Early epithelial dysplasia begins with sub-micron nuclear and architectural changes that white-light endoscopy cannot reveal and random punch biopsies detect only two-thirds of the time. Optical cytology has already transformed skin-cancer care with dermatoscopes and reflectance confocal microscopes, yet the same approach has not reached the cervix or prostate because instrument channels are smaller than 5 mm and existing micro-endoscopes deliver too few resolvable pixels (low space-bandwidth product, SBP). We address both hurdles with a 5 mm-diameter, chip-on-tip quantitative oblique back-illumination (qOBM) micro-endoscope. A GRIN objective (NA 0.79, 2.2x) is bonded to a 720 x 720-pixel OmniVision CMOS sensor, while four 500 mu m fibres sequentially deliver 850 nm illumination for differential phase reconstruction. This endoscope resolves 1.0 mu m laterally over a 0.42 mm field of view and streams quantitative phase videos at 30 fps. We demonstrate its versatility on: (i) fixed human meningioma and oxygen-glucose-deprived brain tissues; (ii)in-vivo human forearm, where epidermal nuclei and capillary flow are visualised. To our knowledge this is the first sub 5 mm endoscope to combine submicron resolution, label-free quantitative phase contrast and video-rate imaging. By steering targeted biopsies or enabling immediate treatment, it could cut procedure time and morbidity in cervical screening and-after a single-fibre (3 mm) redesign-extend to transrectal prostate surveillance.

Accurate intraoperative diagnosis of brain tumors is critical for improving patient outcomes, yet current histopathology is time-consuming and performed outside the operating room. We investigate the use of quantitative oblique back-illumination microscopy (qOBM) for the identification and classification of tumors in brain tumor core samples ex vivo. We studied samples from 28 patients across four tumor types and identified diagnostic features in qOBM images corroborated by H&E. Using samples from 19 glioma patients, a leave-one-out classifier distinguishes high- from low-grade glioma cases with 86.6% accuracy (95% CI: 85.9-87.4%), demonstrating qOBM's potential for real-time intraoperative diagnosis in future studies.

The five-year survival rates for glial brain tumors are extremely variable, ranging from 95% for low-grade astrocytomas to 5% for high-grade glioblastomas. For most brain tumors, accurate diagnosis and resection in the operating room is one of the most important factors in prolonging survival. Currently, the gold standard for brain tumor diagnosis is histopathological analysis, during which tumor samples undergo time-consuming processing outside the operating room. However, there is a lack of intraoperative tools that can successfully identify cancerous tissue in the operating room in-vivo and in real time. Quantitative oblique back-illumination microscopy (qOBM) is a label-free, noninvasive, and real-time imaging modality that has been applied to image several clinical samples at subcellular resolution. This technology has been able to identify cancerous brain tissue in animal models and has been used to image the brain of animal models in-vivo and in real time. Here, we propose to use qOBM as a diagnostic tool in distinguishing glioma tumor types in the human brain. We have imaged ex-vivo human brain samples spanning three tumor types - astrocytoma, glioblastoma, and oligodendroglioma. From these images, we identify visual differences in nuclear morphology between these tumor types. Simultaneously, we compare two machine learning approaches to classify qOBM images by tumor type. First, we train a network to classify by image feature extraction; second, we train a network to classify images by the image data alone. We aim to leverage the superior classification algorithm as we image human brain samples intraoperatively, allowing for in-vivo, real-time tumor diagnosis.

In this work we develop and demonstrate the utility of a compact, handheld quantitative phase imaging microscope that enables label-free, in vivo optical imaging of bulk tissues with clear cellular and subcellular histological detail in real-time. The proposed device overcomes significant challenges in optical imaging for in vivo applications, particularly for clinical human use. The approach uses quantitative oblique back illumination microscopy (qOBM) to obtain quantitative phase information of opaque samples using epi-illumination. The compact handheld probe achieves 0.8 µm lateral resolution, 5 µm axial resolution, 300 µm X 300 µm field of view, and operates at 25Hz in a wide-field (non-scanning) configuration, enabling real-time imaging. The probe is also inexpensive and has no moving components, making it robust. The utility of the probe is demonstrated in (1) human skin in vivo, (2) brain tumor tissue ex vivo from a murine tumor model and from discarded human tissue from neurosurgery, and (3) in vivo using healthy brain tissue from a large animal model (swine), simulating neurosurgical conditions. Given the clear cellular and subcellular histological detail (i.e., "optical biopsy") obtained in real-time, combined with the ease-of-use and low-cost of the system, the proposed device has significant implications for a broad range of clinical applications.

Currently, the field of THz bio-detection faces challenges in terms of sensitivity, efficiency and anti-interference, to which high-sensitivity, multi-channel optical fiber sensors offer a key solution. Here, a high-resolution THz biosensor based on surface plasmon resonance (SPR) is achieved by depositing a MoS2 2 excitation layer on a dual- channel micro-structured fiber (MSF). The dual D-shaped channels are completely separated by support-arm claddings to ensure the detection independence, and the dual-channel introduces self-referencing to reduce the measurement bias caused by environmental changes. Transmission performance of the SPR-MSF sensor is optimized by adjusting its structural design parameters based on a finite element method (FEM). The results show that the proposed SPR-MSF biosensor presents high amplitude sensitivity of up to 384.80 RIU-1 and high wavelength sensitivity of 223.45 mu m/RIU, corresponding to sensing resolution of 4.47 x 10(-7) RIU. Furthermore, the dual-channel wavelength sensitivity and sensing resolution can be improved to 602.52 mu m/RIU and 1.66 x 10(-7) RIU, respectively, with a ZnO thin film deposited in the detection channel. With its unique high sensitivity and high resolution, as well as other advantages provided by fiber-based such as the compactness and biocompatibility, the proposed SPR-MSF bio-sensor has potential applications in biochemical sensing, biomolecule identification, non-invasive cell analysis and many other THz detection fields.

Significance:The acetowhitening effect of acetic acid (AA) enhances light scattering of cell nuclei, an effect that has been widely leveraged to facilitate tissue inspection for (pre)cancerous lesions. Here, we show that a concomitant effect of acetowhitening-changes in refractive index composition-yields nuclear contrast enhancement in quantitative phase imaging (QPI) of thick tissue samples. Aim:We aim to explore how changes in refractive index composition during acetowhitening can be captured through a novel epi-mode 3D QPI technique called quantitative oblique back-illumination microscopy (qOBM). We also aim to demonstrate the potential of using a machine learning-based approach to convert qOBM images of fresh tissues into virtually AA-stained images. Approach:We implemented qOBM, an imaging technique that allows for epi-mode 3D QPI to observe phase changes induced by AA in thick tissue samples. We focus on detecting nuclear contrast changes caused by AA in mouse brain samples. As a proof of concept, we also applied a Cycle-GAN algorithm to convert the acquired qOBM images into virtually AA-stained images, simulating the effect of AA staining. Results:Our findings demonstrate that AA-induced acetowhitening leads to significant nuclear contrast enhancement in qOBM images of thick tissue samples. In addition, the Cycle-GAN algorithm successfully converted qOBM images into virtually AA-stained images, further facilitating the nuclear enhancement process without any physical stains. Conclusions:We show that the acetowhitening effect of acetic acid induces changes in refractive index composition that significantly enhance nuclear contrast in QPI. The application of qOBM with AA, along with the use of a Cycle-GAN algorithm to virtually stain tissues, highlights the potential of this approach for advancing label-free and slide-free, ex vivo, and in vivo histology.

Quantitative oblique back illumination microscopy (qOBM) is a recently developed imaging technique that enables 3D quantitative phase imaging (QPI) and refractive index (RI) tomography of thick scattering samples. To quantify the phase and RI information with qOBM, the optical transfer function (OTF) of the system must be known or estimated, which requires knowledge of the angular distribution of light at an imaging plane inside a highly scattering medium. To date, this information has been estimated using a Monte Carlo photon transport method which relies on documented tissue scattering properties. While this numerical approach has shown high-fidelity quantitative results, it is limited by its dependence on published scattering parameters and simulated conditions. Here we propose a novel approach that allows experimental measurement of the angular distribution of the multiple-scattered light at the imaging plane inside a highly scattering medium. Experimental results using samples with known and unknown scattering properties are presented, including excised brain tissue, in-vivo skin, and formalin-fixed and paraffin-embedded (FFPE) tissues. Results further support qOBM’s quantitative fidelity across different tissue types, and show how directly measuring the angular distribution of light can widen qOBM’s utility to more complex samples with unknown or highly variable scattering properties.

We propose a dual-core, dual-microgroove microstructured fiber biosensor based on surface plasmon resonance (SPR) in the terahertz (THz) region. By coating the two microgrooves with molybdenum disulfide (MoS2) and polyvinylidene fluoride (PVDF), the biosensor enables two distinct and independently tunable SPR peaks. Finite element simulations are used to optimize structural parameters and assess sensing performance. Within the analyte refractive index range of 1.30 to 1.39, the main peak exhibits maximum wavelength and amplitude sensitivities of 467.2 mu m RIU-1 and 101.202 RIU-1, respectively; the secondary peak achieves 625.3 mu m RIU-1 and 113.927 RIU-1. The corresponding figures of merit reach 65.987 RIU-1 (main), and 67.037 RIU-1 (secondary), and demonstrate strong resolution capabilities. This biosensor, featuring an independent dual-resonant peak re-inspection and self-diagnostic mechanism, significantly reduces detection costs and error rates while substantially improving measurement accuracy and interference resistance. The dual-peak response characteristics enable complementary signal enhancement for detecting weak signals and allow real-time instrument status monitoring through analysis of peak position consistency, thereby ensuring result reliability and equipment functionality. It is particularly well-suited for high-risk medical diagnostic applications.

A microstructure optical fiber (MOF) sensor based on surface plasmon resonance (SPR) with an open-loop analyte channel is proposed for detecting malaria parasitic cells. The sensor uses a curved surface structure to bring the excitation layer close to the fiber core to achieve strong coupling. Fiber sensing performance is analyzed using a full-vector finite element method, and structural parameters are optimized to achieve maximum wavelength sensitivity of 257.8 μm/RIU, within an effective refractive index range of 1.33 1.43. Malaria-infected cell sensing results indicate that wavelength sensitivity values in Ring, Trophozoite and Schizont stages are respectively 142.857 μm/RIU, 123.684 μm/RIU and 120.687 μm/RIU, which shows excellent sensitivity compared to previous malaria fiber sensors. Fabrication tolerance analysis is also carried out for important geometrical parameters to confirm sensor feasibility. This SPR-MOF biosensor in THz has advantages of high sensitivity, small size, low cost and being non-destructive compared to conventional malaria sensors.

Phase imaging and fluorescence microscopy provide valuable complementary information, and individually form the basis for a significant portion of the routing biological and biomedical optical imaging performed today. While multimodal phase and fluorescence microscopy has been explored for thin transparent samples to obtain structural information based on the refractive index distribution (with phase contrast) and molecular content (with fluorescence), combining these complementary technologies to study thick samples has been challenging and remains largely unexplored. This work presents the results of a study that combines quantitative phase imaging (QPI) and refractive index (RI) tomography in thick samples—using quantitative oblique back illumination—and bright field fluorescence deconvolution microscopy. The two technologies use a simple bright field microscope configuration with epi-illumination and through-focus z-stack acquisition, along with a deconvolution algorithm, to achieve 3D imaging. Phase and RI information is acquired nearly simultaneously with the fluorescence information with inherent co-registration of the two modalities. In this work, we will present the theoretical underpinning of this multimodal approach, describe the simple multimodal system, and show imaging results of thick tissues, such as labeled mice brains. This multimodal imaging approach could help biologists and clinicians gain a more comprehensive understanding of the tissue's morphology and molecular composition, and can be widely applied across a number of biological and biomedical disciplines, including neuroscience, pathology, and oncology.

Quantitative phase imaging (QPI) offers label-free access to refractive index information of biological samples, which can achieve nanometer-level optical-path-length sensitivity with cellular/sub-cellular biophysical and histological details. Recently we introduced quantitative oblique back-illumination microscopy (qOBM) which works in epi-mode and uses multiply scattered photons within thick samples to yield quantitative phase in thick scattering tissues, thus overcoming QPI's long-standing limitation to thin transparent samples. qOBM provides real-time quantitative phase in 3D, and can be configured in a compact form factor. Here we describe a handheld qOBM probe, suitable for in-vivo diagnostic applications such as brain tumor assessment, dermatology, and more.

Mid‐infrared (mid‐IR) ultrafast lasers are widely employed in biomedicine, molecular spectroscopy, material processing, and nonlinear optics. With the improvement of fiber gain media and other fiber optical elements, low‐cost, compact, and high‐efficiency fiber lasers open up new opportunities for 2–4 µm pulse generations, which calls for a comprehensive review of their mode‐locking mechanisms, gain media, and fiber laser system performance. This paper, beginning with an overview of pulse‐generation technologies, reviews recent progress on 2–4 µm mid‐IR ultrafast fiber lasers, including Tm 3+ ‐, Ho 3+ ‐doped, Tm 3+ /Ho 3+ codoped silicate fiber 2 µm lasers, and Er 3+ ‐, Dy 3+ ‐doped, and Ho 3+ /Pr 3+ codoped ZBLAN fiber 2.5–4 µm lasers. Among them, the status of 2–4 µm ultrafast fiber lasers based on 2D material passive mode‐locking is emphatically discussed. Meanwhile, the novel advances on mode‐locking and gain fibers of mid‐IR ultrafast fiber lasers are explored. Furthermore, current and prospective applications of such laser systems are also introduced in detail. This review finally summarizes challenges associated with future development of mid‐IR ultrafast fiber lasers, which provides an outlook on how to achieve more desirable laser performance (e.g., higher average power, higher pulse energy, and longer emission wavelength) that can lead to more practical uses of such lasers.

Quantitative oblique back-illumination microscopy (qOBM) is a novel imaging technology that enables epi-mode 3D quantitative phase imaging and refractive index (RI) tomography of thick scattering samples. The technology uses four oblique back illumination images captured at the same focal plane and a fast 2D deconvolution reconstruction algorithm to reconstruct 2D phase cross-sections of thick samples. Alternatively, a through-focus z-stack of oblique back illumination images can be used to recover 3D RI tomograms with improved RI quantitative fidelity at the cost of a more computationally expensive reconstruction algorithm. Here, we report on a generative adversarial network (GAN) assisted approach to reconstruct 3D RI tomograms with qOBM that achieves high fidelity and greatly reduces processing time. The proposed approach achieves high-fidelity 3D RI tomography using differential phase contrast images from three adjacent z-planes. A ∼9-fold improvement in volumetric reconstruction time is achieved. We further show that this technique provides high SNR RI tomograms with high quantitative fidelity, reduces motion artifacts, and generalizes to different tissue types. This work can lead to real-time, high-fidelity RI tomographic imaging for in-vivo pre-clinical and clinical applications.

Quantitative oblique-back-illumination microscopy (qOBM) enables quantitative phase imaging (QPI) with epi-illumination, and thus permits the use of phase contrast in applications that were previously out-of-reach for QPI, including clinical medicine. Here, I will discuss our latest efforts to apply qOBM for clinical applications, specifically tissue imaging for non-invasive diagnostics and image guided therapy. Our approach uses an unsupervised cycle generative adversarial networks to translate 3D phase images of thick fresh tissues to appear like H&E-stained tissue sections. This work paves the way for non-invasive, label-free, real-time 3D H&E imaging which can be transformative for disease detection and guided therapy.

Currently, dermatologic diagnosis requires lengthy histopathologic analysis, elongating a patient’s time to diagnosis. We present quantitative oblique back-illumination microscopy (qOBM) as a label-free, low-cost, compact solution providing real-time epider-mal diagnostic information at the dermatologist’s bedside.

Quantitative phase imaging (QPI) has emerged as a valuable method in biomedical research by providing label-free, high-resolution phase distribution of transparent cells and tissues. While QPI is limited to transparent samples, quantitative oblique back-illumination microscopy (qOBM) is a novel imaging technology that enables epi-mode 3D quantitative phase imaging and refractive index (RI) tomography of thick scattering samples. This technology employs four oblique back illumination images taken at the same focal planes, along with a rapid 2D deconvolution reconstruction algorithm, to generate 2D phase cross-sections of thick samples. Alternatively, a through-focus z-stack of oblique back illumination images can be utilized to produce 3D RI tomograms, offering enhanced RI quantitative accuracy. However, 3D RI generation requires a more computationally intensive reconstruction process, preventing its potential of a real-time 3D RI tomography. In this paper, we propose a neural network-involved reconstruction technique that significantly reduces the processing time to a third while maintaining high fidelity compared to the deconvolution-based results.

Pancreatic cancer is a kind of malignant tumor that is difficult to detect in its early stages, developing rapidly and with a 5-year survival rate of only 5% to 10%. Therefore early diagnosis and discovery of pancreatic cancer are very important for the successful treatment of the disease. Here, we report a single hollow-core microstructural fiber (SHC-MSF) biosensor based on a ZEONEX substrate, which has been optimized for the early detection of pancreatic cancer biomarkers. The proposed SHC-MSF biosensor adopts a single-aperture structure to increase the contact range with assay analytes to improve the detection sensitivity. Its biosensing performance was numerically analyzed using a finite element method with a perfect matching layer. Numerical results demonstrated that the proposed MSF-biosensor presented ultra-high sensitivity (bilirubin: 105.55%, glucose: 105.34%, creatinine: 105.67%) and negligible confinement loss (bilirubin: 5.52 x 10-14 cm-1, glucose: 1.65 x 10-14 cm-1, creatinine: 5.57 x 10-14 cm-1) in the range of 0.3 similar to 2.0 THz. Moreover, the SHC-MSF biosensor could selectively detect and distinguish cancer markers of different concentrations in the blood to achieve a more accurate diagnosis of pancreatic cancer. Finally, fabrication tolerance analysis of the proposed MSF-biosensor is provided to ensure the feasibility of rapid preparation.

Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the process is laborious and time-consuming, often limiting its usage in crucial applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.