Multilevel bioluminescence tomography based on radiative transfer equation Part 1: l1 regularization

Gao, Hao; Zhao, Hongkai

doi:10.1364/oe.18.001854

Cited by 101 publications

(84 citation statements)

References 51 publications

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“…[1][2][3][4] It has found a wide range of biomedical applications in drugs development and therapies evaluation for cancer researches. [5][6][7][8] To reconstruct three-dimensional distributions of sources from the collected boundary measurements, both a forward model for simulating light transport and an elaborate inverse algorithm are required. Given a model describing light transport in tissue and the optical properties of the object to be imaged, the key problem of inverse algorithm is to deal with the severe ill-posedness of BLT and hence to produce e±cient and stable reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Source reconstruction for bioluminescence tomography via L1∕2 regularization

2018

J. Innov. Opt. Health Sci.

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Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L 1 -regularized methods have been investigated due to the sparsity-inducing properties of L 1 norm. In this paper, we present a reconstruction method based on L 1=2 regularization to enhance sparsity of BLT solution and solve the nonconvex L 1=2 norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under di®erent noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.

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Section: Introductionmentioning

confidence: 99%

Source reconstruction for bioluminescence tomography via L1∕2 regularization

2018

J. Innov. Opt. Health Sci.

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“…Apart from the a priori information, when the TR method is employed to solve the optimization problem, an optimal can be another method to improve the robustness. It was reported that 1 L regularization was more suitable than 2 L regularization [35,36]. These early reconstruction approaches were consistently committed to control errors to obtain a unique reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Probability method for Cerenkov luminescence tomography based on conformance error minimization

Ding¹,

Wang²,

Jie³

et al. 2014

Biomed. Opt. Express

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Cerenkov luminescence tomography (CLT) was developed to reconstruct a three-dimensional (3D) distribution of radioactive probes inside a living animal. Reconstruction methods are generally performed within a unique framework by searching for the optimum solution. However, the ill-posed aspect of the inverse problem usually results in the reconstruction being non-robust. In addition, the reconstructed result may not match reality since the difference between the highest and lowest uptakes of the resulting radiotracers may be considerably large, therefore the biological significance is lost. In this paper, based on the minimization of a conformance error, a probability method is proposed that consists of qualitative and quantitative modules. The proposed method first pinpoints the organ that contains the light source. Next, we developed a 0-1 linear optimization subject to a space constraint to model the CLT inverse problem, which was transformed into a forward problem by employing a region growing method to solve the optimization. After running through all of the elements used to grow the sources, a source sequence was obtained. Finally, the probability of each discrete node being the light source inside the organ was reconstructed. One numerical study and two in vivo experiments were conducted to verify the performance of the proposed algorithm, and comparisons were carried out using the hp-finite element method (hp-FEM). The results suggested that our proposed probability method was more robust and reasonable than hp-FEM.

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“…In addition, it has been shown that angular dependent data leads to improvements in the image reconstruction compared with angular averaged data [14]. Nonetheless, current small animal imaging systems have not yet seen any success with the collection of angular dependent measurement data.…”

Section: Introductionmentioning

confidence: 99%

Non-contact small animal fluorescence imaging system for simultaneous multi-directional angular-dependent data acquisition

Lee¹,

Kim²,

Chandhanayingyong³

et al. 2014

Biomed. Opt. Express

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Abstract:We present a novel non-contact small animal fluorescent molecular tomography (FMT) imaging system. At the heart of the system is a new mirror-based imaging head that was designed to provide 360-degree measurement data from an entire animal surface in one step. This imaging head consists of two conical mirrors, which considerably reduce multiple back reflections between the animal and mirror surfaces. These back reflections are common in existing mirror-based imaging heads and tend to degrade the quality of raw measurement data. In addition, the introduction of a novel ray-transfer operator allows for the inclusion of the angular dependent data in the image reconstruction process, which results in higher image resolution. We describe in detail the system design and implementation of the hardware components as well as the transport-theorybased image reconstruction algorithm. Using numerical simulations, measurements on a well-defined phantom and a live animal, we evaluate the system performance and show the advantages of our approach.

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Multilevel bioluminescence tomography based on radiative transfer equation Part 1: l1 regularization

Cited by 101 publications

References 51 publications

Source reconstruction for bioluminescence tomography via L1∕2 regularization

Source reconstruction for bioluminescence tomography via L1∕2 regularization

Probability method for Cerenkov luminescence tomography based on conformance error minimization

Non-contact small animal fluorescence imaging system for simultaneous multi-directional angular-dependent data acquisition

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