Main description:

The discovery and use of new optical windows where short-wavelength infrared (SWIR) light can be transmitted between areas of intense water absorption is a major development in photonics. This book covers biomedical uses of light at the conventionally used first and second optical windows, and, in particular, explores emerging applications of SWIR light at a third and a fourth optical window (at 1600-1870 nm and 2100-2350 nm, respectively) in the SWIR range. Diagnostic techniques that utilize these windows are reviewed, as are applications to cancer and diseases of organs such as the heart, brain, and skin. The book ends with an extensive discussion of the potential uses of artificial intelligence to enhance the ability to study these diseases at SWIR optical windows.

Contents:

Optical Properties of Tissues Using SWIR Light
Luminescence Nanothermometry and Photothermal Conversion Efficiency for Particles Operating in the SWIR Region
SWIR Properties of Rare-Earth-Doped Nanoparticles for Biomedical Applications
Short-Wave Infrared Meso-Patterned Imaging for Quantitative and Label-Free Tissue Characterization
Short-Wavelength Infrared Hyperspectral Imaging for Biomedical Applications
VIS-SWIR Wideband Lens-Free Microscopic Imaging
SWIR Fluorescence and Monte Carlo Modeling of Tissues for Medical Applications
Multimodal SWIR Laser Imaging for Assessment and Detection of Urothelial Carcinomas
SWIR Fluorescence Endoscopy for Tumor Imaging
Short-Wavelength Infrared Hyperspectral Imaging to Assess Gastrointestinal Stromal Tumors during Surgery
SWIR for the Assessment of Heart Failure
Transparent Polycrystalline Ceramic Cranial Implant with Photonic Functionality in the SWIR
SWIR Hyperspectral Imaging to Assess Neurocognitive Disorders Using Blood Plasma Samples
Hyperspectral SWIR Imaging of Skin Inflammation
Use of a SWIR Otoscope in the Assessment of Pediatric and Other Conditions
Use of an OCT System in the Short-Wavelength Infrared Region: Applications
SWIR Imaging of Lesions on Tooth Surfaces
Advances in SWIR Deep Tissue Imaging Using Machine Learning