Optimal sparse solution for fluorescent diffuse optical tomography: theory and phantom experimental results

Mohajerani, Pouyan; Eftekhar, Ali A.; Huang, Jing-Song; Adibi, Ali

doi:10.1364/ao.46.001679

Cited by 69 publications

(49 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The incorporation of the L 1 norm as regularization functional, defined as (3.72) was studied for DDOT as well as for the case of fluorescence DOT in [Cao et al, 2007;Mohajerani et al, 2007]. For information regarding fluorescence DOT see Milstein et al [2003].…”

Section: Non-quadratic Penaltiesmentioning

confidence: 99%

Information theoretic regularization in diffuse optical tomography

Panagiotou¹,

Somayajula²,

Gibson³

et al. 2009

J. Opt. Soc. Am. A

View full text Add to dashboard Cite

Diffuse optical tomography (DOT) retrieves the spatially distributed optical characteristics of a medium from external measurements. Recovering these parameters of interest involves solving a non-linear and severely ill-posed inverse problem. In this thesis we propose methods towards the regularization of DOT via the introduction of spatially unregistered, a priori information from alternative high resolution anatomical modalities, using the information theory concepts of joint entropy (JE) and mutual information (MI). Such functionals evaluate the similarity between the reconstructed optical image and the prior image, while bypassing the multi-modality barrier manifested as the incommensurate relation between the gray value representations of corresponding anatomical features in the modalities involved. By introducing structural a priori information in the image reconstruction process, we aim to improve the spatial resolution and quantitative accuracy of the solution.A further condition for the accurate incorporation of a priori information is the establishment of correct alignment between the prior image and the probed anatomy in a common coordinate system. However, limited information regarding the probed anatomy is known prior to the reconstruction process.In this work we explore the potentiality of spatially registering the prior image simultaneously with the solution of the reconstruction process.We provide a thorough explanation of the theory from an imaging perspective, accompanied by preliminary results obtained by numerical simulations as well as experimental data. In addition we compare the performance of MI and JE. Finally, we propose a method for fast joint entropy evaluation and optimization, which we later employ for the information theoretic regularization of DOT. The main areas involved in this thesis are: inverse problems, image reconstruction & regularization, diffuse optical tomography and medical image registration. Statement of intellectual contributionI, Christos Panagiotou, declare that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been properly indicated in the thesis.

show abstract

Section: Non-quadratic Penaltiesmentioning

confidence: 99%

Information theoretic regularization in diffuse optical tomography

Panagiotou¹,

Somayajula²,

Gibson³

et al. 2009

J. Opt. Soc. Am. A

View full text Add to dashboard Cite

show abstract

“…In the reconstructed FMLT images, the true targets are submerged in strong background noises. For the cancerous tumors in the early stages, the targets labeled with the fluorescent probes are typically small, namely sparse [10]. So effective sparsity-constrained methods for FMLT reconstruction are of importance.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear greedy sparsity-constrained algorithm for direct reconstruction of fluorescence molecular lifetime tomography

Cai

Zhang

Cai

et al. 2016

Biomed. Opt. Express

View full text Add to dashboard Cite

Abstract:In order to improve the spatial resolution of time-domain (TD) fluorescence molecular lifetime tomography (FMLT), an accelerated nonlinear orthogonal matching pursuit (ANOMP) algorithm is proposed. As a kind of nonlinear greedy sparsity-constrained methods, ANOMP can find an approximate solution of L 0 minimization problem. ANOMP consists of two parts, i.e., the outer iterations and the inner iterations. Each outer iteration selects multiple elements to expand the support set of the inverse lifetime based on the gradients of a mismatch error. The inner iterations obtain an intermediate estimate based on the support set estimated in the outer iterations. The stopping criterion for the outer iterations is based on the stability of the maximum reconstructed values and is robust for problems with targets at different edge-to-edge distances (EEDs). Phantom experiments with two fluorophores at different EEDs and in vivo mouse experiments demonstrate that ANOMP can provide high quantification accuracy, even if the EED is relatively small, and high resolution.

show abstract

“…According to the CS theory, a sparse or compressive signal can be faithfully recovered from far fewer samples or measurements [12]. Considering that in the practical applications of FMT, the fluorescent sources are usually sparse because the fluorescent probes used in FMT are designed to locate the specific areas of interest such as tumors or cancerous tissues, which are usually small and sparse compared to the entire reconstruction domain [13]. Hence, the problem of FMT can be regarded as a sparse reconstruction problem and the fluorescent source distribution can be recovered by using sparse-type regularization (L0-norm and L1-norm) strategies.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the problem of FMT can be regarded as a sparse reconstruction problem and the fluorescent source distribution can be recovered by using sparse-type regularization (L0-norm and L1-norm) strategies. Inspired by the ideas behind the CS theory, several algorithms incorporated with L1-norm regularization have been proposed to solve the optical tomography problems in recent years [10,[13][14][15][16][17]. To preserve the sparsity of the fluorescent sources, an iteratively reweighted scheme based approach, which was able to obtain more reasonable and satisfactory results compared with the Tikhonov method was proposed [14].…”

Section: Introductionmentioning

confidence: 99%

Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method

Ye¹,

Chi²,

Xue³

et al. 2014

Biomed. Opt. Express

View full text Add to dashboard Cite

Fluorescence molecular tomography (FMT), as a promising imaging modality, can three-dimensionally locate the specific tumor position in small animals. However, it remains challenging for effective and robust reconstruction of fluorescent probe distribution in animals. In this paper, we present a novel method based on sparsity adaptive subspace pursuit (SASP) for FMT reconstruction. Some innovative strategies including subspace projection, the bottom-up sparsity adaptive approach, and backtracking technique are associated with the SASP method, which guarantees the accuracy, efficiency, and robustness for FMT reconstruction. Three numerical experiments based on a mouse-mimicking heterogeneous phantom have been performed to validate the feasibility of the SASP method. The results show that the proposed SASP method can achieve satisfactory source localization with a bias less than 1mm; the efficiency of the method is much faster than mainstream reconstruction methods; and this approach is robust even under quite ill-posed condition. Furthermore, we have applied this method to an in vivo mouse model, and the results demonstrate the feasibility of the practical FMT application with the SASP method.

show abstract

Optimal sparse solution for fluorescent diffuse optical tomography: theory and phantom experimental results

Cited by 69 publications

References 39 publications

Information theoretic regularization in diffuse optical tomography

Information theoretic regularization in diffuse optical tomography

Nonlinear greedy sparsity-constrained algorithm for direct reconstruction of fluorescence molecular lifetime tomography

Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method

Contact Info

Product

Resources

About