MSCN-NET: Multi-stage cascade neural network based on attention mechanism for Čerenkov luminescence tomography

du, mengfei; Chen, Yi; Li, Weitong; Su, Linzhi; Yi, Huangjian; Zhao, Fengjun; Li, Kang; Wang, Lin; Cao, Xin

doi:10.1063/5.0119787

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…In addition to traditional methods, deep learning strategies have been introduced to FMT reconstruction in recent years (Gao et al 2018, Guo et al 2018, Guo et al 2019, Du et al 2022. For instance, Meng et al presented a novel K-nearest neighbor based locally connected (KNN-LC) network for FMT reconstruction in 2020, demonstrating promising performance in terms of stability and accuracy (Meng et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography

Zhang,

Chen

et al. 2023

Phys. Med. Biol.

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Objective. Fluorescence molecular tomography (FMT) is an optical imaging modality that provides high sensitivity and low cost, which can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labeled probe noninvasively. In the field of preclinical cancer diagnosis and treatment, FMT has gained significant traction. Nonetheless, the current FMT reconstruction results suffer from unsatisfactory morphology and location accuracy of the fluorescence distribution, primarily due to the light scattering effect and the ill-posed nature of the inverse problem. Approach. To address these challenges, a regularized reconstruction method based on joint smoothly clipped absolute deviation regularization and graph manifold learning (SCAD-GML) for FMT is presented in this paper. The SCAD-GML approach combines the sparsity of the fluorescent sources with the latent manifold structure of fluorescent source distribution to achieve more accurate and sparse reconstruction results. To obtain the reconstruction results efficiently, the non-convex gradient descent iterative method is employed to solve the established objective function. To assess the performance of the proposed SCAD-GML method, a comprehensive evaluation is conducted through numerical simulation experiments as well as in vivo experiments. Main results. The results demonstrate that the SCAD-GML method outperforms other methods in terms of both location and shape recovery of fluorescence biomarkers distribution. Siginificance. These findings indicate that the SCAD-GML method has the potential to advance the application of FMT in in vivo biological research.

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Section: Introductionmentioning

confidence: 99%

Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography

Zhang,

Chen

et al. 2023

Phys. Med. Biol.

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Source distribution correlation enabled self-attention residual network for effective reconstruction of optical molecular tomography

Wang,

Xiao,

Pan

et al. 2024

Journal of Applied Physics

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As a promising preclinical imaging technique, optical molecular tomography (OMT) shows great potential in early detection and diagnosis of tumor diseases. However, its widespread application has been hindered by the limitations of traditional reconstruction methods, specifically the accuracy of optical transmission models and the ill-posed nature of inverse reconstruction. The development of deep learning has offered novel solutions for OMT, enabling efficient reduction of the ill-posed nature in reconstruction. The existing deep learning approaches employ conventional neural networks and objective functions, which retains significant scope for enhancing the accuracy of image reconstruction. In this paper, we propose a source distribution correlation enabled self-attention residual network (DCeSR network) to address the need for accurate OMT reconstruction. The DCeSR network leverages a residual learning strategy and a self-attention mechanism to effectively integrate the deep and shallow features, subsequently extracting highly informative surface measurements to accurately predict the three-dimensional distribution of light sources within tissues. The efficacy of the DCeSR network was validated through training and testing with two distinct numerical simulated datasets, each encompassing both single and dual source configurations. Both qualitative and quantitative analyses demonstrate the superior performance of the DCeSR network in achieving accurate OMT reconstructions.

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MSCN-NET: Multi-stage cascade neural network based on attention mechanism for Čerenkov luminescence tomography

Cited by 2 publications

References 44 publications

Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography

Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography

Source distribution correlation enabled self-attention residual network for effective reconstruction of optical molecular tomography

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