How to use near-infrared spectroscopy

Donnelly, Peter; Fine-Goulden, Miriam

doi:10.1136/archdischild-2018-315532

Cited by 1 publication

(1 citation statement)

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“…NIR-I wavelengths are ideal for optical imaging as they can penetrate biological tissues such as skin and blood more deeply and effectively because they are not easily absorbed and scattered by these tissues ( Prajapati et al, 2010 ; Vahrmeijer et al, 2013 ; Burggraaf et al, 2015 ). The autofluorescence of biological tissues in this wavelength range is minimal, resulting in a relatively high signal-to-background ratio (SBR) ( Sakudo, 2016 ; Donnelly and Fine-Goulden, 2020 ). Currently, NIR-I wavelengths are the most suitable for deep tissue imaging of animals.…”

Section: Introductionmentioning

confidence: 99%

Advances in optical molecular imaging for neural visualization

Wei,

Liu,

Liang

et al. 2023

Front. Bioeng. Biotechnol.

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Iatrogenic nerve injury is a significant complication in surgery, which can negatively impact patients’ quality of life. Currently, the main clinical neuroimaging methods, such as computed tomography, magnetic resonance imaging, and high-resolution ultrasonography, do not offer precise real-time positioning images for doctors during surgery. The clinical application of optical molecular imaging technology has led to the emergence of new concepts such as optical molecular imaging surgery, targeted surgery, and molecular-guided surgery. These advancements have made it possible to directly visualize surgical target areas, thereby providing a novel method for real-time identification of nerves during surgery planning. Unlike traditional white light imaging, optical molecular imaging technology enables precise positioning and identifies the cation of intraoperative nerves through the presentation of color images. Although a large number of experiments and data support its development, there are few reports on its actual clinical application. This paper summarizes the research results of optical molecular imaging technology and its ability to realize neural visualization. Additionally, it discusses the challenges neural visualization recognition faces and future development opportunities.

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