ArticleAPL photonics2025
On the importance of simultaneous label-free multimodal nonlinear optical imaging for biomedical applications.
Article in APL photonics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Label-free multimodal nonlinear microscopy enabled by an optical parametric generator.APL photonics · 2026Article
- Assessing preterm risk via label-free, multiparametric imaging of collagen fiber remodeling in the cervix.Biomedical optics express · 2026Article
- Investigating Cellular Magnetic Bioeffects Using Two-Channel, Two-Photon Autofluorescence Lifetime Microscopy.IEEE transactions on molecular, biological, and multi-scale communications · 2026Article
- Unified Vibrational and Multiphoton Label-Free Nonlinear Microscopy for Simultaneous Chemical and Structural Imaging.IEEE journal of selected topics in quantum electronics : a publication of the IEEE Lasers and Electro-optics SocietyArticle
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Authors and funding
4 authors.
Funding
Abstract
Label-free nonlinear microscopy offers a powerful tool for the biomedical sciences. It enables investigations of cells and tissues using signals that emerge from endogenous biomolecules and microstructures to derive contrast, thereby preserving the physiological viability and functionality of specimens. Today, the most advanced label-free nonlinear microscopes are multimodal imaging platforms that capitalize on the heterogeneity of biological specimens, capturing not one but many nonlinear signals. Thus, label-free multimodal nonlinear imaging attains a contrast palette with complementary signals, delivering data-rich images that not only allow spatial unmixing and quantification of biochemical species but also unleash the power of correlation analyses and artificial intelligence to extract further information from specimens. In this Perspective, we recap the nonlinear contrast palette and compare the two technological strategies often used to acquire multimodal nonlinear images: a sequential approach vs a simultaneous approach. We then present their strengths and weaknesses and discuss emerging computational strategies that enhance the interpretability of multimodal data.
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Registered trials
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