Estimation of Refractive Index for Biological Tissue Using Micro-Optical Coherence Tomography

Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy

Breast cancer is one of the most spread and monitored pathologies in high-income countries. After breast biopsy, histological tissue is stored in paraffin, sectioned and mounted. Conventional inspection of tissue slides under benchtop light microscopes involves paraffin removal and staining, typically with H&E. Then, expert pathologists are called to judge the stained slides. However, paraffin removal and staining are operator-dependent, time and resources consuming processes that can generate ambiguities due to non-uniform staining. Here we propose a novel method that can work directly on paraffined stain-free slides. We use Fourier Ptychography as a quantitative phase-contrast microscopy method, which allows accessing a very wide field of view (i.e., mm2) in one single image while guaranteeing high lateral resolution (i.e., 0.5 µm). This imaging method is multi-scale, since it enables looking at the big picture, i.e. the complex tissue structure and connections, with the possibility to zoom-in up to the single-cell level. To handle this informative image content, we introduce elements of fractal geometry as multi-scale analysis method. We show the effectiveness of fractal features in describing and classifying fibroadenoma and breast cancer tissue slides from ten patients with very high accuracy. We reach 94.0 ± 4.2% test accuracy in classifying single images. Above all, we show that combining the decisions of the single images, each patient's slide can be classified with no error. Besides, fractal geometry returns a guide map to help pathologist to judge the different tissue portions based on the likelihood these can be associated to a breast cancer or fibroadenoma biomarker. The proposed automatic method could significantly simplify the steps of tissue analysis and make it independent from the sample preparation, the skills of the lab operator and the pathologist.

Coaxial Bright and Dark Field Optical Coherence Tomography

To improve the signal collection efficiency of Optical Coherence Tomography (OCT) for biomedical applications. A novel coaxial optical design was implemented, utilizing a wavefront-division beam splitter in the sample arm with a 45-degree rod mirror. This design allowed for the simultaneous collection of bright and dark field signals. The bright field signal was detected within its circular aperture in a manner similar to standard OCT, while the dark field signal passed through an annular-shaped aperture and was collected by the same spectrometer via a fiber array. This new configuration improved the signal collection efficiency by ∼3 dB for typical biological tissues. Dark-field OCT images were found to provide higher resolution, contrast and distinct information compared to standard bright-field OCT. By compounding bright and dark field images, speckle noise was suppressed by ∼ √2 . These advantages were validated using Teflon phantoms, chicken breast ex vivo, and human skin in vivo. This new OCT configuration significantly enhances signal collection efficiency and image quality, offering great potential for improving OCT technology with better depth, contrast, resolution, speckles, and signal-to-noise ratio. We believe that the bright and dark field signals will enable more comprehensive tissue characterization with the angled scattered light. This advancement will greatly promote the OCT technology in various clinical and biomedical research applications.

PyMieLab_V1.0: A software for calculating the light scattering and absorption of spherical particles

Light scattering and absorption by small particles are widely used in fields such as biomedicine, information technology, and energy technology. However, their theoretical study requires not only a high level of knowledge in electromagnetism but also a high level of computer programming skills. To solve this problem, a software called PyMieLab (https://gitlab.com/Climb12/pymielab.git) for calculating the light scattering and absorption of spherical particles has been developed based on Mie theory. This software is interactive, versatile, visual, flexible, and scalable. It has a friendly graphical user interface and can be used as a numerical simulation platform for scientific research, as well as provides a rich database of particle refractive indices. Moreover, it offers a reliable research platform for discovering new optical properties of specific materials and exploring materials with better optical properties in related fields. This paper describes in detail the theoretical basis, the graphical user interface, the calculation functions, the operational and procedural processes, the features, and the numerical verification of the software. It illustrates the application value of the software with two simulation examples.

Single-Photon Hologram of a Zero-Area Pulse

Single photons exhibit inherently quantum and unintuitive properties such as the Hong-Ou-Mandel effect, demonstrating their bosonic and quantized nature, yet at the same time may correspond to single excitations of spatial or temporal modes with a very complex structure. Those two features are rarely seen together. Here we experimentally demonstrate how the Hong-Ou-Mandel effect can be spectrally resolved and harnessed to characterize a complex temporal mode of a single-photon-a zero-area pulse-obtained via a resonant interaction of a terahertz-bandwidth photon with a narrow gigahertz-wide atomic transition of atomic vapor. The combination of bosonic quantum behavior with bandwidth-mismatched light-atom interaction is of fundamental importance for deeper understanding of both phenomena, as well as their engineering offering applications in characterization of ultrafast transient processes.

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in larger data after exposure to examples; Deep Learning uses neural networks in order to extract meaningful patterns from imaging data, even deciphering that which would otherwise be beyond human perception. Thus, AI has the potential to revolutionize the healthcare systems and clinical practice of doctors all over the world. This is especially true for radiologists, who are integral to diagnostic medicine, helping to customize treatments and triage resources with maximum effectiveness. Related in spirit to Artificial intelligence are Augmented Reality, mixed reality, or Virtual Reality, which are able to enhance accuracy of minimally invasive treatments in image guided therapies by Interventional Radiologists. The potential applications of AI in IR go beyond computer vision and diagnosis, to include screening and modeling of patient selection, predictive tools for treatment planning and navigation, and training tools. Although no new technology is widely embraced, AI may provide opportunities to enhance radiology service and improve patient care, if studied, validated, and applied appropriately.

Refractive index of biological tissues: Review, measurement techniques, and applications

Refractive index (RI) is a characteristic optical variable that controls the propagation of light in the medium (e.g., biological tissues). Basic research with the aim to investigate the RI of biological tissues is of paramount importance for biomedical optics and associated applications. Herein, we reviewed and summarized the RI data of biological tissues and the associated insights. Different techniques for the measurement of RI of biological tissues are also discussed. Moreover, several examples of the RI applications from basic research, clinics and optics industry are outlined. This study may provide a comprehensive reference for RI data of biological tissues for the biomedical research and beyond.

Cell refractive index: Models, insights, applications and future perspectives

Cell refractive index (RI) is an intrinsic optical parameter that governs the propagation of light (i.e., scattering and absorption) in the cell matrix. The RI of cell is sensitively correlated with its mass distribution and thereby has the capability to provide important insights for diverse biological models. Herein, we review the cell refractive index and the fundamental models for measurement of cell RI, summarize the published RI data of cell and cell organelles and discuss the associated insights. Illustrative applications of cell RI in cell biology are also outlined. Finally, future research trends and applications of cell RI, including novel imaging techniques, reshaping flow cytometry and microfluidic platforms for single cell manipulation are discussed. The rapid technological advances in optical imaging integrated with microfluidic regime seems to enable deeper understanding of subcellular dynamics with high spatio-temporal resolution in real time.