An Efficient Numerical Method for General $L_{p}$ Regularization in Fluorescence Molecular Tomography

Baritaux, Jean-Charles; Hassler, Kai; Unser, Michaël

doi:10.1109/tmi.2010.2042814

Cited by 57 publications

(46 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sparsity promoting effects of L p regularization for a more general 1 ≤ p < 2 has been investigated as well (Han et al 2010). A matrix-free strategy using L p (1 ≤ p < 2) regularization was also proposed for FMT (Baritaux et al 2010). These papers clearly demonstrated the effects of L p (1 ≤ p < 2) regularizations in enhancing sparsity.…”

Section: Introductionmentioning

confidence: 97%

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

Zhu

Li²

2014

Phys. Med. Biol.

View full text Add to dashboard Cite

Abstract. In vivo fluorescence imaging has been a popular functional imaging modality in preclinical imaging. Near infrared probes used in fluorescence molecular tomography (FMT) are designed to localize in the targeted tissues, hence sparse solution to the FMT image reconstruction problem is preferred. Nonconvex regularization methods are reported to enhance sparsity in the fields of statistical learning, compressed sensing etc. We investigated such regularization methods in FMT for small animal imaging with numerical simulations and phantom experiments. We adopted a majorization-minimization (MM) algorithm for the iterative reconstruction process and compared the reconstructed images using our proposed nonconvex regularizations with those using the well known L 1 regularization. We found that the proposed nonconvex methods outperform L 1 regularization in accurately recovering sparse targets in FMT.

show abstract

Section: Introductionmentioning

confidence: 97%

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

Zhu

Li²

2014

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…Given a region of interest (ROI) where the fluorophore is confined, the CNR is defined by (20) where and are the mean concentration values in the ROI and background, respectively, and are the variances, and and are the relative volumes of ROI and background. The CNR measures how well features of interest are rendered by the reconstruction [27], [38], [39]. The observations made on Fig.…”

Section: B Experiments 2: Three Dimensions Experimental Data Two Inmentioning

confidence: 98%

“…We refer the reader to [24] for additional details on sparsity, compressive sampling, and related algorithms. These techniques have been considered in the context of optical diffuse imaging in [25]- [27], and lead to resolution improvement. In this contribution, the emphasis is put on group-sparsity, i.e., sparsity between groups of coefficients, rather than sparsity of the coefficients themselves.…”

mentioning

confidence: 99%

Sparsity-Driven Reconstruction for FDOT With Anatomical Priors

Baritaux

Hassler

Bucher

et al. 2011

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Abstract-In this paper we propose a method based on (2, 1)-mixed-norm penalization for incorporating a structural prior in FDOT image reconstruction. The effect of (2, 1)-mixed-norm penalization is twofold: first, a sparsifying effect which isolates few anatomical regions where the fluorescent probe has accumulated, and second, a regularization effect inside the selected anatomical regions. After formulating the reconstruction in a variational framework, we analyze the resulting optimization problem and derive a practical numerical method tailored to (2, 1)-mixed-norm regularization. The proposed method includes as particular cases other sparsity promoting regularization methods such as -norm penalization and total variation penalization. Results on synthetic and experimental data are presented.

show abstract

“…Consequently, noise and errors in the FT data and modeling can produce significant artifacts in the 3D reconstructions. FT inverse solvers utilize regularization techniques to provide robustness and stability against noise and errors [5][6][7][8][9]. However, as the level of noise and error contamination rises, the quality of regularized reconstructions deteriorates.…”

Section: Introductionmentioning

confidence: 99%

Hadamard multiplexed fluorescence tomography

Behrooz

Eftekhar

Adibi

2014

Biomed. Opt. Express

View full text Add to dashboard Cite

Depth-resolved three-dimensional (3D) reconstruction of fluorophore-tagged inclusions in fluorescence tomography (FT) poses a highly ill-conditioned problem as depth information must be extracted from boundary data. Due to the ill-posed nature of the FT inverse problem, noise and errors in the data can severely impair the accuracy of the 3D reconstructions. The signal-to-noise ratio (SNR) of the FT data strongly affects the quality of the reconstructions. Additionally, in FT scenarios where the fluorescent signal is weak, data acquisition requires lengthy integration times that result in excessive FT scan periods. Enhancing the SNR of FT data contributes to the robustness of the 3D reconstructions as well as the speed of FT scans. A major deciding factor in the SNR of the FT data is the power of the radiation illuminating the subject to excite the administered fluorescent reagents. In existing single-point illumination FT systems, the source power level is limited by the skin maximum radiation exposure levels. In this paper, we introduce and study the performance of a multiplexed fluorescence tomography system with orders-of-magnitude enhanced data SNR over existing systems. The proposed system allows for multi-point illumination of the subject without jeopardizing the information content of the FT measurements and results in highly robust reconstructions of fluorescent inclusions from noisy FT data. Improvements offered by the proposed system are validated by numerical and experimental studies. tomography of sub-surface heterogeneities using spatially modulated structured light," Opt.

show abstract

An Efficient Numerical Method for General $L_{p}$ Regularization in Fluorescence Molecular Tomography

Cited by 57 publications

References 40 publications

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement

Sparsity-Driven Reconstruction for FDOT With Anatomical Priors

Hadamard multiplexed fluorescence tomography

Contact Info

Product

Resources

About