Measuring glucose concentration in a solution based on the indices of polarimetric purity

Deep tissue sensing of chiral molecules using polarization-enhanced photoacoustics

Chiral molecule sensing is typically performed using techniques like chromatography, electrophoresis, enzymatic assays, mass spectrometry, and chiroptical methods. While polarimetry allows for in vivo sensing up to 1 mm depth using ultraviolet-visible light, it is limited by dominant light scattering beyond this depth. We propose that photoacoustic sensing in the near-infrared II (NIR-II) window can enable deep tissue sensing as acoustic waves scatter less than light. To achieve this, we developed a photoacoustic polarization-enhanced optical rotation sensing (PAPEORS) system, capable of estimating optical rotation from photoacoustic signals and correlating it with chiral molecular concentration for depths up to 3.5 mm. Experiments were conducted using aqueous glucose solutions, naproxen, serum-based glucose samples, and ex vivo chicken tissue. PAPEORS achieved a detection limit of 80 mg/dl while using circularly polarized light with serum samples, demonstrating the potential for deep-tissue chiral molecular sensing. PAPEORS holds promise for in vivo sensing and easy miniaturization using single wavelength.

Optimal path length identification for accurate glucose sensing with photoacoustic derived optical rotation

Non-invasive glucose monitoring is crucial for diabetes management. This study explores the use of photoacoustic (PA) signals based on optical rotation estimation at multiple depths for detection of glucose concentrations. Experiments were performed with glucose samples mixed in bovine serum albumin with different polarization incidences-vertical (V), 45° linear (P), and right circular (R) polarization. Polarized Monte Carlo (PMC) simulations were performed to understand the depth-dependent behavior between optical and photoacoustic detection of optical rotation, which allows the estimate of glucose concentration. Notably, a specific depth range exhibited both maximum rotation and a better linear relationship with concentration, which are ideal for sensing. Both experimental and simulation studies indicated significant depolarization beyond a depth of 4 mm. Additionally, the change in rotation with respect to depth (Δα) was higher for larger concentration differences compared to smaller concentration differences. Our study identified that the optimal depth for accurate glucose sensing (based on Clarke's error grid (CEG)) was found to be around 3-3.2 mm for the different polarized incidences. These findings showcase the potential of our approach for non-invasive glucose sensing and a calibration procedure to pinpoint optimal sensing depths, extendable to other chiral molecules.

Polarization motivating high-performance weak targets' imaging based on a dual-discriminator GAN

High-level detection of weak targets under bright light has always been an important yet challenging task. In this paper, a method of effectively fusing intensity and polarization information has been proposed to tackle this issue. Specifically, an attention-guided dual-discriminator generative adversarial network (GAN) has been designed for image fusion of these two sources, in which the fusion results can maintain rich background information in intensity images while significantly completing target information from polarization images. The framework consists of a generator and two discriminators, which retain the texture and salient information as much as possible from the source images. Furthermore, attention mechanism is introduced to focus on contextual semantic information and enhance long-term dependency. For preserving salient information, a suitable loss function has been introduced to constrain the pixel-level distribution between the result and the original image. Moreover, the real scene dataset of weak targets under bright light has been built and the effects of fusion between polarization and intensity information on different weak targets have been investigated and discussed. The results demonstrate that the proposed method outperforms other methods both in subjective evaluations and objective indexes, which prove the effectiveness of achieving accurate detection of weak targets in bright light background.

Dynamic polarization fusion network (DPFN) for imaging in different scattering systems

Deep learning has broad applications in imaging through scattering media. Polarization, as a distinctive characteristic of light, exhibits superior stability compared to light intensity within scattering media. Consequently, the de-scattering network trained using polarization is expected to achieve enhanced performance and generalization. For getting optimal outcomes in diverse scattering conditions, it makes sense to train expert networks tailored for each corresponding condition. Nonetheless, it is often unfeasible to acquire the corresponding data for every possible condition. And, due to the uniqueness of polarization, different polarization information representation methods have different sensitivity to different environments. As another of the most direct approaches, a generalist network can be trained with a range of polarization data from various scattering situations, however, it requires a larger network to capture the diversity of the data and a larger training set to prevent overfitting. Here, in order to achieve flexible adaptation to diverse environmental conditions and facilitate the selection of optimal polarization characteristics, we introduce a dynamic learning framework. This framework dynamically adjusts the weights assigned to different polarization components, thus effectively accommodating a wide range of scattering conditions. The proposed architecture incorporates a Gating Network (GTN) that efficiently integrates multiple polarization features and dynamically determines the suitable polarization information for various scenarios. Experimental result demonstrates that the network exhibits robust generalization capabilities across continuous scattering conditions.

TSMPN-PSI: high-performance polarization scattering imaging based on three-stage multi-pipeline networks

Polarization imaging, which provides multidimensional information beyond traditional intensity imaging, has prominent advantages for complex imaging tasks, particularly in scattering environments. By introducing deep learning (DL) into computational imaging and sensing, polarization scattering imaging (PSI) has obtained impressive progresses, however, it remains a challenging but long-standing puzzle due to the fact that scattering medium can result in significant degradation of the object information. Herein, we explore the relationship between multiple polarization feature learning strategy and the PSI performances, and propose a new multi-polarization driven multi-pipeline (MPDMP) framework to extract rich hierarchical representations from multiple independent polarization feature maps. Based on the MPDMP framework, we introduce a well-designed three-stage multi-pipeline networks (TSMPN) architecture to achieve the PSI, named TSMPN-PSI. The proposed TSMPN-PSI comprises three stages: pre-processing polarization image for de-speckling, multiple polarization feature learning, and target information reconstruction. Furthermore, we establish a real-world polarization scattering imaging system under active light illumination to acquire a dataset of real-life scenarios for training the model. Both qualitative and quantitative experimental results show that the proposed TSMPN-PSI achieves higher generalization performance than other methods on three testing data sets refer to imaging distances, target structures, and target materials and their background materials. We believe that our work presents a new framework for the PSI and paves the way to its pragmatic applications.

Optimized sinusoidal patterns for high-performance computational ghost imaging

Computational ghost imaging (CGI) can reconstruct scene images by two-order correlation between sampling patterns and detected intensities from a bucket detector. By increasing the sampling rates (SRs), imaging quality of CGI can be improved, but it will result in an increasing imaging time. Herein, in order to achieve high-quality CGI under an insufficient SR, we propose two types of novel sampling methods for CGI, to the best of our knowledge, cyclic sinusoidal-pattern-based CGI (CSP-CGI) and half-cyclic sinusoidal-pattern-based CGI (HCSP-CGI), in which CSP-CGI is realized by optimizing the ordered sinusoidal patterns through "cyclic sampling patterns," and HCSP-CGI just uses half of the sinusoidal pattern types of CSP-CGI. Target information mainly exists in the low-frequency region, and high-quality target scenes can be recovered even at an extreme SR of 5%. The proposed methods can significantly reduce the sampling number and real-time ghost imaging possible. The experiments demonstrate the superiority of our method over state-of-the-art methods both qualitatively and quantitatively.

Self-attention module in a multi-scale improved U-net (SAM-MIU-net) motivating high-performance polarization scattering imaging

Polarization imaging has outstanding advantages in the field of scattering imaging, which still encounters great challenges in heavy scattering media systems even though there are helps from deep learning technology. In this paper, we propose a self-attention module (SAM) in multi-scale improved U-net (SAM-MIU-net) for the polarization scattering imaging, which can extract a new combination of multidimensional information from targets effectively. The proposed SAM-MIU-net can focus on the stable feature carried by polarization characteristics of the target, so as to enhance the expression of the available features, and make it easier to extract polarization features which help to recover the detail of targets for the polarization scattering imaging. Meanwhile, the SAM's effectiveness has been verified in a series of experiments. Based on proposed SAM-MIU-net, we have investigated the generalization abilities for the targets' structures and materials, and the imaging distances between the targets and the ground glass. Experimental results demonstrate that our proposed SAM-MIU-net can achieve high-precision reconstruction of target information under incoherent light conditions for the polarization scattering imaging.

High-performance scanning-mode polarization based computational ghost imaging (SPCGI)

Computational ghost imaging (CGI) uses preset patterns and single-pixel detection, breaking through the traditional form of point-to-point imaging. In this paper, based on the Monte Carlo model, a reflective polarization based CGI (PCGI) system has been proposed and constructed under the foggy environments. And the imaging performances of the PCGI at different optical distances have been investigated and analyzed quantitatively. When the targets and the background have a small difference in reflectivity, the difference of polarization characteristics between the targets and the background can help the CGI to remove the interference of scattering light and improve the imaging contrast. Besides, in order to further improve imaging efficiency, a scanning-mode polarization based CGI (SPCGI) has also been proposed, in which the combination of polarization characteristics and the scanning-mode plays an important role to improve the CGI's imaging efficiency and imaging quality.

Unraveling the physical information of depolarizers

The link between depolarization measures and physical nature and structure of material media inducing depolarization is nowadays an open question. This article shows how the joint use of two complementary sets of depolarizing metrics, namely the Indices of polarimetric purity and the Components of purity, are sufficient to completely describe the integral depolarizing properties of a sample. Based on a collection of illustrative and representative polarimetric configurations, a clear and meaningful physical interpretation of such metrics is provided, thus extending the current tools and comprehension for the study and analysis of the depolarizing properties of material media. This study could be of interest to those users dealing with depolarization or depolarizing samples.

Polarization and Orbital Angular Momentum of Light in Biomedical Applications: feature issue introduction

In the last decade, consistent and successful innovations have been achieved in the field of lasers and optics, collectively known as 'photonics', founding new applications in biomedicine, including clinical biopsy. Non-invasive photonics-based diagnostic modalities are rapidly expanding, and with their exponential improvement, there is a great potential to develop practical instrumentation for automatic detection and identification of different types and/or sub-types of diseases at a very early stage. While using conventional light for the studies of different properties of objects in materials science, astrophysics and biomedicine already has a long history, the interaction of polarized light and optical angular momentum with turbid tissue-like scattering media has not yet been ultimately explored. Since recently this research area became a hot topic. This feature issue is a first attempt to summarize the recognitions achieved in this emerging research field of polarized light and optical angular momentum for practical biomedical applications during the last years.