arXiv subjects

Luca Giannoni

Publications and source records attributed to Luca Giannoni.

10 recordsLinked to original sources

arXiv2026

A unified perspective on wavelength selection for molecular composition inference from diffuse spectroscopy

Optical monitoring of living tissue targeting quantification of molecular composition is an active area of research. In the case of broadband spectroscopy, one can attempt to extract molecular, or more precisely, chromophore concentration from a broad-range spectrum of the reflected light. However, selecting the shortest wavelength range sufficient for quantitative optical monitoring remains an open problem. Various wavelength optimization methods are scattered throughout the literature, however, there is no unified view to date. Our work's motivation is to construct a wavelength selection framework by unifying existing selection approaches and propose a novel projection-based method that allows for the pre-identification of a wavelength range that is adequate for the final selection. The framework specifically focuses on proposing different methods that quantify match or mismatch between the chosen light-matter interaction model, defined by the chosen endmembers (e.g. molecular chromophores), and the measured intensity from the spectroscopic data. To evaluate the framework, we perform a retrospective analysis on a broadband spectroscopy dataset of piglets during an induced hypoxia-ischemia state. Overall, we show that our novel projection-based method can be used for band selection, and that existing approaches can be used in conjunction to select an optimal minimal wavelength set that satisfies biophysical model constraints.

physics.optics↗

arXiv2026

OptoCENTAL: a standardised, bench-testing platform based on phantoms for validating optical systems aimed at clinical monitoring of the placenta

Optical imaging and spectroscopy solutions, such as near-infrared spectroscopy (NIRS) and diffuse optical tomography (DOT), have the potential to provide compact, bedside monitoring of the placenta in the clinic, thanks to recent advancements in miniaturisation and wireless wearability. This would provide neonatologist with continuous assessment of the pregnancy status in real-time, as well as tools to possibly predict delivery outcomes. We present here OptoCENTAL, a standardized platform based on multiple optical phantoms, from digital, through solid to liquid, for a comprehensive bench-testing, characterisation and validation of any photonics solution and instrumentation that aims at in vivo, clinical monitoring of the human placenta. Results: Exemplary applications of the OptoCENTAL platform on different types of optical systems, from wearable, continuous-wave devices to broadband and time-domain NIRS systems, demonstrate the flexibility of its procedures to be implemented with any setup, allowing users to compare performances across different solutions. The results also show the capability of OptoCENTAL to provide quantitative assessment of the major features required by any photonic solution for providing effective and efficient monitoring of the placenta, including basic instrument performances, quantification of monitoring accuracy, as well as depth sensitivity. OptoCENTAL represent the first-of-a-kind effort in standardising bench-testing and validation of optical imaging and spectroscopy methods in the framework of placental clinical applications, further advancing the translation of such modalities into the hospitals, as well as towards future certification and commercialisation of such technologies.

physics.med-ph↗

arXiv2026

Hyperspectral imaging solutions for brain tissue metabolic and haemodynamic monitoring: an updated perspective

Since the publication of our review article Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments in 2018, the technological and applicational landscape of the use of hyperspectral imaging (HSI) in brain sciences has evolved and transformed significantly. The number of studies and works where HSI has been deployed in its many forms to map and monitor the haemodynamic and metabolic states of cerebral tissues have grown exponentially, to such a point where an update on the cur-rent state of the art is timely, and we believe would be desirable for both long-term experts in the field, as well as for any new researcher approaching it for the first time. In this commentary, we provide a renewed perspective on the newest and latest developments in brain haemodynamic and metabolic monitoring with HSI over the past eight years. Our hope is that even greater breakthroughs and broader, more numerous novel applications will come forward in the future for the technology, that may benefit from this new overview, as they did from the original one.

physics.med-ph↗

arXiv2026

Multimodal Imaging System Combining Hyperspectral and Laser Speckle Imaging for In Vivo Hemodynamic and Metabolic Monitoring

We present the development and validation of a novel multimodal optical imaging platform that integrates hyperspectral imaging (HSI) and laser speckle contrast imaging (LSCI) to enable real-time, non-invasive mapping of tissue oxygenation, perfusion and metabolism, via blood flowmetry and targeting of oxy- (HbO2), deoxyhemoglobin (HHb), as well as oxidized cytochrome-c-oxidase (oxCCO). The system architecture features a single high-speed camera and dual optical path, with synchronized alternating illumination: a filtered, supercontinuum laser for HSI and a He-Ne laser for LSCI. The system performances were evaluated through in vivo experiments on rat spinal cord under normoxic and hypoxic conditions, revealing coherent physiological changes in hemodynamics, metabolism and relative blood flow index (rBFI). These results demonstrate the potential of the platform for functional tissue imaging and quantitative dynamic monitoring of both oxygen delivery and consumption.

physics.med-ph↗

arXiv2025

2bNIRS: a portable, multi-distance, broadband oximeter and cytochrome-c-oxidase monitoring system for in vivo applications

Conventional near-infrared spectroscopy (NIRS) instruments typically employ 2-3 wavelengths for monitoring tissue haemodynamics. However, the use of broadband illumination (hundreds of wavelengths) unlocks also the targeting of tissue metabolism by exploiting the wide differential-redox peak of absorption of cytochrome-c-oxidase (CCO). For this purpose, we present here a novel, broadband (780-900nm), multi-distance NIRS system, called 2bNIRS. Compact and portable (20 x 30 x 40 cm3), 2bNIRS was validated using a dynamic, optical phantom con-taining blood and yeast, and benchmarked against a multi-wavelength time-domain NIRS (TD-NIRS) system. Validation confirmed its accuracy in tracking haemody-namic and metabolic changes, with superior oximetry performance achieved via our BRUNO algorithm, which integrates broadband spectral fitting with spatially-re-solved analysis. Preliminary in vivo, human applications demonstrated 2bNIRS ap-plicability to monitoring both muscle tissue, during arm cuff occlusions, and the brain, during frontal cortex activation.

physics.med-ph↗

arXiv2025

Redefining spectral unmixing for in-vivo brain tissue analysis from hyperspectral imaging

In this paper, we propose a methodology for extracting molecular tumor biomarkers from hyperspectral imaging (HSI), an emerging technology for intraoperative tissue assessment. To achieve this, we employ spectral unmixing, allowing to decompose the spectral signals recorded by the HSI camera into their constituent molecular components. Traditional unmixing approaches are based on physical models that establish a relationship between tissue molecules and the recorded spectra. However, these methods commonly assume a linear relationship between the spectra and molecular content, which does not capture the whole complexity of light-matter interaction. To address this limitation, we introduce a novel unmixing procedure that allows to take into account non-linear optical effects while preserving the computational benefits of linear spectral unmixing. We validate our methodology on an in-vivo brain tissue HSI dataset and demonstrate that the extracted molecular information leads to superior classification performance.

physics.med-ph↗

arXiv2024

Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue

Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of functional and structural properties of living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra in real-time deems the spectroscopy techniques as a unique diagnostic tool. However, no established method exists to streamline the inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. In this paper, we analyse a machine learning technique for fast and accurate inference of changes in the molecular composition of brain tissue. We reconsider and propose modifications to the existing learnable methodology based on the Beer-Lambert law, which analytically connects the spectra with concentrations. We evaluate the method's applicability to linear and non-linear formulations of the Beer-Lambert law. The approach is tested on real data obtained from the bNIRS- and HSI-based optical monitoring of brain tissue. The results demonstrate that the proposed method enables real-time molecular composition inference while maintaining the accuracy of traditional linear and non-linear optimization solvers. Preliminary findings show that Beer-Lambert law-based spectral unmixing allows to contrast brain anatomy semantics such as the vessel tree and tumor area.

physics.bio-ph↗

arXiv2024

A digital instrument simulator to optimize the development of hyperspectral systems: application for intraoperative functional brain mapping

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas can be assessed by monitoring cerebral hemodynamics and metabolism. A robust quantification of these biomarkers is complicated to perform during neurosurgery due to the critical context of the operating room. In actual devices, the inhomogeneities of the optical properties of exposed brain cortex are poorly taken into consideration, which introduce quantification errors of biomarkers of brain functionality. Moreover, the choice of the best spectral configuration is still based on an empirical approach. We propose a digital instrument simulator to optimize the development of hyperspectral systems. This simulator can provide a realistic modelling of the cerebral cortex and the identification of the optimal wavelengths to monitor cerebral hemodynamics (oxygenated and deoxygenated hemoglobin) and metabolism (oxidized state of cytochromes b, c and cytochrome-c-oxidase). The digital instrument allows the modelling of intensity maps collected by a camera sensor as well as images of pathlength to take into account the inhomogeneities of the optical properties. The optimization procedure helps to identify the best wavelength combination of 18 wavelengths that reduce the quantification errors in HbO2, Hb, oxCCO of 61%, 29% and 82% compared to the gold standard of 121 wavelengths between 780 and 900 nm. The optimization procedure does not help to resolve changes in cytochrome b and c in a significant way but help to better resolve oxCCO changes. We proposed a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This digital instrument simulator and this optimization framework could be used to optimize the design of hyperspectral imaging devices.

physics.med-ph↗

arXiv2024

A transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies

Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long times required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. We propose here a transportable, high-density, spectral-scanning based hyperspectral imaging setup, named HyperProbe1, that can provide in situ, fast biochemical analysis and mapping of fresh surgical tissue samples, right after excision, and without the need of fixing or staining. HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tuneable filters. Such methodology allows the user to select any number and type of wavelength bands in the visible and near-infrared range between 510 and 900 nm (up to 79), and to reconstruct 3D hypercubes composed of high-resolution, widefield images of the surgical samples, where each pixel is associated with a complete spectrum. The system is applied on 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumour classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct mapping of major biomarkers. We also provided a preliminary attempt to infer tumour classification based on differences of composition in the samples, suggesting the possibility to use lipid content and differential cytochrome-c-oxidase concentrations to distinguish between lower and higher grade gliomas. A proof-of-concept of the performances of HyperProbe1 for quantitative, biochemical mapping of surgical biopsies is demonstrated, paving the way for improving current post-surgical, histopathological practice via non-destructive, in situ streamlined screening of fresh tissue samples in a matter of minutes after excision.

physics.med-ph↗

arXiv2023

Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery.

q-bio.QM↗