ArticleBiomedical optics express2017
Improving mesoscopic fluorescence molecular tomography through data reduction.
Article in Biomedical optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy.Biochemical and biophysical research communications · 2021Article
- Intravital mesoscopic fluorescence molecular tomography allows non-invasive in vivo monitoring and quantification of breast cancer growth dynamics.Communications biology · 2021Article
- High-resolution tomographic analysis of in vitro 3D glioblastoma tumor model under long-term drug treatment.Science advances · 2020Article
- System configuration optimization for mesoscopic fluorescence molecular tomography.Biomedical optics express · 2019Article
- Comparison of time- and angular-domain scatter rejection in mesoscopic optical projection tomography: a simulation study.Biomedical optics express · 2019Article
- Recent methodology advances in fluorescence molecular tomography.Visual computing for industry, biomedicine, and art · 2018Review
- Improving mesoscopic fluorescence molecular tomography via preconditioning and regularization.Biomedical optics express · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique that aims at obtaining the 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters. To achieve high resolution, around 100-150μm scale in turbid samples, dense spatial sampling strategies are required. However, a large number of optodes leads to sizable forward and inverse problems that can be challenging to compute efficiently. In this work, we propose a two-step data reduction strategy to accelerate the inverse problem and improve robustness. First, data selection is performed via signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) criteria. Then principal component analysis (PCA) is applied to further reduce the size of the sensitivity matrix. We perform numerical simulations and phantom experiments to validate the effectiveness of the proposed strategy. In both
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.