bb4fd29,
  abstract     = {One of the most challenging problems in medical imaging is to ``see{''} a tumour embedded into tissue, which is a turbid medium, by using fluorescent probes for tumour labeling. This problem, despite the efforts made during the last years, has not been fully encountered yet, due to the non-linear nature of the inverse problem and the convergence failures of many optimization techniques. This paper describes a robust solution of the inverse problem, based on data fitting and image fine-tuning techniques. As a forward solver the coupled radiative transfer equation and diffusion approximation model is proposed and compromised via a finite element method, enhanced with adaptive multi-grids for faster and more accurate convergence. A database is constructed by application of the forward model on virtual tumours with known geometry, and thus fluorophore distribution, embedded into simulated tissues. The fitting procedure produces the best matching between the real and virtual data, and thus provides the initial estimation of the fluorophore distribution. Using this information, the coupled radiative transfer equation and diffusion approximation model has the required initial values for a computational reasonable and successful convergence during the image fine-tuning application.},
  author       = {Gorpas, Dimitris and Politopoulos, Kostas and Yova, Dido and Andersson-Engels, Stefan},
  booktitle    = {IMAGING, MANIPULATION, AND ANALYSIS OF BIOMOLECULES, CELLS, AND TISSUES VI},
  editor       = {Farkas, DL and Nicolau, DV and Leif, RC},
  isbn         = {978-0-8194-7034-8},
  issn         = {0277-786X},
  language     = {eng},
  publisher    = {The Society of Photo-optical Instrumentation Engineers (SPIE)},
  title        = {Data fitting and image fine-tuning approach to solve the inverse problem in fluorescence molecular imaging},
  url          = {http://dx.doi.org/10.1117/12.762968},
  volume       = {6859},
  year         = {2008},
}