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 to 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 demonstrated1,2 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 in3 and our model.1,2 We demonstrated a reduction of the fit residual by 95% in comparison with the reference model.3
Protoporphyrin IX (PpIX) is a fluorophore being currently used to localize tumoral tissues. The tissue is usually excited at one wavelength, e.g., 405 nm, and the fluorescence signal is used to estimate the amount of PpIX during surgery. However, other fluorophores (baseline) whose emission spectra are close to the one of PpIX impair the quantification of PpIX and consequently the tissue pathological status classification. An efficient multi-excitation wavelengths method, free from any a priori on the baseline shape, has been proposed to cope with this issue. This method requires decorrelated measurements in the range of PpIX emission at multiple excitation wavelengths. We investigated the influence of the source bandwith on this decorelation by comparing two experimental setups using either LED or laser diode sources. The experimental setup using laser diodes for excitation increases the decorrelation by 35.3 % compared to the one using LEDs in the spectral range of PpIX emission.
RGB imaging is a non-invasive technique that is able to monitor hemodynamic brain responses following neuronal activation during neurosurgery. These cameras are often present in operating rooms, but a robust quantification is complicated to perform during neurosurgery. Liquid blood have been proposed, but it is not possible to model hemodynamic responses similar to those that occur in the brain. To overcome this issue, we propose a 3D brain model, including activated, non-activated grey matter and temporal hemodynamic fluctuations using Monte Carlo simulations. Several setups were modeled to evaluate their impact for identifying activated brain areas using statistical parametric mapping.
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.
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