Clinical Biophotonics III

Thermal therapies are energy-based interventions that provide a minimally invasive treatment option for cancer patients who may not be surgical candidates. Being these therapies based on the increase of tissue temperature to achieve tumor necrosis, it is important to assure complete tumor destruction and simultaneous safety by minimizing collateral thermal damage. Thus, the availability of accurate tools for monitoring tissue temperature changes during the treatment and its control represents an important clinical need. This work presents an overview of two main optical tools (i.e., fiber optic sensors and hyperspectral imaging) which are under investigation for thermometry purposes in tumors and organs undergoing thermal therapies.

High resolution optical in vivo skin biopsies with subcellular resolution and metabolic information can be provided by mutimodal multiphoton tomography. We report on the use of an ultracompact air-chilled 50 MHz femtosecond fiber laser for two-photon autofluorescence excitation, generation of higher harmonics, fluorescence lifetime imaging, and confocal reflectance imaging. The laser resonator is placed in a 360° imaging head without the need of an optical arm The energy consumption of the whole tomograph is 250 Watt only. Therefore, the imaging system can be used with batteries even in remote areas and can be charged with foldable flexible photovoltaics. In vivo results on the detection of malignant melanoma as well on the effects of cosmetics in human skin are presented.

Surgical resection of skin cancer implies safety margins delineation: currently, surgeons have no diagnostic aid to narrow or widen such margins if necessary. A promising approach is the use of optical methods, which can be used non-invasively and offer real-time diagnostic assistance. This study presents the results of classification of autofluorescence (AF) and diffuse reflectance (DR) spectra obtained in vivo on skin Basal Cell Carcinomas (BCC) and Squamous Cell Carcinomas (SCC), Actinic Keratoses (AK) and Healthy skin (H) of 140 patients. The bimodal spectroscopic instrument used in this study uses five LEDs for fluorescence excitation at wavelengths peaks between 365 and 415 nm, and a xenon lamp featuring 350-800 nm emission range to obtain AF and DR spectra for four source-detector distances (from 400 to 1000 µm). The classification (C vs H, H vs AK) was done by support vector machine, discriminant analysis, and multilayer perceptron. Final accuracy of two-class classification tests for almost all pairs of classes was more than 80%. This study presents a comparison of the performance of these combination of methods with the standard clinical procedure.

Vulvar skin, distinct from other areas, undergoes changes due to aging, causing symptoms like dryness and itchiness. While much research focuses on facial or forearm skin, vulva skin properties are underexplored. This study uses an in-house developed fiber-based Diffuse Reflectance Spectroscopy (DRS) system to assess vulva skin changes in 100 women. This objective evaluation includes analyzing tissue chromophores—water, lipid, oxyhemoglobin, and deoxyhemoglobin providing insights into moisture content, lipid levels, oxygen saturation, and blood fraction. DRS, compared to invasive methods, achieved a 65% accuracy in estimating estrogen levels, suggesting its potential for objective diagnosis and monitoring of genitourinary skin conditions.

In the presence of diabetes mellitus (DM), the wound healing process is interrupted and chronic wounds develop. This study investigated the molecular effect of PBM on protein expression associated with the canonical WNT/β-catenin pathway in diabetic wounded cells in vitro. Cells were irradiated using a diode laser at a wavelength of 660 nm, power output of 100 mW/cm2 and a fluence of 5 J/cm2. Non-irradiated cells were used as controls. After 48 h post PBM, PCR profiler array assay was used to analyse 44 genes related to the canonical WNT/β-catenin pathway beside the immunological assays to quantitatively and qualitatively assess for β catenin. PBM upregulated 11 genes, downregulated 33 genes, and significantly increased β catenin in diabetic wounded fibroblast cells.

SURGICAL OPTOMICS is a new -omics science that combines the study of light-tissue interactions and unique properties of photons with the use of advanced ML and DL algorithms to precisely characterize organs involved in the surgical scene and extract quantifiable tissue features and functions during the surgical procedure. Intraoperative optical technologies such as Near-Infrared Fluorescence imaging, multispectral or hyperspectral imaging enable an improved visualization of unapparent anatomical structures, the evaluation of metabolic activities and the enhanced visualization of tumor tissue, when compared to white light evaluation alone. Thanks to some groundbreaking innovations, optical imaging can well be a powerful theranostic tool that can help tackling the challenges of surgical oncology: to ensure a complete removal of tumor tissue and to reduce the risk of surgical complications. An extensive intelligence and networking activity, including main opinion leaders in the field, has allowed identifying 4 major axes of development of optical imaging, including: 1) SOFTWARE: the integration of computer-assisted interpretation of the optically generated signal through dedicated software solution and Artificial Intelligence, machine and deep learning approaches, towards the building of an OPTOMICS paradigm, in analogy with other omics (genomics, proteomics, metabolomics, radiomics). 2) HARDWARE: the development of improved hardware solutions, with optimized sensitivity and improved ergonomics. 3) CHEMISTRY: the development of innovative probes, which recognize precisely biological targets or tumor cells and allow for image-guided removal of cancers by focused energy delivery or surgical ablation. 4) TECHNIQUES: improvement of state-of-the-art techniques (surgical or interventional) by the implementation of optical imaging AND development of innovative minimally invasive organ-sparing techniques specifically enabled by optical imaging and surgical robotics.

One in three people living with diabetes are affected by diabetic foot ulcers (DFUs). To avoid amputations, screenings are in place that involve monitoring ischemia. However, these screenings have low patient compliance. Home monitoring is subject to active research, but the devices currently available still require skilled operators. A multispectral imager has been developed to allow people living with diabetes to monitor their feet at home. The device is ultra-low-cost and is easy to use. Tissue oxygenation maps have been produced on the foot of a healthy volunteer, where circulation has been restricted with a band. Future work will assess the usability of the device, its clinical value in at-home monitoring of DFUs and impact on screening compliance.

We have been investigating Optical Coherence Tomography (OCT) as a tool to measure the tympanic membrane and middle ear morphology and vibrational response. The hand-held OCT ostoscope system, based on a 1.3 µm swept laser, is integrated into an endoscopy cart. It has an ~ 8 mm diameter field of view, 38 µm lateral resolution, 35 µm axial resolution, A-line rate of 200 kHz, and subnanometer sensitivity to vibration within the tympanic membrane and middle ear. The system has been used in the clinic at USC Keck Medical Center to image over 100 patients and healthy volunteers. Total imaging time is ~2 minutes, which allows it to easily fit into the clinic workflow, while providing high-resolution images and vibrometric assessment of the tympanic membrane and middle ear. The functional and morphological features visible within these image sets that allow us to readily differentiate among pathologies, will be discussed.

Doppler holography, employing high-speed digital imaging and near-infrared light, maps blood flow in the eye's fundus. It can be exploited to estimate quantitatively the velocity and volume of blood flow by assessing the Doppler frequency broadening increase in retinal arteries with respect to local surrounding tissue. This technique enhances our ability to gauge hemodynamics within these arteries across the cardiac cycle, crucial for ocular disease diagnosis and management. Infrared radiation scatters and broadens within the retina's deeper layers, aiding the analysis of blood flow in superficial retinal vessels. Light interaction with blood scatterers is quantified to estimate flow velocity, using a model of forward scattering for momentum transfer. The root-mean-square velocity reflects the degree of Doppler broadening, allowing for a detailed assessment of retinal hemodynamics. This approach provides a valuable tool for analysing ocular vascular health.

Macroscale fluorescence lifetime (FLT) imaging is emerging as a promising tool to improve tumor margin delineation and enhance contrast between tumor and healthy tissue during fluorescence-guided surgery. We have been developing the tauCAM, a custom-built time-gated CMOS camera with a high quantum efficiency (QE) in NIR (46%) and a resolution of 128x128 pixels, specifically for this application. The images obtained from the tauCAM are overlaid on high-resolution color images acquired with a dedicated color camera. We demonstrate the capabilities of our camera by performing in vivo studies on subcutaneous mice tumor models administered with nanobody- and antibody-based NIR tumor-targeted fluorescent contrast agents. The results indicate that FLT imaging enhances the contrast between tumor and healthy tissue.

Ventriculocentesis usually requires preoperative route planning by MRI or CT. However, due to the displacement of brain tissue caused by craniotomy, there is a certain degree of deviation between the actual operation and the preoperative planning, which may eventually lead to potential cerebrovascular damage. Therefore, accurate and real-time intraoperative imaging of blood vessels and puncture needles is particularly important. Ultrafast Doppler imaging can simultaneously achieve high spatiotemporal resolution, whole-brain vascular imaging. So we plan to build an ultrafast Doppler imaging system. Also, deep learning technology is used to reduce the amount of data required and speed up the image refresh speed. In ultrasound-guided puncture, low image contrast complicates tip positioning.Therefore, we designed a needle-tip-signal enhancement needle based on photoacoustic effect. To sum up, we will fuse the needle image and blood vessel image to complete real-time intraoperative imaging of cerebrovascular and puncture needle, so as to help doctors identify the needle tip more accurately and significantly improve the safety and efficiency of surgery.

I will explore various facets of photonic quantum systems and their application in photonic quantum technologies. Firstly, I will focus into quantum foundations and by discuss quantum interference, a key element in photonic quantum technologies. I will highlight how the distinguishability and mixedness of quantum states influence the interference of multiple single photons – and demonstrate novel schemes for generating multipartite entangled quantum states. I will then address photonic quantum computing, specifically focusing on the building blocks of photonic quantum computers. This includes the generation of resource states essential for photonic quantum computing. I will then shift to photonic quantum networks, covering both their hardware aspects and showcasing quantum-network applications that extend beyond bi-partite quantum communication. Lastly, I will outline how photonic integration facilitates the scalability of these systems and discuss the associated challenges.

Joining the rich photophysics of organic light-emitting materials with the exquisite sensitivity of optical resonances to geometry and refractive index enables a plethora of devices with unusual and exciting properties. Examples from my team include biointegrated microlasers for real time sensing of cellular activity and long-term cell tracking, as well as the development of photonic implants with extreme form factors and wireless power supply that support thousands of individually addressable organic LEDs and thus allow optogenetic targeting of neurons deep in the brain with unprecedented spatial control. Very recently, by driving the interaction between excited states in organic materials and resonances in thin optical cavities into the strong coupling regime, we unlocked new tuning parameters which may play a crucial role in the next generation of TVs and computer displays to achieve even more saturated colour while retaining angle-independent emission characteristics.

Porphysomes are liposome-like nanoparticles self-assembled from a single porphyrin-lipid building block, which enables their inherent multifunction of photothermal, photoacoustic, photodynamic, fluorescence, PET, MRI, and drug delivery. Since its discovery, we have demonstrated porphysome’s high tumor selectivity and multimodal theranostic utilities in diverse tumor models and animal species. We have completed GMP manufacturing, GLP safety studies and clinical trial protocols for its first-in-human use, aka ‘beyond lab’. We have also developed a suite of next generation porphysomes that greatly broadened its theranostic applications from light to sound to radiation. These allow us to pursue new directions of ‘beyond light’ and ‘beyond cancer’.

Advanced-stage ovarian cancer becomes extremely challenging to treat effectively using current surgical and chemotherapy methods due to factors such as peritoneal metastasis, incomplete resection, and drug resistance. While photoimmunotherapy is emerging as a promising option for unresectable metastases, its full potential often goes unrealized due to varying treatment outcomes. This research effort aims to enhance the reliability, safety, and effectiveness of photoimmunotherapy for peritoneal metastases by combining targeted nanotechnology, fluorescence-guided intervention, and a state-of-the-art medical laser system.

For the treatment of malignant gliomas stereotactic interstitial photodynamic therapy (iPDT) is an upcoming treatment approach undergoing clinical trials. 5-aminolevulinic acid (5-ALA) mediated iPDT shows prolonged survival. New approaches have to be considered concerning MRI analysis and interpretation, as it highly differs from the standard of care.

Cancers of the upper gastrointestinal tract remain a major contributor to overall cancer risk. This study showcases the potential of diffuse reflectance spectroscopy in tissue characterisation intraoperatively during stomach and oesophageal cancer surgery. The data is normalised, and significant features are selected to improve tissue discrimination accuracy. Using a sterilisable reflection fibre probe, we achieved remarkable sensitivities: 88.11% and 97.09% for the stomach, and 88.35% and 95.05% for the oesophagus. The noteworthy outcomes of this research study have the potential to revolutionise surgical decision-making and accuracy, pushing the boundaries of technological integration in clinical practice.

Laser Speckle Contrast Imaging is a well established method able to produce relative blood flow maps contactless and without using dyes. It relies on the statistical analysis of dynamic speckle images, observed when a coherent light is used to illuminate a medium that contains moving scatterers. The local speckle contrast is related to the movements of the scatterers. Multiple exposure speckle imaging (MESI) is a variant of the technique that takes advantage of multiple exposure data to retrieve more quantitative flow maps by accounting for the unwanted and superimposed contribution of static scatterers. Yet, in MESI, a model is adjusted pixelwise to the experimental data requiring long computation times and an a priori guess on the flow regimes. These issues hindered so far the translation of MESI to clinical applications though some studies have already demonstrated its potential. Here we propose an alternative method based on convolutional neural networks to analyse MESI data. In addition to be model-bias-free, we have found that it is much faster than the classical pixelwise regression approach. This new approach is promising for the clinical translation of MESI.

Classification of actinic keratosis (AK) relies on the evaluation of clinical features but is an imperfect estimate of disease severity and outcome. Non-invasive assessment of subclinical features using optical coherence tomography angiography (OCTA) could enable a more detailed disease characterization with the potential to improve management strategies. In this study, we investigate characteristic vascular metrics of AK grades I-III and photodamaged skin using the open-source toolbox OCTAVA (OCTA vascular analyzer) for automated quantitative analysis of cutaneous microvasculature. We observe significant differences in branchpoint density and mean vessel length between AK I-II and photodamaged skin, indicating occlusion of vessels in the AKs. In comparison to qualitative evaluation, the automated quantification of vascular metrics provided more detailed characterization of subclinical differences. These results demonstrate the potential of OCTA to complement clinical grading of AKs for improved treatment guidance.

In radiation therapy patients are treated by high energy x-ray beams with complex setup and treatment plans. Because of the nature of the setup and delivery, it is not possible to know the dose delivered, rather the clinic relies upon complex simulations and careful pre-treatment human setup steps. But the ability to image the treatment delivery by capturing the Cherenkov emission allows the treatment team to see the actually daily delivery to each patient, for the first time. Cherenkov cameras are time-gated image-intensified CMOS cameras that capture the small bursts of Cherenkov emission that occur in each 4 microsecond pulse of the linear accelerator. The cameras have been designed to self trigger on the scattered radiation, and the image intensifier only activates during the pulses, thereby suppressing ambient room lights significantly. The use of filtering and lens choice also maximize the sensitivity to achieve single-photon level imaging of Cherenkov emissions, with the room lights being present. The recent optimizations to the cameras and their use cases will be reviewed to illustrate the technical improvements and the value of this imaging to the clinical team.

Liver transplantation is a life-saving procedure for patients with end-stage liver diseases. However, the unbalance between number of transplanted patients and patients on waiting list is still an issue. The introduction of perfusion machines and the use of marginal organs tend to reduce this unbalance. However, all marginal organs cannot be transplanted as they suffer more frequently from post-transplant complications. Today there is no efficient method to assess the viability of the organ and therefore to select the marginal organs suitable transplantation. Tissue autofluorescence is an optical method which enables to detect metabolic activity and offer insights about liver viability. Our preliminary results with fluorescence measurements show that we can measure the relative concentration of different mitochondria biomarkers such as flavin, NADH and PpIX under different ischemia-reperfusion conditions on porcine model.

Diffuse gliomas account for more than fifty percent of primitive brain tumors and are challenging to remove because tumor margins are not distinguishable from healthy tissues for the naked eye. To help neurosurgeon in localizing tumoral areas, 5-ALA induced fluorescence of protoporphyrin IX (PpIX) is currently used through surgical microscopes. Various methods based on single wavelength excitation have been proposed to tackle sensitivity issues. New methods based on multiple excitation wavelengths, aim at improving the expert-based estimation models for detection of the tumoral areas. We previously demonstrated [2] using a digital phantom the improvement of classification by our method, which does not have any a priori on other fluorophores. In the present work, we perform the comparison of the separability between healthy and tumoral categories on real clinical data between a state-of-the-art model described in [3] and our model[2]. We demonstrated a reduction of the fit residual by 95% in comparison with the reference model[3]. [1] C. Jonin et al., 10.1016/j.jphotochem.2020.112812 [2

Oral squamous cell carcinomas represent a significant number of cancers diagnosed globally. Of these cancers, surgical resection of the primary tumor is the standard treatment. Conventional methods of assessing completeness of resection are time-consuming, laborious, and cannot be used to evaluate the entire margin of a resected tumor. As such, widefield fluorescence molecular imaging is being explored as an intraoperative technique to guide resections. The widely used single-view techniques have had high success in identifying positive margins (those with thickness < 1mm), but limited success in identifying close margins (1-5 mm). Here a dual aperture fluorescence ratio is presented as a means of improved detection of close margins, with evidence that this technique may be highly useful for future intraoperative fluorescence molecular imaging applications. Clinical results are accompanied by simulations to further optimize the methodology.

Cancer remains a pressing global health concern, challenging efforts to improve patient outcomes. Despite advancements in understanding cancer biology and targeted therapies, many patients struggle to achieve favorable results. Traditional 2D cell cultures fall short in replicating tumor complexities, hindering treatment predictions. Enter 3D cell culture models, particularly multicellular spheroids, offering promise in cancer research. Our study delves into spheroid development and growth dynamics across four cancer cell lines. Results unveil distinct growth patterns, with 9L-GFP cells displaying remarkable stability and growth. Optimal conditions for spheroid stability were identified, emphasizing the importance of cell count and dilution ratios for consistent formation. This underscores the value of 3D spheroid models in advancing cancer research and drug development, offering insights into tumor biology and therapeutic responses.

Basal cell carcinoma is the most common type of cancer. Additionally, the worldwide incidence is increasing by 3-10% per year. Autofluorescence and autofluorescence photobleaching imaging is a potential approach to early diagnosis. Basal cell nevus syndrome is a rare autosomal dominant disorder with characteristic postnatal tumors, especially basal cell carcinomas. The nature of these lesions, however, remains identical. Therefore, investigation of basal cell nevus syndrome associated basal cell carcinoma autofluorescence intensity and autofluorescence photobleaching kinetics could assist in early detection and assessment of basal cell carcinoma. An imaging device with 405 nm LED illumination was used for cutaneous autofluorescence excitation. Autofluorescence intensity imaging and photobleaching imaging data were analyzed, the photobleaching kinetics of basal cell carcinoma in sporadic cases and in patients with basal cell nevus syndrome was studied. Research was supported by Latvian Council of Science project "Non-melanoma skin cancer diagnostics by evaluating autofluorescence photobleaching kinetics (Nr. lzp-2022/1-0255).

Spectral imaging – acquisition of images within specific spectral intervals - is a powerful tool for optical diagnostics, able to provide objective data on various clinical parameters, e.g. abnormal content of biomolecules in pathologic tissues. Performance of diagnostics depends on the spectral selectivity of imaging; from this point, ultra-narrowband spectral line imaging appears well-suited for diagnostic applications. Two prototype devices for triple laser line imaging have been developed and tested in laboratory and clinical environments. Large area or whole-body skin spectral imaging device comprises vertically movable high-resolution camera coupled with a specific illumination unit - side-emitting optical fiber spiral that emits simultaneously three laser spectral lines at the wavelengths 450 nm, 520 nm and 628 nm. In the other device, conventional white broadband endoscopic illumination has been replaced by a combined three spectral line white illumination from a low power RGB laser-fiber system attached to the lighting channel of intranasal endoscope. Both prototypes undergo clinical validation; their design details and preliminary test results are reported and discussed.

Hyperspectral imaging is a method that uses UV-NIR light to capture the physiological and morphological properties of biological tissues. A promising use case of HSI is the study and diagnosis of various types of tumors by extracting tissue parameters. This study examines various tissue indices as an alternative to tissue parameters extracted using the inverse adding-doubling (IAD) algorithm. Tissue indices were compared to tissue parameters extracted using IAD from simulated spectra, mice models, and healthy human forearms. Preliminary results indicate a positive linear correlation between melanin concentration and melanin indices, as well as total hemoglobin and hemoglobin indices. Tissue indices are promising for real-time tissue property extraction from hyperspectral images. They can potentially be used as a fast and accurate tool to aid in the diagnosis of tumors.

Dealing with certain types of skin cancer, like Basal Cell Carcinoma (BCC), can be a major struggle for those affected by it. Currently, the diagnosis of BCC necessitates a biopsy, which is a time-consuming and costly process. Therefore, it is imperative to explore alternative diagnostic methods that are both more efficient and effective. To this goal, we have developed a handheld optical coherence microscopy (OCM) imaging device capable of producing in real-time, high-resolution optical slices of the tissue. The instrument is equipped with a variable focus liquid lens that allows for fast shifting of the focus inside the sample, resulting in high-resolution lateral images throughout the whole axial imaging range.

The high reoperation rate after breast-conserving surgery (in average 19% in the UK) is associated with the lack of efficient and easy to apply intraoperative methods for detection the tumour residue (“positive margin”) of the excised sample. In-situ tests, based on diffuse reflectance and intrinsic fluorescence spectroscopy could potentially palliate this problem by interrogating tissues at a depth of up to several millimetres. We evaluated three machine learning algorithms applied to a dataset of diffuse reflectance and fluorescence spectra consisting of 181 frozen breast samples, collected from 138 patients. The diagnostic accuracy depended on the applied algorithm and the AUCs ranged from 0.71 to 0.81 (maximal sensitivity 86.16%, specificity 58.97%) and is comparable with existent intraoperational modalities, such as, for example, MarginProbe. Further research is needed to find an optimal combination of spectral features and diagnostic algorithm.

Cardiovascular diseases are the leading cause of death worldwide. Samples of 32 human aortic rings with aneurysm, atherosclerosis, aortic stenosis, aortic insufficiency, and bicuspid aortic valve were acquired from surgical interventions, as well as healthy control specimens. Ex-vivo measurements were taken and co-registered using wide-field HyperSpectral Imaging (HSI), in the SWIR ranges, as well as Optical Coherence Tomography (OCT). OCT was used to find the attenuation coefficient of the samples, which revealed that healthy samples had higher values than their pathological counterparts. This coefficient also decreased for dilated aortic walls, which is an indicator of the studied diseases. Additionally, HSI was used for deriving the elastin, collagen, and lipid content of the samples. Collagen was more present in samples with aortic insufficiency, while lipids were more visible on aortic stenosis samples. Therefore, OCT-guided HSI could potentially be used to provide a better understanding of aortic diseases, enabling significant advances in their diagnosis, treatment, and prevention.

We present a fully integrated, clinical-compatible SRS imaging device giving access to the complete Raman spectrum during tumor surgeries. Leveraging the advantages of a compact and robust fiber laser, we have integrated the entire microscopy system into a clinical cart, facilitating deployment in diverse clinical environments. The laser provides rapid tunability within milliseconds across a broad spectral range of 700 to 3300 cm^-1, covering biomedically relevant resonances in the fingerprint region. For detailed examination of larger tissue samples, we have designed a high-speed, low-resolution imaging mode to quickly identify cancerous hot-spots, followed by a high-resolution imaging mode.

Optical cavities with nonlinear elements and delayed self-coupling are widely explored candidates for photonic reservoir computing (RC). For time series prediction applications that appear in many real-world problems, energy efficiency, robustness and performance are key indicators. With this contribution I want to clarify the role of internal dynamic coupling and timescales on the performance of a photonic RC system and discuss routes for optimization. By numerically comparing various delay-based RC systems e.g., quantum-dot lasers, spin-VCSEL (vertically emitting semiconductor lasers), and semiconductor amplifiers regarding their performance on different time series prediction tasks, to messages are emphasized: First, a concise understanding of the nonlinear dynamic response (bifurcation structure) of the chosen dynamical system is necessary in order to use its full potential for RC and prevent operation with unsuitable parameters. Second, the input scheme (optical injection, current modulation etc.) crucially changes the outcome as it changes the direction of the perturbation and therewith the nonlinearity. The input can be further utilized to externally add a memory timescale that is needed for the chosen task and thus offers an easy tunability of RC systems.

Programmable photonic circuits manipulate the flow of light on a chip by electrically controlling a set of tunable analog gates connected by optical waveguides. Light is distributed and spatially rerouted to implement various linear functions by interfering signals along different paths. A general-purpose photonic processor can be built by integrating this flexible hardware in a technology stack comprising an electronic monitoring and controlling layer and a software layer for resource control and programming. This processor can leverage the unique properties of photonics in terms of ultra-high bandwidth, high-speed operation, and low power consumption while operating in a complementary and synergistic way with electronic processors. This talk will review the recent advances in the field and it will also delve into the potential application fields for this technology including, communications, 6G systems, interconnections, switching for data centers and computing.

Fourier Transform Infrared (FTIR) spectroscopy and Raman spectroscopy, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares – Discriminant Analysis (PLS-DA), were used to build classification models which allow to diagnose colon cancer in cell samples. The FTIR measurements were carried out in two spectral ranges of the mid-infrared spectrum, in the 1000-1800 cm-1 range 2700-3700 cm-1 range, whereas the Raman spectra were measured in the 980-1800 cm-1 range by focusing the laser beam onto nucleus and cytoplasm region of single cells grown onto glass coverslip. In fact, PCA technique highlights small biochemical differences between healthy and cancerous cells, mainly due to the larger relative lipid content in the former cells with respect to the latter ones, and to the larger relative amount of nucleic acid components in the cancerous cells with respect to the healthy ones. The comparison between the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR and Raman spectra remarks that both algorithms allow to classify unknown spectra with excellent accuracy (100%) for both cell regions.

Optical imaging is a non-invasive technique that is able to monitor hemodynamic and metabolic brain response following neuronal activation during neurosurgery. However, it still lacks robustness to be used as a clinical standard. In particular, the quantification of the biomarkers of brain functionality needs to be improved. The quantification relies on the modified Beer Lambert law, which needs a correct estimation of the optical mean path length of traveled photons. Monte Carlo simulations are used for estimating the optical path length, but it is time-consuming, especially when modeling a patient’s brain cortex. In this study, we developed a neural network based on the UNET architecture for a pixel-wise and real-time estimation of optical mean path length. The neural network was trained with segmentation of brain cortex as input and mean path length data as target. This deep learning approach allows a real time estimation of the optical mean path length. The results can be beneficial and useful within the framework of our EU-funded HyperProbe project, which aims at transforming neuronavigation during glioma resection using novel hyperspectral imaging technology.

We demonstrate the pre-clinical use of a large are metamaterial surfaces that are fabricated using low-cost Laser Interference Lithography. The signal reading can be achieved using an objective free reflection probe. The minimum detection level can go down to 1pg/ml for S100A9 and AXL, for the major relevant brain metastasis biomarkers.

This study aims to integrate real-time hyperspectral (HS) imaging with a surgical microscope to assist neuro- surgeons in differentiating between healthy and pathological tissue during procedures. Using the LEICA M525 microscope’s optical ports, we register HS data and RGB, in an efforts to improve margin delineation and surgical outcomes. The CUBERT ULTRIS SR5 camera with 51 bands and 15 Hz is employed, and critical calibration steps are outlined for clinical application. Experimental validation is conducted on ex-vivo animal tissue using reflectance spectroscopy. We present the preliminary validation results of the performance comparison between the designed hyperspectral imaging microscope prototype and diffuse reflectance spectroscopy conducted on animal tissue.

There is a strong need to refine our understanding of the visual and non-visual phenomena governed by light entering the eye. Current light-measuring devices allow to evaluate such light exposure but they often present measurement limitations or aesthetic constraints. Unfortunately, this makes the acceptance of such devices difficult and alters the activities of the wearer. In turn, these disruptions to the wearer's life can bias the data analysis results afterwards. We propose an electronic eyewear similar to an ordinary frame embedding calibrated visible light and wear sensors. This device enables continuous light measurements received on the ocular surface over several days in real life conditions.

The project focused on exploring the potential of ultrasonic waves to augment the efficiency of photodynamic therapy using sono-photodynamic therapy (SPDT). The primary objective is to theoretically ascertain the minimum energy required for acoustic nucleation and cavitation within biological tissue and assess its implications for the efficacy of SPDT. The software Wolfram Mathematica was used for the analysis, and the determined minimum energy threshold was approximately 1.5 MPa in biological media and 10 MPa in water. Moreover, the phenomena induced by inertial cavitation due to the bubble implosion, such as sonoluminescence, can be important for amplifying the combined effects of sono and photomechanical/chemical events. This approach elucidates the ultrasonic parameters capable of enhancing SPDT, providing an initial approach to possible pathways for therapeutic enhancement.

Multi-separation diffuse reflectance spectroscopy (MSDRS), could potentially improve colon cancer diagnostics if combined with an accurate diagnostic algorithm. Several machine learning algorithms were compared for the analysis of MSDRS spectra collected from freshly excised colon carcinoma and normal tissues from 8 patients. Although the diagnostic accuracy depends on a combination of spectral features and the algorithm selected, preliminary results are promising, with sensitivity to tumour of 82%, and a specificity of 86%, with AUC=0.89. Further research will find an optimal combination of spectral features and machine learning algorithm.

Fluorescence microscopy (FM) has developed as a cutting-edge tool in the field of pathological sciences, enabling the capability for real-time study of cells and tissues with outstanding resolution. Here, we proposed an augmented reality fluorescence microscopy (ARFM), which presents a cost-effective and 3D-printed alternative to increase specimen examinations. By mixing classical fluorescence with augmented reality, this technique promises to boost diagnostic precision and simplify tailored treatment plans. The proposed concept involves a confocal fluorescence microscope coupled with augmented reality, simulated by the application of Optic Studio Zemax. This novel methodology has tremendous promise for future application in pathological sciences, with the potential to transform the techniques by which diseases are detected and treated.