Sparse-Laplace hybrid graph manifold method for fluorescence molecular tomography.

IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Physics in medicine and biology Pub Date : 2024-10-17 DOI:10.1088/1361-6560/ad84b8
Beilei Wang, Shuangchen Li, Heng Zhang, Lizhi Zhang, Jintao Li, Jingjing Yu, Xiaowei He, Hongbo Guo
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Abstract

Objective.Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation.Approach.To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model. This model integrates a hybrid Laplace norm-based graph manifold learning term, facilitating a trade-off between sparsity and preservation of morphological features. To address the non-convexity of the hybrid objective function, a fixed-point equation is designed, which employs two successive resolvent operators and a forward operator to find a converged solution.Main results.Through numerical simulations andin vivoexperiments, we demonstrate that the SLHGM model achieves an improved performance in providing accurate spatial localization while preserving morphological details.Significance.Our findings suggest that the SLHGM model has the potential to advance the application of FMT in biological research, not only in simulation but also inin vivostudies.

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用于荧光分子断层成像的稀疏-拉普拉斯混合图流形方法。
目的:荧光分子断层成像(FMT)能以低成本、非侵入性的方式绘制三维荧光制剂图,有望用于早期肿瘤检测。然而,由于生物组织中的强散射效应和可测量数据的有限性,目前的荧光分子断层重构在位置预测和形态保存等方面的准确性仍不尽人意。该模型集成了基于拉普拉斯规范的混合图流形学习项,有助于在稀疏性和形态特征保留之间进行权衡。主要结果:通过数值模拟和活体实验,我们证明了稀疏-拉普拉斯混合图流形模型在提供精确空间定位的同时保留了形态细节,性能得到了改善。
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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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