There is an urgent need for improved techniques for disease detection. Optical spectroscopy and imaging technologies have potential for non- or minimally-invasive use in a wide range of clinical applications. The focus here, in vivo Raman spectroscopy (RS), measures inelastic light scattering based on interaction with the vibrational and rotational modes of common molecular bonds in cells and tissue. The Raman 'signature' can be used to assess physiological status and can also be altered by disease. This information can supplement existing diagnostic (e.g. radiological imaging) techniques for disease screening and diagnosis, in interventional guidance for identifying disease margins, and in monitoring treatment responses. Using fiberoptic-based light delivery and collection, RS is most easily performed on accessible tissue surfaces, either on the skin, in hollow organs or intra-operatively. The strength of RS lies in the high biochemical information content of the spectra, that characteristically show an array of very narrow peaks associated with specific chemical bonds. This results in high sensitivity and specificity, for example to distinguish malignant or premalignant from normal tissues. A critical issue is that the Raman signal is often very weak, limiting clinical use to point-by-point measurements. However, non-linear techniques using pulsed-laser sources have been developed to enable in vivo Raman imaging. Changes in Raman spectra with disease are often subtle and spectrally distributed, requiring full spectral scanning, together with the use of tissue classification algorithms that must be trained on large numbers of independent measurements. Recent advances in instrumentation and spectral analysis have substantially improved the clinical feasibility of RS, so that it is now being investigated with increased success in a wide range of cancer types and locations, as well as for non-oncological conditions. This review covers recent advances and continuing challenges, with emphasis on clinical translation.

We present an asymmetric double-clad fiber coupler (A-DCFC) exploiting a disparity in fiber etendues to exceed the equipartition limit (≤50% extraction of inner cladding multi-mode light). The A-DCFC is fabricated using two commercially available fibers and a custom fusion-tapering setup to achieve >70% extraction of multi-mode inner cladding light without affecting (>95% transmission) single-mode light propagation in the core. Imaging with the A-DCFC is demonstrated in a spectrally encoded imaging setup using a weakly backscattering biological sample. Other applications include the combination of optical coherence tomography with weak fluorescent or Raman scattering signals.

We present a novel optical fiber surface plasmon resonance (SPR) sensor scheme using reflected guided cladding modes captured by a double-clad fiber coupler and excited in a gold-coated fiber with a tilted Bragg grating. This new interrogation approach, based on the reflection spectrum, provides an improvement in the operating range of the device over previous techniques. The device allows detection of SPR in the reflected guided cladding modes and also in the transmitted spectrum, allowing comparison with standard techniques. The sensor has a large operating range from 1.335 to 1.432 RIU, and a sensitivity of 510.5 nm/RIU. The device shows strong dependence on the polarization state of the guided core mode which can be used to turn the SPR on or off.

Optical coherence tomography (OCT) yields microscopic volumetric images representing tissue structures based on the contrast provided by elastic light scattering. Multipatient studies using OCT for detection of tissue abnormalities can lead to large datasets making quantitative and unbiased assessment of classification algorithms performance difficult without the availability of automated analytical schemes. We present a mathematical descriptor reducing the dimensionality of a classifier's input data, while preserving essential volumetric features from reconstructed three-dimensional optical volumes. This descriptor is used as the input of classification algorithms allowing a detailed exploration of the features space leading to optimal and reliable classification models based on support vector machine techniques. Using imaging dataset of paraffin-embedded tissue samples from 38 ovarian cancer patients, we report accuracies for cancer detection [Formula: see text] for binary classification between healthy fallopian tube and ovarian samples containing cancer cells. Furthermore, multiples classes of statistical models are presented demonstrating [Formula: see text] accuracy for the detection of high-grade serous, endometroid, and clear cells cancers. The classification approach reduces the computational complexity and needed resources to achieve highly accurate classification, making it possible to contemplate other applications, including intraoperative surgical guidance, as well as other depth sectioning techniques for fresh tissue imaging.

Double-clad fibers (DCF) have many advantages in fibered confocal microscopes as they allow for coherent illumination through their core and partially coherent detection through their inner cladding. We report a double-clad fiber coupler (DCFC) made from small inner cladding DCF that preserves optical sectioning in confocal microscopy while increasing collection efficiency and reducing coherent effects. Due to the small inner cladding, previously demonstrated fabrication methods could not be translated to this coupler's fabrication. To make such a coupler possible, we introduce in this article three new design concepts. The resulting DCFC fabricated using two custom fibers and a modified fusion-tapering technique achieves high multimodal extraction (≥70 %) and high single mode transmission (≥80 %). Its application to reflectance confocal microscopy showed a 30-fold increase in detected signal intensity, a 4-fold speckle contrast reduction with a penalty in axial resolution of a factor 2. This coupler paves the way towards more efficient confocal microscopes for clinical applications.