Enhancing Total Optical Throughput of Microscopy with Deep Learning for Intravital Observation

Frontiers in fluorescence imaging: tools for the in situ sensing of disease biomarkers

Fluorescence imaging has been recognized as a powerful tool for the real-time detection and specific imaging of biomarkers within living systems, which is crucial for early diagnosis and treatment evaluation of major diseases. Over the years, significant advancements in this field have been achieved, particularly with the development of novel fluorescent probes and advanced imaging technologies such as NIR-II imaging, super-resolution imaging, and 3D imaging. These technologies have enabled deeper tissue penetration, higher image contrast, and more accurate detection of disease-related biomarkers. Despite these advancements, challenges such as improving probe specificity, enhancing imaging depth and resolution, and optimizing signal-to-noise ratios still remain. The emergence of artificial intelligence (AI) has injected new vitality into the designs and performances of fluorescent probes, offering new tools for more precise disease diagnosis. This review will not only discuss chemical modifications of classic fluorophores and in situ visualization of various biomarkers including metal ions, reactive species, and enzymes, but also share some breakthroughs in AI-driven fluorescence imaging, aiming to provide a comprehensive understanding of these advancements. Future prospects of fluorescence imaging for biomarkers including the potential impact of AI in this rapidly evolving field are also highlighted.

Intravital observation of high-scattering and dense-labeling hepatic tissues using multi-photon fluorescence microscopy

Achieving high-resolution and large-depth microscopic imaging in vivo under conditions characterized by high-scattering and dense-labeling, as commonly encountered in the liver, poses a formidable challenge. Here, through the optimization of multi-photon fluorescence excitation window, tailored to the unique optical properties of the liver, intravital microscopic imaging of hepatocytes and hepatic blood vessels with high spatial resolution was attained. It's worth noting that resolution degradation caused by tissue scattering of excitation light was mitigated by accounting for moderate tissue self-absorption. Leveraging high-quality multi-photon fluorescence microscopy, we discerned structural and functional alterations in hepatocytes during drug-induced acute liver failure. Furthermore, a reduction in indocyanine green metabolism rates associated with acute liver failure was observed using NIR-II fluorescence macroscopic imaging.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.