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Statistical modeling and structured ...

Northeastern University., Electrical and Computer Engineering.

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Statistical modeling and structured regularization for fluorescence molecular tomography.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical modeling and structured regularization for fluorescence molecular tomography./
Author:	Hyde, Damon Eliot.
Description:	184 p.
Notes:	Advisers: Dana H. Brooks; Eric L. Miller.
Contained By:	Dissertation Abstracts International69-12B.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3339256
ISBN:	9780549950301

Statistical modeling and structured regularization for fluorescence molecular tomography.
Hyde, Damon Eliot.

Statistical modeling and structured regularization for fluorescence molecular tomography. - 184 p.

Advisers: Dana H. Brooks; Eric L. Miller.

Thesis (Ph.D.)--Northeastern University, 2009.

Fluorescence molecular tomography (FMT) is an optical imaging technique that uses near infrared light to localize and quantify in vivo distributions of fluorescent probes targeting biochemical markers such as genes, proteins, and enzymes. In this thesis, we examine three aspects of the FMT reconstruction problem: statistical data modeling in the context of normalized fluorescence imaging, methods for the use of prior structural information arising from multi-modal FMT-CT imaging, and techniques to compensate for errors in that prior information. We derive a probabilistic model for normalized fluorescence data and use this model as the basis for reconstruction. This eliminates errors and human biases introduced by manual data thresholding and is shown to yield improved reconstructions with greater consistency. To improve upon the resolution limits of standalone FMT, we examine modeling and regularization that incorporates structural prior information available from data acquired by a complementary imaging modality such as CT or MRI. We show that improved diffusion forward models using average tissue optical properties can subsequently result in improved reconstructions. A two step inversion approach is then presented, using the solution to an anatomically defined low dimensional problem as the basis of a spatially varying regularization term for the full resolution problem. Results are presented for both simulated and in vivo data, in the context of imaging a mouse model of Alzheimer's disease. Such diffuse targets are difficult to reconstruct with stand alone approaches, thus highlighting the utility of the multimodal approach. Results are correlated with post mortem fluorescence measurements, and show a high degree of correlation between reconstruction intensity and observed fluorescence. Finally, two methods are presented to address situations where the prior information and underlying fluorescence share similar, but not identical, structure. The first uses differential equations to derive a Gaussian prior model for the fluorescence image. The incorporation of boundary conditions between anatomical regions allows information to cross their boundaries, and can help to compensate for boundary misplacement in the prior. The second approach uses the sparsity inducing properties of 1-norm minimization to localize the boundary within an uncertainty region around its initial position. Both approaches are tested using a range of 2-D simulated experiments.

ISBN: 9780549950301Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Statistical modeling and structured regularization for fluorescence molecular tomography.
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100 1 $a Hyde, Damon Eliot. $3 1058316
245 1 0 $a Statistical modeling and structured regularization for fluorescence molecular tomography.
300 $a 184 p.
500 $a Advisers: Dana H. Brooks; Eric L. Miller.
500 $a Source: Dissertation Abstracts International, Volume: 69-12, Section: B, page: 7703.
502 $a Thesis (Ph.D.)--Northeastern University, 2009.
520 $a Fluorescence molecular tomography (FMT) is an optical imaging technique that uses near infrared light to localize and quantify in vivo distributions of fluorescent probes targeting biochemical markers such as genes, proteins, and enzymes. In this thesis, we examine three aspects of the FMT reconstruction problem: statistical data modeling in the context of normalized fluorescence imaging, methods for the use of prior structural information arising from multi-modal FMT-CT imaging, and techniques to compensate for errors in that prior information. We derive a probabilistic model for normalized fluorescence data and use this model as the basis for reconstruction. This eliminates errors and human biases introduced by manual data thresholding and is shown to yield improved reconstructions with greater consistency. To improve upon the resolution limits of standalone FMT, we examine modeling and regularization that incorporates structural prior information available from data acquired by a complementary imaging modality such as CT or MRI. We show that improved diffusion forward models using average tissue optical properties can subsequently result in improved reconstructions. A two step inversion approach is then presented, using the solution to an anatomically defined low dimensional problem as the basis of a spatially varying regularization term for the full resolution problem. Results are presented for both simulated and in vivo data, in the context of imaging a mouse model of Alzheimer's disease. Such diffuse targets are difficult to reconstruct with stand alone approaches, thus highlighting the utility of the multimodal approach. Results are correlated with post mortem fluorescence measurements, and show a high degree of correlation between reconstruction intensity and observed fluorescence. Finally, two methods are presented to address situations where the prior information and underlying fluorescence share similar, but not identical, structure. The first uses differential equations to derive a Gaussian prior model for the fluorescence image. The incorporation of boundary conditions between anatomical regions allows information to cross their boundaries, and can help to compensate for boundary misplacement in the prior. The second approach uses the sparsity inducing properties of 1-norm minimization to localize the boundary within an uncertainty region around its initial position. Both approaches are tested using a range of 2-D simulated experiments.
590 $a School code: 0160.
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710 2 $a Northeastern University. $b Electrical and Computer Engineering. $3 1018491
773 0 $t Dissertation Abstracts International $g 69-12B.
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790 1 0 $a Brooks, Dana H., $e advisor
790 1 0 $a Miller, Eric L., $e advisor
790 1 0 $a Niedre, Mark $e committee member
790 1 0 $a Ntziachristos, Vasilis $e committee member
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3339256