Multispectral imaging (MSI) devices are optical diagnostic tools that can be used for the non-invasive monitoring and characterization of various kinds of pathologies, including skin conditions such as wounds and ulcers, due to the capability of such technology to track alterations of structural and physiological parameters (e.g., oxygenation and haemodynamics) from changes in the optical properties of the investigated tissue across a large number of spectral bands. In this work, a novel, compact and transportable MSI device based on spectral scanning and diffuse reflectance imaging is going to be presented. The apparatus is composed of light emitting diodes (LEDs) as light sources and a CMOS camera, making it a very compact, manageable, user-friendly, and cost-effective system. The wavelengths of the LED sources, that are located in the visible-NIR portion of the spectrum, have been specifically selected to target and monitor alterations of oxygenation and haemodynamics that can provide biomarkers of monitoring wound healing in chronic ulcers. The calibration of the MSI system is going to be illustrated, discussing the calibration procedure and results obtained with Monte Carlo-based, digital phantoms and liquid optical phantoms. Both types of phantoms mimic the properties of biological tissues and allow to introduce variations in a controlled manner. The proposed MSI system is also going to be tested on patients affected by chronic skin ulcers in order to assess its efficacy and accuracy.
Recent advancements in imaging technologies (MRI, PET, CT, among others) have significantly improved clinical localisation of lesions of the central nervous system (CNS) before surgery, making possible for neurosurgeons to plan and navigate away from functional brain locations when removing tumours, such as gliomas. However, neuronavigation in the surgical management of brain tumours remains a significant challenge, due to the inability to maintain accurate spatial information of pathological and healthy locations intraoperatively. To answer this challenge, the HyperProbe consortium have been put together, consisting of a team of engineers, physicists, data scientists and neurosurgeons, to develop an innovative, all-optical, intraoperative imaging system based on (i) hyperspectral imaging (HSI) for rapid, multiwavelength spectral acquisition, and (ii) artificial intelligence (AI) for image reconstruction, morpho-chemical characterisation and molecular fingerprint recognition. Our HyperProbe system will (1) map, monitor and quantify biomolecules of interest in cerebral physiology; (2) be handheld, cost-effective and user-friendly; (3) apply AI-based methods for the reconstruction of the hyperspectral images, the analysis of the spatio-spectral data and the development and quantification of novel biomarkers for identification of glioma and differentiation from functional brain tissue. HyperProbe will be validated and optimised with studies in optical phantoms, in vivo against gold standard modalities in neuronavigational imaging, and finally we will provide proof of principle of its performances during routine brain tumour surgery on patients. HyperProbe aims at providing functional and structural information on biomarkers of interest that is currently missing during neuro-oncological interventions.
In recent years, hyperspectral imaging (HSI) has demonstrated the capacity to non-invasively differentiate tumours from healthy tissues and identify cancerous regions during surgery, particularly for glioma resection. This is thanks to the use of a relatively large number of adjacent wavelength bands, in order to reconstruct full reflectance spectra of each pixel in the acquired images of the target, thus providing information about its morpho-chemical composition. However, current HSI analysis approaches seem not to fully exploit such advantage, since they mostly tend to focus on tissue features recognition and cancer identification based on supervised algorithm trained upon diagnostic evaluations made by the neurosurgeons or from other diagnostic tools (e.g., histopathology). There is indeed a lack of proper broad-range, optical characterisation of tumour tissue, specifically gliomas, which could provide a more objective, comprehensive and quantitative insight in the spectro-chemistry of the tumour itself and help identifying novel biomarkers for cancer imaging via HSI. For this purpose, we present a fully optical characterisation of fresh ex vivo samples of glioma from surgical biopsies using both a laboratory spectrophotometer and an in-house, high-spectral density HSI system. The latter is based on spectral scanning of the samples via supercontinuum laser (SCL) illumination filtered with acousto-optic tunable filters (AOTF). The results of the spectral characterisation are analysed and compared to extract optical signatures for potential glioma biomarkers in order to further aid neuronavigation via HSI during glioma resection, in particular in the framework of our recently started HyperProbe project.
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