Rekha Gautam

We present a real-valued intensity transmission matrix (RVITM) framework that enables phase-free structured illumination control through multimode fibers, overcoming long-standing challenges of modal scrambling from dispersion and bending. Unlike previous TM-based approaches that require complex-field measurements or interferometric detection, our method relies solely on 16 × 16 Hadamard intensity inputs, enabling deterministic synthesis of circularly symmetric illumination patterns that remain stable under fiber bending. To the best of our knowledge, this is the first demonstration of RVITM-based ring illumination through a multimode fiber for Raman spectroscopy and the first proof-of-concept application to inverse spatially offset Raman spectroscopy (iSORS). Using a 3 mm-radius ring pattern for iSORS of a two-layer phantom, the subsurface-to-surface Raman peak ratio was enhanced by 1.5 compared with point illumination, confirming improved depth sensitivity without added system complexity. RVITM offers a calibration-friendly and mechanically robust route to fiber-delivered structured illumination, advancing minimally invasive spectroscopy and imaging applications.

Raman spectroscopy is a noninvasive technique providing detailed molecular insights into tissue composition, enabling applications in diagnostics and therapy monitoring. However, Raman signals are inherently weak and prone to superimposed background signals, making baseline correction, a critical step to remove background signals, essential. Typically, experts assess the quality of baseline fitting visually, which is subjective, time-consuming, and impractical with large clinical data sets. To address this, we propose the IS (Integrity Spectrum) Score, a quantitative and automatic metric that evaluates baseline correction quality directly from Raman spectra. The algorithm leverages several spectral properties: it measures the distance between the baseline and the band’s prominence, quantifies the Raman shift distances within band edges using a band prominence–proportional factor, compares area-under-the-curve differences between the raw spectrum and an intentionally overfitted baseline, and counts dips where the baseline rises above the spectral curve. These combined indicators capture both underfitting and overfitting behaviors without requiring ground truth labels or manual inspection. Results obtained using complex two-layered blood cell data sets show a strong correlation between IS-Score and expert evaluations, while substantially reducing the time required from experts. This method supports multicenter studies by facilitating consistent baseline correction, reducing labor, and minimizing subjective bias, ultimately enhancing the reliability and comparability of Raman data for clinical research.

Bone-related disorders, including osteoporosis, lead to decreased bone density and altered microarchitecture, significantly increasing the risk of fragility fracture. There is an urgent need for innovative early detection methods, as conventional techniques like dual-energy x-ray absorptiometry offer limited insights into bone quality, often overlooking critical microstructural changes. In this study, we present a novel dual-wavelength inverse spatially offset Raman spectroscopy (DWiSORS) system specifically designed to enhance the assessment of transcutaneous bone signals. This innovative approach utilises ring illumination to deliver adequate power while minimising the fluorescence background, and dual-wavelength excitation to capture a broader molecular profile (spectral range). We introduce a novel metric, enhancement-to-noise ratio (ENR), which provides a quantitative and robust strategy to identify the optimal offset for accurate assessment at specified depths, addressing a significant limitation in existing in-vivo SORS methodologies. The metric efficacy was validated through in-vivo measurements on ten healthy volunteers, where the ENR was analysed against increasing source-detector offsets, with tissue thickness determined by ultrasound imaging. Notably, optimal signal-to-noise ratio for bone signals were achieved at offsets of 7 or 9 mm, even beneath 10–14 mm of overlaying tissue, reinforcing increasing the offset beyond this threshold does not necessarily enhance bone signals detection. In-vivo measurements demonstrate the technical feasibility and robustness of the proposed DWiSORS framework for depth-resolved bone Raman measurements. This study indicates the potential of Raman spectroscopy to complement conventional imaging techniques and support future approaches for non-invasive assessment and longitudinal monitoring of bone-related disorders.

Crohn's disease (CD) is typically diagnosed through endoscopy and histological analysis of biopsies. Currently, specific noninvasive biomarkers for accurate diagnosis and monitoring of CD remain unavailable. This study investigates Raman spectroscopy (RS) as a noninvasive diagnostic and monitoring tool for CD. Plasma and biopsy samples from 66 participants (55—CD, 11—controls) were analyzed to identify disease‐specific molecular alterations. RS revealed significant spectral differences ( p < 0.05) in amino acids, proteins, carbohydrates, and carotenoids. Decreased valine and arginine levels were consistent with mucosal damage and inflammation, supported by reduced carbohydrate signals indicating barrier impairment. Carotenoid depletion reflected oxidative stress‐related inflammation. Correlations between plasma and tissue spectra suggested that plasma RS can reflect local tissue inflammation. Despite the small and heterogeneous cohort, RS detected biochemical shifts toward healthy profiles following biologic therapy. These findings support RS as a minimally invasive approach for detecting inflammation‐associated molecular alterations, holding potential to guide personalized disease management.

In vivo Raman spectroscopy (RS) enables fast, label-free evaluation of tissue biochemistry in situ with high molecular specificity. The Raman spectrum provides a chemical ‘fingerprint’ of tissue composition, facilitating investigations of dynamic changes in real-time in various physiological and pathophysiological states. This capability makes in vivo RS a promising approach for rapid diagnostics, surgical guidance and biological research. Despite its numerous advantages, the widespread acceptance of RS for in vivo measurements has been hindered by the lack of a standardized stepwise protocol. This protocol serves as a guide for applying RS in vivo and includes steps for proper instrument selection, system alignment, calibration, system parameter setup, in vivo data collection, instrument cleaning, spectral pre-processing, data analysis and interpretation. Troubleshooting information is described for overcoming challenges in acquiring in vivo RS data due to inherently weak Raman signals, variable tissue optical properties, autofluorescence background and interference from ambient lighting and off-target tissues. Specific steps for applying in vivo RS in the skin, cervix, esophagus and colon are described and can be readily adapted to probe other organs. Typical parameters for acquiring and processing in vivo Raman spectra, as well as example spectral output from different organs, are provided for reference. Ultimately, this standardized protocol serves as a guideline to enhance the repeatability of in vivo RS studies and further expand the adoption of this approach as a research and clinical tool.

Background: Raman Spectroscopy is a non-invasive technique capable of characterising tissue constituents and detecting conditions such as cancer with high accuracy. Machine learning techniques can automate this task and discover relevant data patterns. However, the high-dimensional, multicollinear nature of Raman data makes their deployment and explainability challenging. A model’s transparency and ability to explain decision pathways have become crucial for medical integration. Consequently, an effective method of feature-reduction while minimising information loss is sought. Methods: Two new feature selection methods for Raman spectroscopy are introduced. These methods are based on explainable deep learning approaches, considering Convolutional Neural Networks and Transformers. Their features are extracted using GradCam and attention scores, respectively. The performance of the extracted features is compared to established feature selection approaches across four classifiers and three datasets. Results: We compared the proposed method against established feature selection approaches over three real-world datasets and different compression levels. Comparable accuracy levels were obtained using only 10% of features. Model-based approaches are the most accurate. Using Convolutional Neural Networks and Random Forest-assigned feature importance performs best when maintaining between 5–20% of features, while LinearSVC with L1 penalisation leads to higher accuracy when selecting only 1% of them. The proposed Convolutional Neural Networks-based GradCam approach has the highest average accuracy. Conclusions: No approach is found to perform best in all scenarios, suggesting that multiple alternatives should be assessed in each application.

This study investigates how filtration affects the biochemical composition of human saliva, aiming to standardize protocols and enhance the accuracy of Raman spectral analysis for diagnostic applications.

Phantoms simultaneously mimicking anatomical and optical properties of real tissues can play a pivotal role for improving dosimetry algorithms. The aim of the paper is to design and develop a hybrid phantom model that builds up on the strengths of solid and liquid phantoms for mimicking various anatomical structures for prostate cancer photodynamic therapy (PDT) dosimetry validation. The model comprises of a photosensitizer-embedded gelatin lesion within a liquid Intralipid prostate shape that is surrounded by a solid silicone outer shell. The hybrid phantom was well characterized for optical properties. The final assembled phantom was also evaluated for fluorescence tomographic reconstruction in conjunction with SpectraCure’s IDOSE software. The developed model can lead to advancements in dosimetric evaluations. This would improve PDT outlook as a clinical treatment modality and boost phantom based standardization of biophotonic devices globally.

This study developed gelatin-based phantoms optimized across a broad range of optical properties. These well-characterized, multi-biomarker phantoms have the potential to expedite the development of multimodal optical spectroscopy devices for clinical translation.