Interferometric techniques for virtual histology and staining: principles, techniques, and applications in biomedical imaging
Background: Traditional histopathology relies on labor-intensive processes like tissue fixation, sectioning, and staining, which are time-consuming, costly, and prone to sample degradation. Virtual histology, leveraging advanced interferometric and optical imaging techniques, offers transformative p...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302502208X |
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| Summary: | Background: Traditional histopathology relies on labor-intensive processes like tissue fixation, sectioning, and staining, which are time-consuming, costly, and prone to sample degradation. Virtual histology, leveraging advanced interferometric and optical imaging techniques, offers transformative potential by enabling real-time, non-invasive, and label-free tissue analysis. Methods: This review explores the principles and applications of interferometric techniques, including Optical Coherence Tomography (OCT), Quantitative Phase Imaging (QPI), and Phase-Shifting Interferometry (PSI). A comparative analysis of their underlying physics, imaging capabilities, and integration with multimodal and artificial intelligence (AI)-enhanced systems is presented. Applications in various fields, such as ophthalmology, oncology, cardiology, and neurology, are highlighted. Results: Interferometric methods deliver high-resolution, depth-resolved, and label-free imaging of tissues in both in-vivo and ex-vivo contexts. OCT and its extensions provide structural and functional insights, while QPI and PSI enable quantification of tissue morphology and refractive properties. AI integration enhances imaging accuracy, automates analysis, and generates virtual histological images that rival traditional stained specimens. Conclusion: Interferometric techniques represent a paradigm shift in histopathology by enabling rapid, non/minimally-invasive diagnostics and detailed tissue characterization. Despite challenges related to resolution limits, computational demands, and standardization, these methods hold immense potential for advancing precision medicine. Future research should focus on optimizing system robustness, integrating multimodal imaging, and leveraging AI for clinical applications. |
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| ISSN: | 2590-1230 |