Reconstruction for Limited-Projection Fluorescence Molecular Tomography Based on a Double-Mesh Strategy

Yi, Huangjian; Zhang, Xu; Peng, Jinye; Zhao, Fengjun; Wang, Xiaodong; Hou, Yi; Chen, Duofang; He, Xiaowei

doi:10.1155/2016/5682851

Cited by 7 publications

(6 citation statements)

References 44 publications

(48 reference statements)

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“…The experiment codes were written in MATLAB and all these processes were executed on a desktop computer with 3.20 GHz Intel Processor i7-8700 CPU and 16 GB RAM. To quantify the reconstruction performance, location error (LE), normalized root-mean-square error (NRMSE), and contrast-to-noise ratio (CNR) were adopted in this study 31 , 32 . The detailed formula descriptions can be found in the literature 32 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…To quantify the reconstruction performance, location error (LE), normalized root-mean-square error (NRMSE), and contrast-to-noise ratio (CNR) were adopted in this study. 31 , 32 The detailed formula descriptions can be found in the literature. 32 In general, a high-quality reconstructed image possesses LE, NRMSE, close to 0 and a high CNR value.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Neighbor-based adaptive sparsity orthogonal least square for fluorescence molecular tomography

Yang

et al. 2023

J. Biomed. Opt.

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. Significance Fluorescence molecular tomography (FMT) is a promising imaging modality, which has played a key role in disease progression and treatment response. However, the quality of FMT reconstruction is limited by the strong scattering and inadequate surface measurements, which makes it a highly ill-posed problem. Improving the quality of FMT reconstruction is crucial to meet the actual clinical application requirements. Aim We propose an algorithm, neighbor-based adaptive sparsity orthogonal least square (NASOLS), to improve the quality of FMT reconstruction. Approach The proposed NASOLS does not require sparsity prior information and is designed to efficiently establish a support set using a neighbor expansion strategy based on the orthogonal least squares algorithm. The performance of the algorithm was tested through numerical simulations, physical phantom experiments, and small animal experiments. Results The results of the experiments demonstrated that the NASOLS significantly improves the reconstruction of images according to indicators, especially for double-target reconstruction. Conclusion NASOLS can recover the fluorescence target with a good location error according to simulation experiments, phantom experiments and small mice experiments. This method is suitable for sparsity target reconstruction, and it would be applied to early detection of tumors.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Neighbor-based adaptive sparsity orthogonal least square for fluorescence molecular tomography

Yang

et al. 2023

J. Biomed. Opt.

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“…Subsequently, fluorescent photons emitted from fluorochromes propagate to detectors in the emission process. Each process can be modeled by the diffusion equation with Robin-type boundary condition as follows [ 35 – 39 ]:

where r denotes the position, Φ is the photon density, Q is the source term, and μ a and D are the absorption and diffusion coefficients, respectively. The diffusion coefficient D is defined as D = 1/[3( μ a + μ s ′)], where μ s ′ is the reduced scattering coefficient.…”

Section: Methodsmentioning

confidence: 99%

Image Restoration for Fluorescence Planar Imaging with Diffusion Model

Zhang

Gong

et al. 2017

BioMed Research International

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Fluorescence planar imaging (FPI) is failure to capture high resolution images of deep fluorochromes due to photon diffusion. This paper presents an image restoration method to deal with this kind of blurring. The scheme of this method is conceived based on a reconstruction method in fluorescence molecular tomography (FMT) with diffusion model. A new unknown parameter is defined through introducing the first mean value theorem for definite integrals. System matrix converting this unknown parameter to the blurry image is constructed with the elements of depth conversion matrices related to a chosen plane named focal plane. Results of phantom and mouse experiments show that the proposed method is capable of reducing the blurring of FPI image caused by photon diffusion when the depth of focal plane is chosen within a proper interval around the true depth of fluorochrome. This method will be helpful to the estimation of the size of deep fluorochrome.

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“…Another effective strategy is to obtain more prior knowledge by extracting feasible regions of the fluorescent targets and fusing the structural a priori information to improve the reconstruction performance of FMT. To tackle the problem that the mesh density affects the computational accuracy when solving the DE model with the FEM, Yi et al (2016) proposed a dual-mesh feasible region extraction strategy based on limited projections. In such a method, preliminary reconstruction results are obtained by using the coarse mesh first.…”

Section: Strategies For Combining Prior Knowledgementioning

confidence: 99%

A review of advances in imaging methodology in fluorescence molecular tomography

Zhang

Song

et al. 2022

Phys. Med. Biol.

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Objective. Fluorescence molecular tomography (FMT), as an encouraging and non-invasive optical molecular imaging technology with strong specificity and sensitivity, has great potential for preclinical and clinical studies in tumor diagnosis, drug development, and therapeutic evaluation. However, the strong scattering of photons and the insufficient surface measurements make it very challenging to improve the quality of FMT reconstruction and practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of obtaining high-quality FMT reconstructions. Approach. This review takes a comprehensive overview of imaging methodology advances of FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the FMT reconstruction quality are summarized. Notably, the deep learning methods have been elaborately discussed to illustrate the advantages in promoting the imaging performance of FMT owing to the practicality of large datasets, the emergence of optimized algorithms, and the applications of innovative networks. Main results. The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combining with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and the deep neural network-based methods, especially the end-to-end deep network, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction. Significance. This review aims at illustrating a variety of effective and practical methods for FMT image reconstruction, from which future research may benefit. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote the development of FMT and other optical tomography.

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Reconstruction for Limited-Projection Fluorescence Molecular Tomography Based on a Double-Mesh Strategy

Cited by 7 publications

References 44 publications

Neighbor-based adaptive sparsity orthogonal least square for fluorescence molecular tomography

Neighbor-based adaptive sparsity orthogonal least square for fluorescence molecular tomography

Image Restoration for Fluorescence Planar Imaging with Diffusion Model

A review of advances in imaging methodology in fluorescence molecular tomography

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