Data fitting and image fine-tuning approach to solve the inverse problem in fluorescence molecular imaging
(2008) Conference on Imaging, Manipulation and Analysis of Biomolecules, Cells, and Tissues VI, San Jose, CA, JAN 21-23, 2008 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2260228
- author
- Gorpas, Dimitris ; Politopoulos, Kostas ; Yova, Dido and Andersson-Engels, Stefan LU
- organization
- publishing date
- 2008
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- IMAGING, MANIPULATION, AND ANALYSIS OF BIOMOLECULES, CELLS, AND TISSUES VI
- editor
- Farkas, DL ; Nicolau, DV and Leif, RC
- volume
- 6859
- publisher
- SPIE
- conference name
- Conference on Imaging, Manipulation and Analysis of Biomolecules, Cells, and Tissues VI, San Jose, CA, JAN 21-23, 2008
- conference dates
- 0001-01-02
- external identifiers
-
- scopus:78650225884
- ISSN
- 1996-756X
- 0277-786X
- 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
- 2016-04-01 12:23:39
- date last changed
- 2024-01-08 18:59:09
@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 = {{1996-756X}},
language = {{eng}},
publisher = {{SPIE}},
title = {{Data fitting and image fine-tuning approach to solve the inverse problem in fluorescence molecular imaging}},
url = {{https://lup.lub.lu.se/search/files/2905315/2297222.pdf}},
doi = {{10.1117/12.762968}},
volume = {{6859}},
year = {{2008}},
}