Three-dimensional opto-thermo-mechanical model for predicting photo-thermal optical coherence tomography responses in multilayer geometries

Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography

Optical coherence tomography (OCT) is a medical imaging method that generates micron-resolution 3D volumetric images of tissues in-vivo. Photothermal (PT)-OCT is a functional extension of OCT with the potential to provide depth-resolved molecular information complementary to the OCT structural images. PT-OCT typically requires long acquisition times to measure small fluctuations in the OCT phase signal. Here, we use machine learning with a neural network to infer the amplitude of the photothermal phase modulation from a short signal trace, trained in a supervised fashion with the ground truth signal obtained by conventional reconstruction of the PT-OCT signal from a longer acquisition trace. Results from phantom and tissue studies show that the developed network improves signal to noise ratio (SNR) and contrast, enabling PT-OCT imaging with short acquisition times and without any hardware modification to the PT-OCT system. The developed network removes one of the key barriers in translation of PT-OCT (i.e., long acquisition time) to the clinic.

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.

Photothermal optical coherence tomography for 3D live cell detection and mapping

Imaging cells in their 3D environment with molecular specificity is important to cell biology study. Widely used microscopy techniques, such as confocal microscopy, have limited imaging depth when probing cells in optically scattering media. Optical coherence tomography (OCT) can provide millimeter-level depth for imaging of highly scattering media but lacks the contrast to distinguish cells from extracellular matrix or to distinguish between different types of cells. Photothermal OCT (PT-OCT) is a promising technique to obtain molecular contrast at the imaging scale of OCT. Here, we report PT-OCT imaging of live, nanoparticle-labeled cells in 3D. In particular, we demonstrate detection and mapping of single cell in 3D without causing call death, and show the feasibility of 3D cell mapping through optical scattering media. This work presents live cell detection and mapping at an imaging scale that complements the major microscopy techniques, which is potentially useful to study cells in their 3D native or culture environment.