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.

Effects of lipid composition on photothermal optical coherence tomography signals.

SIGNIFICANCE: Photothermal optical coherence tomography (PT-OCT) has the promise to offer structural images coregistered with chemical composition information, which can offer a significant impact in early detection of diseases such as atherosclerosis. AIM: We take the first step in understanding the relation between PT-OCT signals and the endogenous tissue composition by considering the interplay between the opto-thermo-physical properties of tissue as a function of its lipid composition and the ensuing effects on the PT-OCT signals. APPROACH: Multiparameter theoretical estimates for PT-OCT signal as a function of composition in a two-component lipid-water model are derived and discussed. Experimental data from various concentrations of lipid in the form of droplets and injections under bovine cardiac muscle align with theoretical predictions. RESULTS: Theoretical and experimental results suggest that the variations of heat capacity and mass density with tissue composition significantly contribute to the amount of optical path length difference measured by OCT phase. CONCLUSION: PT-OCT has the potential to offer key insights into the chemical composition of the subsurface lipid pools in tissue; however, the interpretation of results needs to be carried out by keeping the nonlinear interplay between the tissue of opto-thermo-physical properties and PT-OCT signals in mind.

Low-Cost Active Thermography using Cellphone Infrared Cameras: from Early Detection of Dental Caries to Quantification of THC in Oral Fluid.

Active thermography (AT) is a widely studied non-destructive testing method for the characterization and evaluation of biological and industrial materials. Despite its broad range of potential applications, commercialization and wide-spread adaption of AT has long been impeded by the cost and size of infrared (IR) cameras. In this paper, we demonstrate that this cost and size limitation can be overcome using cell-phone attachment IR cameras. A software development kit (SDK) is developed that controls camera attributes through a simple USB interface and acquires camera frames at a constant frame rate up to 33 fps. To demonstrate the performance of our low-cost AT system, we report and discuss our experimental results on two high impact potential applications. The first set of experiments is conducted on a dental sample to investigate the clinical potential of the developed low-cost technology for detecting early dental caries, while the second set of experiments is conducted on the oral-fluid based lateral flow immunoassay to determine the viability of our technology for detecting and quantifying cannabis consumption at the point-of-care. Our results suggest achievement of reliable performance in the low-cost platform, comparable to those of costly and bulky research-grade systems, paving the way for translation of AT techniques to market.