Journal of Innovative Optical Health Sciences

Polarimetry is a powerful optical tool in the biomedical field, providing more comprehensive information on the sub-wavelength micro-physical structure of a sample than traditional light intensity measurement techniques. This review summarizes the concepts and techniques of polarization and its biomedical applications. Specifically, we first briefly describe the basic principles of polarized light and the Mueller matrix (MM) decomposition method, followed by some research progress of polarimetric measurement techniques in recent years. Finally, we introduce some studies on biological tissues and cells, and then illustrate the application value of polarization optical method.

The careful observation and evaluation of the squamocolumnar junction (SCJ) are essential requirements in colposcopy procedures. While traditional colposcopes with visible light offer high resolution and sensitivity, the presence of glare patterns on the cervix limits the visualization of deeper tissue features. Cross-polarization imaging, on the other hand, overcomes this limitation by effectively removing glare and providing greater optical penetration depth to noninvasively examine subsurface tissue structures. In this study, we present the potential of polarized light in enhancing diagnostics through cross-polarization imaging for the identification and contrast enhancement of squamous and columnar epithelium (CE) in cases of cervical ectropion. Our results demonstrate that polarized light colposcopy offers valuable diagnostic information that complements traditional colposcopy. By effectively reducing glare and improving visibility during cervical examinations, polarized light colposcopy proves to be a useful tool. Furthermore, we introduce a novel method that practically enhances the contrast ratio (CR) between columnar and squamous epithelium (SE) in colposcopic images. This method significantly increases the contrast between these tissue types, facilitating clearer differentiation and improving diagnostic accuracy. Notably, the combination of the cross-polarization imaging technique with our proposed algorithm enables the clear observation of the SCJ boundary. These findings emphasize the potential of our approach in enhancing the accuracy and effectiveness of polarized colposcopy for evaluating cervical tissue.

Osteosarcoma is the most common primary bone tumor with high malignancy. It is particularly necessary to achieve rapid and accurate diagnosis in its intraoperative examination and early diagnosis. Accordingly, the multimodal microscopic imaging diagnosis system constructed by bright field, spontaneous fluorescence and polarized light microscopic imaging was used to study the pathological mechanism of osteosarcoma from the tissue microenvironment level and achieve rapid and accurate diagnosis. First, the multimodal microscopic images of normal and osteosarcoma tissue slices were collected to characterize the overall morphology of the tissue microenvironment of the samples, the arrangement structure of collagen fibers and the content and distribution of endogenous fluorescent substances. Second, based on the correlation and complementarity of the feature information contained in the three single-mode images, combined with convolutional neural network (CNN) and image fusion methods, a multimodal intelligent diagnosis model was constructed to effectively improve the information utilization and diagnosis accuracy. The accuracy and true positivity of the multimodal diagnostic model were significantly improved to 0.8495 and 0.9412, respectively, compared to those of the single-modal models. Besides, the difference of tissue microenvironments before and after cancerization can be used as a basis for cancer diagnosis, and the information extraction and intelligent diagnosis of osteosarcoma tissue can be achieved by using multimodal microscopic imaging technology combined with deep learning, which significantly promoted the application of tissue microenvironment in pathological examination. This diagnostic system relies on its advantages of simple operation, high efficiency and accuracy and high cost-effectiveness, and has enormous clinical application potential and research significance.

Recently, Mueller matrix (MM) polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis. However, since our human eyes cannot observe polarized light directly, it raises a notable challenge for interpreting the measurement results by pathologists who have limited familiarity with polarization images. One feasible approach is to combine MM polarimetric imaging with virtual staining techniques to generate standardized stained images, inheriting the advantages of information-abundant MM polarimetric imaging. In this study, we develop a model using unpaired MM polarimetric images and bright-field images for generating standard hematoxylin and eosin (H&E) stained tissue images. Compared with the existing polarization virtual staining techniques primarily based on the model training with paired images, the proposed Cycle-Consistent Generative Adversarial Networks (CycleGAN)-based model simplifies data acquisition and data preprocessing to a great extent. The outcomes demonstrate the feasibility of training CycleGAN with unpaired polarization images and their corresponding bright-field images as a viable approach, which provides an intuitive manner for pathologists for future polarization-assisted digital pathology.

Thermoacoustic imaging (TAI) contrast comes from different electrical properties (EPs) of microwave absorption. However, the relationship between the permittivity distribution and the electric field polarization limits the application of TAI in some extent. Here, we present a polarimetric thermoacoustic imaging (pTAI) system based on a rotary waveguide. By optimizing a rotary waveguide (insertion loss < 0.2dB, return loss > 15dB at 3GHz), the excitation wave emitted from a horn antenna can rotate freely in the direction of polarization. The multi-polarization pulsed microwave signal is used to evoke the thermoacoustic signals (TAS) of samples in different polarimetric directions. The simulation for pTAI with a multi-directional sample is first obtained. Then, we rotated the rotary waveguide at 10^∘/s and experimentally demonstrated the performance of pTAI by imaging three randomly placed soy sauce tubes and ex vivo beef. This study suggests that pTAI has potential for dielectric properties “diffusion” mapping, for example, provides conductivity tensor mapping of brain and muscle.

A new polarization–interference biomedical diagnostic three-dimensional (3D) Jones-matrix technology with digital Fourier reconstruction of layered maps of optical anisotropy (thesiograms) of dehydrated films (facies) of biological fluids of human organs is presented and experimentally tested. An original model of layered phase scanning of polycrystalline architectonics of supramolecular networks of biological fluid facies is proposed for the purpose of theoretical justification and prognostic use of the obtained results. On its basis, algorithms of Jones-matrix reconstruction of thesiograms of birefringence and dichroism of facies of synovial fluid, bile and blood are found. As a result, layered thesiograms of linear and circular birefringence and dichroism of facies with different spatial–angular architectonics of supramolecular networks are experimentally obtained for the first time. Within the framework of statistical analysis of experimental data, new objective markers (asymmetry and excess of optical anisotropy parameter distributions) for diagnostics of pathological changes in the optical anisotropy of biological fluid facies were defined and clinically tested. As a result, an excellent level of balanced accuracy of the developed polarization–interference Jones-matrix method of layer-by-layer reconstruction of thesiograms of polycrystalline supramolecular networks in differential diagnostics of bile facies (cholelithiasis), synovial fluid (reactive synovitis–septic arthritis) and whole blood (follicular adenoma–papillary thyroid cancer) was achieved.

It is of great significance in clinical diagnosis and treatment evaluation to accurately identify the lesion tissue and further extract its characteristics and depth location. In this study, we construct tissue phantoms for three lesion types: fibrosis (FT), organelle proliferation (OPT), and pigmentation (PT). These phantoms allow for the quantitative regulation of mimicked disease depth. The experimental results show that the parameter Kc, combined with MMT parameters, can effectively distinguish the presence of lesions and their abnormal types. Further, the study extracts depth-sensitive polarization feature parameters (DSPFPs) for specific lesion types. Through experiments of tissue phantoms with various depth settings, the established machine learning regression models based on DSPFPs demonstrate their depth retrieval capabilities.

The high mortality rates of colon and rectal tumors have put forward an urgent need for rapid, sensitive, and accurate diagnosis. The polarization imaging technology, with the advantages of noninvasiveness, noncontact, quantification, rapidity, and high sensitivity, is expected to be used for auxiliary diagnosis of colorectal cancer. Herein, the differences in colorectal tissues of four pathological types were studied using this powerful technology. Polarized light imaging combined with the Mueller matrix decomposition (MMPD) method was applied to extract structural features that may be related to colorectal tumors. It demonstrated that parameters $\delta$ and $\theta$ could reflect the structural differences of colorectal tumors. Preliminary simulated experiment results revealed that the parameter $\delta$ was related to the fiber density, and the parameter $\theta$ was related to the fiber angle. Then Tamura image texture analysis was used to quantitatively describe tissues of different pathological types, and the results showed that the coarseness, contrast, directionality, and roughness of the four groups were statistically different. Texture analysis based on the quantitative data of the four dimensions could be applied for the identification of benign and malignant colorectal tumors.

The hippocampus is the brain structure that is responsible for the formation of learning memories. Sleep disorders leading to cognitive impairment are strongly associated with the hippocampus. Phototherapy offers a new physical therapy for the treatment of sleep disorders, with the advantages of being noninvasive and having few side effects. However, the mechanism by which phototherapy improves cognitive impairment caused by sleep disorders remains unclear. In this study, we used phototherapy combined with optogenetic technology to investigate the effect of noninvasive phototherapy on cognitive functions in sleep-deprived mice. Our results suggest that phototherapy might improve cognitive functions in sleep-deprived mice by modulating the hippocampus. Our study expands the research progress on noninvasive phototherapy for the treatment of sleep disorders.

Acoustic-resolution photoacoustic microscopy (AR-PAM) suffers from degraded lateral resolution due to acoustic diffraction. Here, a resolution enhancement strategy for AR-PAM via a mean-reverting diffusion model was proposed to achieve the transition from acoustic resolution to optical resolution. By modeling the degradation process from high-resolution image to low-resolution AR-PAM image with stable Gaussian noise (i.e., mean state), a mean-reverting diffusion model is trained to learn prior information of the data distribution. Then the learned prior is employed to generate a high-resolution image from the AR-PAM image by iteratively sampling the noisy state. The performance of the proposed method was validated utilizing the simulated and in vivo experimental data under varying lateral resolutions and noise levels. The results show that an over 3.6-fold enhancement in lateral resolution was achieved. The image quality can be effectively improved, with a notable enhancement of ∼66% in PSNR and ∼480% in SSIM for in vivo data.

Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of $256\times 256$ pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.