Journal of Physics-Photonics最新文献

Transabdominal fetal pulse oximetry offers a promising approach to non-invasively monitor fetal arterial oxygen saturation (SaO₂), potentially enhancing clinical decision-making and reducing unnecessary interventions during delivery. However, accurate estimation of fetal SaO₂ (denoted as SpO₂ when measured non-invasively) is complicated by the multi-layer maternal-fetal tissue structure, distinct maternal and fetal physiological signals, and inherently low fetal oxygen saturation levels. A multi-layer self-calibrated algorithm was developed by combining the multi-layer modified Beer-Lambert law with an analytical photon partial pathlength model. This approach distinguishes maternal and fetal tissue contributions, enabling more accurate fetal SpO₂ estimation. Validation was performed using Monte Carlo photon simulations of multi-layer tissue geometries, where synthetic optical signals representing fetal cardiac pulsations were generated under two fetal depths and randomly varied maternal and fetal oxygen saturations and optical properties. Further validation was performed using in vivo sheep data, where fetal SpO₂ values derived from transabdominal continuous-wave near-infrared spectroscopy measurements were compared against reference fetal SaO₂ from CO-oximetry. In simulations, the algorithm achieved a mean absolute error (MAE) below 5% and a Pearson correlation coefficient (R) of 0.98 between estimated fetal SpO₂ and ground truth fetal SaO₂ when using optimal input parameters. In the sheep experiment, agreement with reference measurements was maintained (MAE = 10.3%, R = 0.91). However, algorithm performance was highly sensitive to accurate optical properties and tissue layer thicknesses inputs, which may be challenging to obtain in clinical settings. These results demonstrate proof-of-concept feasibility for the multi-layer self-calibrated algorithm in both simulated and in vivo conditions. While further refinement, particularly in optical property estimation and fetal depths in human pregnancies, is necessary, this work provides a foundational framework for the future clinical translation of non-invasive fetal SpO₂ monitoring.

Macroscopic fluorescence lifetime imaging (MFLI) has emerged as a robust, non-invasive imaging technique offering quantitative insights into physiological and molecular processes within live tissues, independent of fluorophore concentration, excitation intensity, or signal attenuation. However, a key limitation is the inability to accurately determine the depth at which fluorescence signals originate, potentially compromising biological interpretation due to ambiguous localization. In this study, we introduce high spatial frequency-fluorescence lifetime imaging (HSF-FLI), an innovative optical correction methodology designed to effectively eliminate surface signal bias, such as those arising from skin in preclinical imaging, without requiring chemical clearing agents. We develop a modulation transfer function linking spatial frequency with signal penetration depth through comprehensive Monte Carlo eXtreme simulations. Utilizing structured, three-phase sinusoidal illumination, fluorescence signals were accurately decomposed into distinct surface and subsurface components. Experimental validation was performed using agar-based capillary phantoms and a time-gated intensified charged coupled device coupled with a digital micromirror device imaging system. Further demonstrating its practical utility, we successfully applied HSF-FLI to preclinical drug delivery assessments employing Förster resonance energy transfer MFLI. The method was rigorously validated in vivo using mouse tumor xenograft models and cross-validated through ex vivo analyses. Overall, by integrating structure illumination techniques with physics-based depth modeling, HSF-FLI achieves precise depth-selective FLI. This advancement significantly enhances the accuracy, biological interpretation, and applicability of FLI, positioning HSF-FLI as a valuable tool for translational research.

Photoacoustic tomography (PAT) is an emerging biomedical imaging technology that combines the molecular sensitivity of optical imaging with the spatial resolution of ultrasonic imaging in deep tissue. Molecular PAT, a subset of PAT, takes advantage of the specific absorption of molecules to reveal tissue structures, functions, and dynamics. Thanks to the high sensitivity to the optical absorption of molecules, PAT can selectively image those molecules by tuning the excitation wavelength to each target's optical absorption signature. PAT has imaged various molecular targets in vivo, ranging from endogenous chromophores, e.g. hemoglobin, melanin, and lipids, to specialized exogenous contrasts such as organic dyes, genetically encoded proteins, and nano/microparticles. Each molecular contrast hosts inherent advantages. Endogenous contrasts allow for truly noninvasive imaging but cannot attain high specificity or sensitivity for many biological processes, whereas artificial exogenous contrasts can. Recent advances in imaging these contrast agents have shown the immense potential of photoacoustic imaging for diagnosing, monitoring, and treating medical conditions, along with studying the fundamental processes in vivo.

The understanding of cellular mechanisms benefits substantially from accurate determination of protein diffusive properties. Prior work in this field primarily focuses on traditional methods, such as mean square displacements, for calculation of protein diffusion coefficients and biological states. This proves difficult and error-prone for proteins undergoing heterogeneous behaviour, particularly in complex environments, limiting the exploration of new biological behaviours. The importance of determining protein diffusion coefficients, anomalous exponents, and biological behaviours led to the Anomalous Diffusion Challenge 2024, exploring machine learning methods to infer these variables in heterogeneous trajectories with time-dependent changepoints. In response to the challenge, we present M3, a machine learning method for pointwise inference of diffusive coefficients, anomalous exponents, and states along noisy heterogenous protein trajectories. M3 makes use of long short-term memory cells to achieve small mean absolute errors for the diffusion coefficient and anomalous exponent alongside high state accuracies (>90%). Subsequently, we implement changepoint detection to determine timepoints at which protein behaviour changes. M3 removes the need for expert fine-tuning required in most conventional statistical methods while being computationally inexpensive to train. The model finished in the Top 5 of the Anomalous Diffusive Challenge 2024, with small improvements made since challenge closure.

Imaging of subcellular structures, which underpins many of the advances in biological and medical sciences, requires microscopes with high numerical aperture (N.A.) objectives which are costly, complex, requires oil immersion and have very limited field-of-view, typically covering a handful of cells. Here, we leverage a low N.A. objective to simultaneously capture scattering, phase, and fluorescence images of subcellular structures in breast cancer cells (BT-20) and observe nanoparticle uptake, with sub-diffraction-limited resolution (<400 nm with a 0.25 N.A. objective) utilizing a 2-dimensional (2-D) microlens substrate. High resolution labeled and label-free images of subcellular components is made possible by implementing a specific configuration, wherein the sample is placed in close proximity to the microlens substrate, which results in efficient collection of the rapidly decaying evanescent waves that contains the high frequency information, thereby improving resolution and the light capture efficiency. The microlens-assisted imaging provides an easy-to-implement and cost-effective means of drastically improving the resolution of any microscope with low N.A. objective lenses, paving the way for the development of affordable, portable multi-modal imaging systems with high-resolution imaging capabilities. This technology has broad implications for various fields and could democratize access to high-quality microscopy, particularly for application in resource-limited settings.