Mueller Polarimetry Research Articles

Mueller polarimetry for quantifying the stress optic coefficient in the infrared

Mueller polarimetry for quantifying the stress optic coefficient in the infrared

Chiral media and optical rotatory dispersion by means of a simple polarimetric experiment for undergraduate students

A simple experiment taking into account media with natural optical activity and their characterization by means of Mueller polarimetry is proposed for undergraduate Optics and Photonics laboratories. The objectives are, first, to review how to characterize dielectric and homogeneous media with natural optical activity by means of a Mueller matrix, taking into account the variation of these characteristics with wavelength, and combining concepts such as polarimetry, natural optical activity, and rotatory dispersion. Secondly, a complete and simple experiment is proposed to characterize a chiral medium by means of its Mueller matrix for different wavelengths. This experiment can be performed by undergraduate students in a standard optics laboratory. As a particular example of a chiral medium, a quartz crystal is used. The experimental results are compared with those published for crystalline quartz to validate the experiment.

Optical properties of a fs laser-created sphere inside a CYTOP fiber by Mueller polarimetry.

Optical elements embedded in an optical fiber can be used to shape and modulate the light transmitted within. We consistently observe, via Mueller polarimetry, that the optical properties of a femtosecond (fs) laser-created spherical cavity within a perfluorinated fiber exhibit predictable patterns. Specifically, linear birefringence is always induced at the periphery of the cavity, with its value showing a bell-shape distribution. The peak value of LB showed an increase correlating with the laser fluence and power, but its FWHM remains unchanged. Furthermore, it is important to highlight that when the cavity is disrupted, forming a channel to the fiber's surface, a negative LB is observed at the cavity's periphery, with a value reaching up to -0.4 rad. These optical phenomena may pique the interest of engineering and technical fields, potentially inspiring innovative approaches in optical fiber technology and its associated applications.

Time-efficient filtering of imaging polarimetric data by checking physical realizability of experimental mueller matrices.

Imaging Mueller polarimetry has already proved its potential for biomedicine, remote sensing and metrology. The real-time applications of this modality require both video rate image acquisition and fast data post-processing algorithms. First, one must check the physical realizability of the experimental Mueller matrices in order to filter out non-physical data, ie to test the positive semi-definiteness of the 4 × 4 Hermitian coherency matrix calculated from the elements of corresponding Mueller matrix pixel-wise. For this purpose, we compared the execution time for the calculations of i) eigenvalues, ii) Cholesky decomposition, iii) Sylvester's criterion, and iv) coefficients of the characteristic polynomial (two different approaches) of the Hermitian coherency matrix, all calculated for the experimental Mueller matrix images (600 pixels × 700 pixels) of mouse uterine cervix. The calculations were performed using C ++ and Julia programming languages. Our results showed the superiority of the algorithm iv) based on the simplification via Pauli matrices over other algorithms for our dataset. The sequential implementation of latter algorithm on a single core already satisfies the requirements of real-time polarimetric imaging. This can be further amplified by the proposed parallelization (e.g., we achieve a 5-fold speed up on 6 cores). The source codes of the algorithms and experimental data are available at https://github.com/pogudingleb/mueller_matrices.

Near-real-time Mueller polarimetric image processing for neurosurgical intervention.

Wide-field imaging Mueller polarimetry is a revolutionary, label-free, and non-invasive modality for computer-aided intervention; in neurosurgery, it aims to provide visual feedback of white matter fibre bundle orientation from derived parameters. Conventionally, robust polarimetric parameters are estimated after averaging multiple measurements of intensity for each pair of probing and detected polarised light. Long multi-shot averaging, however, is not compatible with real-time in vivo imaging, and the current performance of polarimetric data processing hinders the translation to clinical practice. A learning-based denoising framework is tailored for fast, single-shot, noisy acquisitions of polarimetric intensities. Also, performance-optimised image processing tools are devised for the derivation of clinically relevant parameters. The combination recovers accurate polarimetric parameters from fast acquisitions with near-real-time performance, under the assumption of pseudo-Gaussian polarimetric acquisition noise. The denoising framework is trained, validated, and tested on experimental data comprising tumour-free and diseased human brain samples in different conditions. Accuracy and image quality indices showed significant ( ) improvements on testing data for a fast single-pass denoising versus the state-of-the-art and high polarimetric image quality standards. The computational time is reported for the end-to-end processing. The end-to-end image processing achieved real-time performance for a localised field of view ( ). The denoised polarimetric intensities produced visibly clear directional patterns of neuronal fibre tracts in line with reference polarimetric image quality standards; directional disruption was kept in case of neoplastic lesions. The presented advances pave the way towards feasible oncological neurosurgical translations of novel, label-free, interventional feedback.

Polarization-Based Digital Histology of Human Skin Biopsies Assisted by Deep Learning

Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions.

Mueller-Polarimetry of Barley Leaves II: Mueller Matrix Decompositions

This paper highlights the application of decomposition methods in Mueller polarimetry for the discrimination of three groups of barley leaf samples from Hordeum vulgare: Chlorina mutant, Chlorina etiolated mutant and Cesaer varieties in the visible wavelength at λ = 632.8 nm. To obtain the anisotropic and depolarizing properties of the samples under study, the additive and multiplicative decompositions of experimental Mueller matrices were used. We show how a rich set of anisotropy and depolarization parameters obtained from decompositions can be used as effective observables for the discrimination between different varieties of the same plant species.

Characterization of Polarimetric Properties in Various Brain Tumor Types Using Wide-Field Imaging Mueller Polarimetry.

Neuro-oncological surgery is the primary brain cancer treatment, yet it faces challenges with gliomas due to their invasiveness and the need to preserve neurological function. Hence, radical resection is often unfeasible, highlighting the importance of precise tumor margin delineation to prevent neurological deficits and improve prognosis. Imaging Mueller polarimetry, an effective modality in various organ tissues, seems a promising approach for tumor delineation in neurosurgery. To further assess its use, we characterized the polarimetric properties by analysing 45 polarimetric measurements of 27 fresh brain tumor samples, including different tumor types with a strong focus on gliomas. Our study integrates a wide-field imaging Mueller polarimetric system and a novel neuropathology protocol, correlating polarimetric and histological data for accurate tissue identification. An image processing pipeline facilitated the alignment and overlay of polarimetric images and histological masks. Variations in depolarization values were observed for grey and white matter of brain tumor tissue, while differences in linear retardance were seen only within white matter of brain tumor tissue. Notably, we identified pronounced optical axis azimuth randomization within tumor regions. This study lays the foundation for machine learning-based brain tumor segmentation algorithms using polarimetric data, facilitating intraoperative diagnosis and decision making.

Mueller-Polarimetry of Barley Leaves I: Depolarization Metrics

In this paper, Mueller polarimetry is applied to study the three groups of common barley leaf samples (Hordeum vulgare) in the visible spectrum (λ = 632.8 nm): Chlorina mutant, Chlorina etiolated mutant and Cesaer varieties. These samples differed in internal leaf structure from genetic mutation or by illumination during growth. Our main concern is to discriminate and characterize these three groups of leaf samples by depolarization metrics: degree of polarization (DoP), average degree of polarization (Average DoP), depolarization index (DI(M)), and Q(M) and R(M) metrics. The results obtained show that all depolarization metrics are sensitive to the sample’s polarization properties. The most effective observable is the Q(M) metric in both forward and backward scattering. The DoP metric showed presence of depolarization anisotropy, which is significantly different for forward and backward scattering for all three groups of samples. Dichroism is observed for both forward and backward scattering, with lower dichroism in forward scattering.

Non-destructive latent fingerprint development and background elimination by Mueller Polarimetry

In this article we have explored the use of Mueller polarimetry for the simultaneous and non-destructive latent fingerprint development and background elimination. This proposal not only expands previously reported uses of the polarization state of light for fingerprint development, but offers some advantages. Samples of a few donors, taken on different heavily back grounded substrates, were measured and processed. Several strategies for background removal were applied. The results are very promising, since the background was successfully erased in all cases and the quality of the developed fingerprints was excellent for most substrates and donors. Considering that the method is non-destructive and that the proposed measurement instrument is inexpensive and portable the method could be tried in real cases with no risk for the evidence. In addition, the success of the procedure for background removal raises the possibility of exploring if it would work for the enhancement of fingerprints developed with other methods.

Assessment of the Impact of Nanowarming on Microstructure of Cryopreserved Fibroblast-Containing 3D Tissue Models Using Mueller Polarimetry

We studied the impact of two different thawing mechanisms on the microstructure of defrosted cryopreserved 3D tissue models using transmission Mueller microscopy and a statistical analysis of polarimetric images of thin histological sections of defrosted tissue models. The cryopreserved 3D tissue models were thawed by using either a 37 °C water bath or radio-frequency inductive heating with the magnetic nanoparticles embedded into the 3D tissue model during the preparation process. Polarimetric measurements were conducted at 700 nm and the acquired Mueller matrices of the samples were post-processed using the differential decomposition and the statistical analysis of the maps of the azimuth of the optic axis. Our results indicate the sensitivity of polarimetry to the changes in thawed tissue morphology compared to that of reference non-frozen tissue. Thus, Mueller microscopy can be used as a fast complementary technique to the currently accepted gold standard methods for the assessment of the cryopreserved tissue microstructure after thawing.

Impact of corpus callosum fiber tract crossing on polarimetric images of human brain histological sections: ex vivo studies in transmission configuration.

Imaging Mueller polarimetry is capable to trace in-plane orientation of brain fiber tracts by detecting the optical anisotropy of white matter of healthy brain. Brain tumor cells grow chaotically and destroy this anisotropy. Hence, the drop in scalar retardance values and randomization of the azimuth of the optical axis could serve as the optical marker for brain tumor zone delineation. The presence of underlying crossing fibers can also affect the values of scalar retardance and the azimuth of the optical axis. We studied and analyzed the impact of fiber crossing on the polarimetric images of thin histological sections of brain corpus callosum. We used the transmission Mueller microscope for imaging of two-layered stacks of thin sections of corpus callosum tissue to mimic the overlapping brain fiber tracts with different fiber orientations. The decomposition of the measured Mueller matrices was performed with differential and Lu-Chipman algorithms and completed by the statistical analysis of the maps of scalar retardance, azimuth of the optical axis, and depolarization. Our results indicate the sensitivity of Mueller polarimetry to different spatial arrangement of brain fiber tracts as seen in the maps of scalar retardance and azimuth of optical axis of two-layered stacks of corpus callosum sections The depolarization varies slightly () with the orientation of the optical axes in both corpus callosum stripes, but its value increases by 2.5 to 3 times with the stack thickness. The crossing brain fiber tracts measured in transmission induce the drop in values of scalar retardance and randomization of the azimuth of the optical axis at optical path length of . It suggests that the presence of nerve fibers crossing within the depth of few microns will be also detected in polarimetric maps of brain white matter measured in reflection configuration.

Bayesian inference approach for Full Poincaré Mueller polarimetry

Full Poincaré Mueller Polarimetry is a new technique for characterizing samples by means of their Mueller matrix. The method is based on the use of a full Poincaré beam as a generator of polarization states. These beams present different polarization states, covering the entire Poincaré sphere surface, at different points in the beam cross section. To obtain the Mueller matrix, Stokes parameters are collected at both the entrance and the output of the sample. They are calculated from irradiance measurements at each pixel of a CCD camera for different configurations of the polarization state analyzer. These measurements can be processed in several ways. In this work, we propose to use Bayesian inference, in particular, Markov chain Monte Carlo methods, to obtain, without any prior knowledge of the sample, its Mueller matrix together with its uncertainties. The new approach is tested with experimental measurements of different samples and compared with the real theoretical Mueller matrices. Excellent agreement is observed between the experimental results and the theoretical ones for all the samples tested.

Synthetic Mueller Imaging Polarimetry

The transformation of the state of polarization of a light beam via its linear interaction with a material medium can be modeled through the Stokes–Mueller formalism. The Mueller matrix associated with a given interaction depends on many aspects of the measurement configuration. In particular, different Mueller matrices can be measured for a fixed material sample depending on the spectral profile of the light probe. For a given light probe and a given sample with inhomogeneous spatial behavior, the polarimetric descriptors of the point-to-point Mueller matrices can be mapped, leading to respective polarimetric images. The procedure can be repeated sequentially using light probes with different central frequencies. In addition, the point-to-point Mueller matrices, consecutively measured, can be combined synthetically through convex sums leading to respective new Mueller matrices, in general with increased polarimetric randomness, thus exhibiting specific values for the associated polarimetric descriptors, including the indices of polarimetric purity, and generating new polarimetric images which are different from those obtained from the original Mueller matrices. In this work, the fundamentals for such synthetic generation of additional polarimetric images are described, providing a new tool that enhances the exploitation of Mueller polarimetry.

Increasing signal-to-noise ratio in over-determined Mueller matrices.

This work investigates how the signal-to-noise ratio (SNR) of an over-determined Mueller matrix can be improved by changing the method of calculation. Specifically, our investigation focused on comparing SNRs achieved using the vector methodology from the field of partial Mueller polarimetry, and the matrix methodology. We use experimentally derived measurements from an investigation into the time-varying signal produced by the Mueller matrix of an electro-optic Bismuth Silicon Oxide (BSO) crystal undergoing cyclical impact of a Helium plasma ionisation wave. Our findings show that the vector methodology is superior to the matrix methodology, with a maximum SNR of 7.54 versus 4.97. We put forth that the superiority of the vector methodology is due to its greater flexibility, which results in the Mueller matrix being calculated with better condition matrices, and higher levels of SNR in the intensity measurements used for calculation.

Polarimetric imaging for cervical pre-cancer screening aided by machine learning: ex vivo studies.

Wide-field imaging Mueller polarimetry is an optical imaging technique that has great potential to become a reliable, fast, non-contact in vivo imaging modality for the early detection of, e.g., deceases and tissue structural malformations, such as cervical intraepithelial neoplasia, in both clinical and low-resource settings. On the other hand, machine learning methods have established themselves as a superior solution in image classification and regression tasks. We combine Mueller polarimetry and machine learning, critically assess the data/classification pipeline, investigate the bias arising from training strategies, and demonstrate how higher levels of detection accuracy can be achieved. We aim to automate/assist with diagnostic segmentation of polarimetric images of uterine cervix specimens. A comprehensive capture-to-classification pipeline is developed in house. Specimens are acquired and measured with imaging Mueller polarimeter and undergo histopathological classification. Subsequently, a labeled dataset is created within tagged regions of either healthy or neoplastic cervical tissues. Several machine learning methods are trained utilizing different training-test-set-split strategies, and their corresponding accuracies are compared. Our results include robust measurements of model performance with two approaches: a 90:10 training-test-set-split and leave-one-out cross-validation. By comparing the classifier's accuracy directly with the ground truth obtained during histology analysis, we demonstrate how conventionally used shuffled split leads to an over-estimate of true classifier performance . The leave-one-out cross-validation, however, leads to more accurate performance with respect to newly obtained samples that were not used to train the models. Combination of Mueller polarimetry and machine learning is a powerful tool for the task of screening for pre-cancerous conditions in cervical tissue sections. Nevertheless, there is a inherent bias with conventional processes that can be addressed using more conservative classifier training approaches. This results in overall improvements of the sensitivity and specificity of the developed techniques for "unseen" images.

Effects of formalin fixation on polarimetric properties of brain tissue: fresh or fixed?

Imaging Mueller polarimetry (IMP) appears as a promising technique for real-time delineation of healthy and neoplastic tissue during neurosurgery. The training of machine learning algorithms used for the image post-processing requires large data sets typically derived from the measurements of formalin-fixed brain sections. However, the success of the transfer of such algorithms from fixed to fresh brain tissue depends on the degree of alterations of polarimetric properties induced by formalin fixation (FF). Comprehensive studies were performed on the FF induced changes in fresh pig brain tissue polarimetric properties. Polarimetric properties of pig brain were assessed in 30 coronal thick sections before and after FF using a wide-field IMP system. The width of the uncertainty region between gray and white matter was also estimated. The depolarization increased by 5% in gray matter and remained constant in white matter following FF, whereas the linear retardance decreased by 27% in gray matter and by 28% in white matter after FF. The visual contrast between gray and white matter and fiber tracking remained preserved after FF. Tissue shrinkage induced by FF did not have a significant effect on the uncertainty region width. Similar polarimetric properties were observed in both fresh and fixed brain tissues, indicating a high potential for transfer learning.

Robustness of the wide-field imaging Mueller polarimetry for brain tissue differentiation and white matter fiber tract identification in a surgery-like environment: an ex vivo study.

During neurooncological surgery, the visual differentiation of healthy and diseased tissue is often challenging. Wide-field imaging Muller polarimetry (IMP) is a promising technique for tissue discrimination and in-plane brain fiber tracking in an interventional setup. However, the intraoperative implementation of IMP requires realizing imaging in the presence of remanent blood, and complex surface topography resulting from the use of an ultrasonic cavitation device. We report on the impact of both factors on the quality of polarimetric images of the surgical resection cavities reproduced in fresh animal cadaveric brains. The robustness of IMP is observed under adverse experimental conditions, suggesting a feasible translation of IMP for in vivo neurosurgical applications.

Efficient acquisition of Mueller matrix via spatially modulated polarimetry at low light field.

Mueller polarimetry performed in low light field with high speed and accuracy is important for the diagnosis of living biological tissues. However, efficient acquisition of the Mueller matrix at low light field is challenging owing to the interference of background-noise. In this study, a spatially modulated Mueller polarimeter (SMMP) induced by a zero-order vortex quarter wave retarder is first presented to acquire the Mueller matrix rapidly using only four camera shots rather than 16 shots, as in the state of the art technique. In addition, a momentum gradient ascent algorithm is proposed to accelerate the reconstruction of the Mueller matrix. Subsequently, a novel adaptive hard thresholding filter combined with the spatial distribution characteristics of photons at different low light levels, in addition to a low-pass fast-Fourier-transform filter, is utilized to remove redundant background noise from raw-low intensity distributions. The experimental results illustrate that the proposed method is more robust to noise perturbation, and its precision is almost an order of magnitude higher than that of the classical dual-rotating retarder Mueller polarimetry at low light field.

Microscopic analysis of spin-momentum locking on a geometric phase metasurface

We revisit spin-orbit coupling in a plasmonic Berry metasurface composed of rotated nanoapertures, which is known to imprint a robust far-field polarization response. We present a scattering formalism that shows how that spin-momentum locking emerges from the geometry of the unit cell without requiring global rotation symmetries. We find and confirm with Mueller polarimetry measurements that spin-momentum locking is an approximate symmetry. The symmetry breakdown is ascribed to the elliptical projection of circularly polarized light into the planar surface. This breakdown is maximal when surface waves are excited, and a new set of spin-momentum locking rules is presented for this case.