Removing non-resonant background from broadband CARS using a physics-informed neural network.

Broadband coherent anti-Stokes Raman scattering (BCARS) is capable of producing high-quality Raman spectra spanning broad bandwidths, 400-4000 cm-1, with millisecond acquisition times. Raw BCARS spectra, however, are a coherent combination of vibrationally resonant (Raman) and non-resonant (electronic) components that may challenge or degrade chemical analyses. Recently, we demonstrated a deep convolutional autoencoder network, trained on pairs of simulated BCARS-Raman datasets, which could retrieve the Raman signal with high quality under ideal conditions. In this work, we present a new computational system that incorporates experimental measurements of the laser system spectral and temporal properties, combined with simulated susceptibilities. Thus, the neural network learns the mapping between the susceptibility and the measured response for a specific BCARS system. The network is tested on simulated and measured experimental results taken with our BCARS system.

Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification.

Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra.

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky-Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky-Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

Quantitative Polarization-Resolved Second-Harmonic-Generation Microscopy of Glycine Microneedles.

Second-harmonic generation (SHG) is a nonlinear optical process that can provide disease diagnosis through characterization of biological building blocks such as amino acids, peptides, and proteins. The second-order nonlinear susceptibility tensor χ(2) of a material characterizes its tendency to cause SHG. Here, a method for finding the χ(2) elements from polarization-resolved SHG microscopy in transmission mode is presented. The quantitative framework and analytical approach that corrects for micrometer-scale morphology and birefringence enable the determination and comparison of the SHG susceptibility tensors of ß- and γ-phase glycine microneedles. The maximum nonlinear susceptibility coefficients are d33  = 15 pm V-1 for the ß and d33  = 5.9 pm V-1 for the γ phase. The results demonstrate glycine as a useful biocompatible nonlinear material. This combination of the analytical model and polarization-resolved SHG transmission microscopy is broadly applicable for quantitative SHG material characterization and diagnostic imaging.

Label-free color staining of quantitative phase images of biological cells by simulated Rheinberg illumination.

Modern microscopes are designed with functionalities that are tailored to enhance image contrast. Dark-field imaging, phase contrast, differential interference contrast, and other optical techniques enable biological cells and other phase-only objects to be visualized. Quantitative phase imaging refers to an emerging set of techniques that allow for the complex transmission function of the sample to be measured. With this quantitative phase image available, any optical technique can then be simulated; it is trivial to generate a phase contrast image or a differential interference contrast image. Rheinberg illumination, proposed almost a century ago, is an optical technique that applies color contrast to images of phase-only objects by introducing a type of optical staining via an amplitude filter placed in the illumination path that consists of two or more colors. In this paper, the complete theory of Rheinberg illumination is derived, from which an algorithm is proposed that can digitally simulate the technique. Results are shown for a number of quantitative phase images of diatom cells obtained via digital holographic microscopy. The results clearly demonstrate the potential of the technique for label-free color staining of subcellular features.

Spatial-domain filter enhanced subtraction microscopy and application to mid-IR imaging.

We have experimentally investigated the enhancement in spatial resolution by image subtraction in mid-infrared central solid-immersion lens (c-SIL) microscopy. The subtraction exploits a first image measured with the c-SIL point-spread function (PSF) realized with a Gaussian beam and a second image measured with the beam optically patterned by a silicon π-step phase plate, to realize a centrally hollow PSF. The intense sides lobes in both PSFs that are intrinsic to the SIL make the conventional weighted subtraction methods inadequate. A spatial-domain filter with a kernel optimized to match both experimental PSFs in their periphery was thus developed to modify the first image prior to subtraction, and this resulted in greatly improved performance, with polystyrene beads 1.4 ± 0.1 µm apart optically resolved with a mid-IR wavelength of 3.4 µm in water. Spatial-domain filtering is applicable to other PSF pairs, and simulations show that it also outperforms conventional subtraction methods for the Gaussian and doughnut beams widely used in visible and near-IR microscopy.

Doubling the far-field resolution in mid-infrared microscopy.

The spatial resolution in far-field mid-infrared (λ>2.5 µm) microscopy and micro-spectroscopy remains limited with the full-width at half maximum of the point-spread function ca. λ/1.3; a value that is very poor in comparison to that commonly accessible with visible and near-infrared optics. Hereafter, it is demonstrated however that polymer beads that are centre-to-centre spaced by λ/2.6 can be resolved in the mid-infrared. The more than 2-fold improvement in resolution in the far-field is achieved by exploiting a newly constructed scanning microscope built around a mid-infrared optical parametric oscillator and a central solid-immersion lens, and by enforcing the linear polarization unidirectional resolution enhancement with a novel and robust specimen error minimization based on a particle swarm optimization. The method is demonstrated with specimens immersed in air and in water, and its robustness shown by the analysis of dense and complex self-assembled bead islands.

Radiographic assessment of biomechanical parameters following hip resurfacing and cemented total hip arthroplasty.

Resurfacing hip arthroplasty and total hip replacement both aim to restore anatomical parameters.Leg length and offset discrepancy can result in altered joint reaction forces, and are associated with increased wear, dislocation, and decreased patient satisfaction. This study assesses the accuracy of leg length and offset restoration after either a Birmingham Hip Resurfacing (BHR) or a cemented total hip replacement (THR).Standardised antero-posterior radiography was performed on two groups of 30 patients with unilateral primary osteoarthritis undergoing either a cemented total hip or resurfacing. The normal contra-lateral hip was used as the control. Leg length and offset were measured pre-operatively with no significant difference between the two groups.Cup offset, femoral offset, total offset and leg length of the prosthesis and normal side were measured by two observers and mean measurements were analysed by a paired t test.Leg lengths in each group did not differ significantly from the normal side, THR 0.53 mm (95% CI -2.4 to 3.4 mm) but BHR implantation did result in mean leg shortening of -1.9 mm (95% CI -4.5 mm to 0.6mm). Cup offset differed significantly from normal anatomy in both groups, as did femoral and total offset for the total hip replacement group. However, femoral offset was restored in the Birmingham resurfacing group. When the THR group was compared against the BHR group we found no difference between restoration of leg lengths (p = 0.21) and cup offset (p = 0.30) but femoral (p = 0.0063) and total offset (p = 0.03) were restored more accurately with a BHR.