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

Gorpas, D; Politopoulos, K; Yova, D; Andersson-Engels, S

dc.contributor.author	Gorpas, D	en
dc.contributor.author	Politopoulos, K	en
dc.contributor.author	Yova, D	en
dc.contributor.author	Andersson-Engels, S	en
dc.date.accessioned	2014-03-01T02:51:35Z
dc.date.available	2014-03-01T02:51:35Z
dc.date.issued	2008	en
dc.identifier.issn	16057422	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35575
dc.subject	diffusion approximation	en
dc.subject	finite elements method	en
dc.subject	fluorescence image registration	en
dc.subject	Fluorescence molecular imaging	en
dc.subject	image fine-tuning	en
dc.subject	radiative transfer equation	en
dc.subject.other	Diffusion approximations	en
dc.subject.other	finite elements method	en
dc.subject.other	fluorescence image registration	en
dc.subject.other	Fluorescence molecular	en
dc.subject.other	image fine-tuning	en
dc.subject.other	radiative transfer equation	en
dc.subject.other	Biomolecules	en
dc.subject.other	Computer simulation	en
dc.subject.other	Coupled circuits	en
dc.subject.other	Data handling	en
dc.subject.other	Diffusion	en
dc.subject.other	Finite element method	en
dc.subject.other	Fluorophores	en
dc.subject.other	Heat radiation	en
dc.subject.other	Histology	en
dc.subject.other	Image registration	en
dc.subject.other	Inverse problems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Medical problems	en
dc.subject.other	Radiative transfer	en
dc.subject.other	Tumors	en
dc.subject.other	Medical imaging	en
dc.title	Data fitting and image fine-tuning approach to solve the inverse problem in fluorescence molecular imaging	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1117/12.762968	en
heal.identifier.secondary	http://dx.doi.org/10.1117/12.762968	en
heal.identifier.secondary	68591H	en
heal.publicationDate	2008	en
heal.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. © 2008 Copyright SPIE - The International Society for Optical Engineering.	en
heal.journalName	Progress in Biomedical Optics and Imaging - Proceedings of SPIE	en
dc.identifier.doi	10.1117/12.762968	en
dc.identifier.volume	6859	en