Intraoperative identification of functional brain areas with RGB imaging using statistical parametric mapping: Simulation and clinical studies.

Complementary technique to preoperative fMRI and electrical brain stimulation (EBS) for glioma resection could improve dramatically the surgical procedure and patient care. Intraoperative RGB optical imaging is a technique for localizing functional areas of the human cerebral cortex that can be used during neurosurgical procedures. However, it still lacks robustness to be used with neurosurgical microscopes as a clinical standard. In particular, a robust quantification of biomarkers of brain functionality is needed to assist neurosurgeons. We propose a methodology to evaluate and optimize intraoperative identification of brain functional areas by RGB imaging. This consist in a numerical 3D brain model based on Monte Carlo simulations to evaluate intraoperative optical setups for identifying functional brain areas. We also adapted fMRI Statistical Parametric Mapping technique to identify functional brain areas in RGB videos acquired for 12 patients. Simulation and experimental results were consistent and showed that the intraoperative identification of functional brain areas is possible with RGB imaging using deoxygenated hemoglobin contrast. Optical functional identifications were consistent with those provided by EBS and preoperative fMRI. We also demonstrated that a halogen lighting may be particularity adapted for functional optical imaging. We showed that an RGB camera combined with a quantitative modeling of brain hemodynamics biomarkers can evaluate in a robust way the functional areas during neurosurgery and serve as a tool of choice to complement EBS and fMRI.

OpenSpyrit: an ecosystem for open single-pixel hyperspectral imaging.

This paper describes OpenSpyrit, an open access and open source ecosystem for reproducible research in hyperspectral single-pixel imaging, composed of SPAS (a Python single-pixel acquisition software), SPYRIT (a Python single-pixel reconstruction toolkit) and SPIHIM (a single-pixel hyperspectral image collection). The proposed OpenSpyrit ecosystem responds to the need for reproducibility and benchmarking in single-pixel imaging by providing open data and open software. The SPIHIM collection, which is the first open-access FAIR dataset for hyperspectral single-pixel imaging, currently includes 140 raw measurements acquired using SPAS and the corresponding hypercubes reconstructed using SPYRIT. The hypercubes are reconstructed by both inverse Hadamard transformation of the raw data and using the denoised completion network (DC-Net), a data-driven reconstruction algorithm. The hypercubes obtained by inverse Hadamard transformation have a native size of 64 × 64 × 2048 for a spectral resolution of 2.3 nm and a spatial resolution that is comprised between 182.4 µm and 15.2 µm depending on the digital zoom. The hypercubes obtained using the DC-Net are reconstructed at an increased resolution of 128 × 128 × 2048. The OpenSpyrit ecosystem should constitute a reference to support benchmarking for future developments in single-pixel imaging.

A priori free spectral unmixing with periodic absorbance changes: application for auto-calibrated intraoperative functional brain mapping.

Spectral unmixing designates techniques that allow to decompose measured spectra into linear or non-linear combination of spectra of all targets (endmembers). This technique was initially developed for satellite applications, but it is now also widely used in biomedical applications. However, several drawbacks limit the use of these techniques with standard optical devices like RGB cameras. The devices need to be calibrated and a a priori on the observed scene is often necessary. We propose a new method for estimating endmembers and their proportion automatically and without calibration of the acquisition device based on near separable non-negative matrix factorization. This method estimates the endmembers on spectra of absorbance changes presenting periodic events. This is very common in in vivo biomedical and medical optical imaging where hemodynamics dominate the absorbance fluctuations. We applied the method for identifying functional brain areas during neurosurgery using four different RGB cameras (an industrial camera, a smartphone and two surgical microscopes). Results obtained with the auto-calibration method were consistent with the intraoperative gold standards. Endmembers estimated with the auto-calibration method were similar to the calibrated endmembers used in the modified Beer-Lambert law. The similarity was particularly strong when both cardiac and respiratory periodic events were considered. This work can allow a widespread use of spectral imaging in the industrial or medical field.

Intraoperative Resting-State Functional Connectivity Based on RGB Imaging.

RGB optical imaging is a marker-free, contactless, and non-invasive technique that is able to monitor hemodynamic brain response following neuronal activation using task-based and resting-state procedures. Magnetic resonance imaging (fMRI) and functional near infra-red spectroscopy (fNIRS) resting-state procedures cannot be used intraoperatively but RGB imaging provides an ideal solution to identify resting-state networks during a neurosurgical operation. We applied resting-state methodologies to intraoperative RGB imaging and evaluated their ability to identify resting-state networks. We adapted two resting-state methodologies from fMRI for the identification of resting-state networks using intraoperative RGB imaging. Measurements were performed in 3 patients who underwent resection of lesions adjacent to motor sites. The resting-state networks were compared to the identifications provided by RGB task-based imaging and electrical brain stimulation. Intraoperative RGB resting-state networks corresponded to RGB task-based imaging (DICE:0.55±0.29). Resting state procedures showed a strong correspondence between them (DICE:0.66±0.11) and with electrical brain stimulation. RGB imaging is a relevant technique for intraoperative resting-state networks identification. Intraoperative resting-state imaging has several advantages compared to functional task-based analyses: data acquisition is shorter, less complex, and less demanding for the patients, especially for those unable to perform the tasks.

Comparison of high-resolution magnetic resonance imaging and micro-computed tomography arthrography for in-vivo assessment of cartilage in non-human primate models.

BACKGROUND: Non-human primate (NHP) could be an interesting model for osteoarthritis (OA) longitudinal studies but standard medical imaging protocols are not able to acquire sufficiently high-resolution images to depict the thinner cartilage (compared to human) in an in vivo context. The aim of this study was thus to develop and validate the acquisition protocols for knee joint examination of NHP using magnetic resonance imaging (MRI) at 1.5 T and X-ray micro-computed tomography arthrography (µCTA). METHODS: The first phase of the study focused on developing dedicated in vivo HR-MRI and µCTA protocols for simultaneous acquisitions of both knee joints on NHP. For MR, a dedicated two-channel receiver array coil and acquisition sequence were developed on a 1.5 T Siemens Sonata system and tuned to respect safety issues and reasonable examination time. For µCTA, an experimental setup was devised so as to fulfill similar requirements. The two imaging protocols were used during a longitudinal study so as to confirm that repeated injections of loxaglic acid (contrast agent used for µCTA) didn't induce any bias in cartilage assessment and to compare segmentation results from the two modalities. Lateral and medial cartilage tibial plateaus were assessed using a common image processing protocol leading to a 3D estimation of the cartilage thickness. RESULTS: From HR-MRI and µCTA images, thickness distributions were extracted allowing for proper evaluation of knee cartilage thickness of the primates. Results obtained in vivo indicated that the µCTA protocol did not induce any bias in the measured cartilage parameters and moreover, segmentation results obtained from the two imaging modalities were consistent. CONCLUSIONS: MR and µCTA are valuable imaging tools for the morphological evaluation of cartilage in NHP models which in turn can be used for OA studies.

Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy.

Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack sensitivity and specificity. Here a fully automatic clustering method is proposed to discriminate glioma margin. This is obtained from spectroscopic fluorescent measurements acquired with a recently introduced intraoperative set up. We describe a data-driven selection of best spectral features and show how this improves results of margin prediction from healthy tissue by comparison with the standard biomarker-based prediction. This pilot study based on 10 patients and 50 samples shows promising results with a best performance of 77% of accuracy in healthy tissue prediction from margin tissue.

Repetitive motion compensation for real time intraoperative video processing.

In this paper, we present a motion compensation algorithm dedicated to video processing during neurosurgery. After craniotomy, the brain surface undergoes a repetitive motion due to the cardiac pulsation. This motion as well as potential video camera motion prevent accurate video analysis. We propose a dedicated motion model where the brain deformation is described using a linear basis learned from a few initial frames of the video. As opposed to other works using linear basis for the flow, the camera motion is explicitly accounted in the transformation model. Despite the nonlinear nature of our model, all the motion parameters are robustly estimated all at once, using only one singular value decomposition (SVD), making our procedure computationally efficient. A Lagrangian specification of the flow field ensures the stability of the method. Experiments on in vivo data are presented to evaluate the capacity of the method to cope with occlusion or camera motion. The method we propose satisfies the intraoperative constraints: it is robust to surgical tools occlusions, it works in real time, and it is able to handle large camera viewpoint changes.

Intraoperative quantitative functional brain mapping using an RGB camera.

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. However, it still lacks robustness to be used as a clinical standard. In particular, new biomarkers of brain functionality with improved sensitivity and specificity are needed. We present a method for the computation of hemodynamics-based functional brain maps using an RGB camera and a white light source. We measure the quantitative oxy and deoxyhemoglobin concentration changes in the human brain cortex with the modified Beer-Lambert law and Monte Carlo simulations. A functional model has been implemented to evaluate the functional brain areas following neuronal activation by physiological stimuli. The results show a good correlation between the computed quantitative functional maps and the brain areas localized by electrical brain stimulation (EBS). We demonstrate that an RGB camera combined with a quantitative modeling of brain hemodynamics biomarkers can evaluate in a robust way the functional areas during neurosurgery and serve as a tool of choice to complement EBS.

Nonlinear relation between concentration and fluorescence emission of protoporphyrin IX in calibrated phantoms.

5-ALA-induced protoporphyrin IX (PpIX) has shown its relevance in medical assisting techniques, notably in the detection of glioma (brain tumors). Validation of instruments on phantoms is mandatory and a standardization procedure has recently been proposed. This procedure yields phantoms recipes to realize a linear relationship between PpIX concentration and fluorescence emission intensity. The present study puts forward phantoms where this linear relationship cannot be used. We propose a model that considers two states of PpIX, corresponding to two different aggregates of PpIX, with fluorescence spectra peaking at 634 and 620 nm, respectively. We characterize the influence of these two states on PpIX fluorescence emission spectra in phantoms with steady concentration of PpIX and various microenvironment parameters (surfactant, Intralipid or bovine blood concentration, and pH). We show that, with fixed PpIX concentration, a modification of the microenvironment induces a variation of the emitted spectrum, notably a shift in its central wavelength. We show that this modification reveals a variation of proportions of the two states. This establishes phantom microenvironment regimes where the usual single state model is biased while a linear combination of the two spectra enables accurate recovering of any measured spectra.

Two-peaked 5-ALA-induced PpIX fluorescence emission spectrum distinguishes glioblastomas from low grade gliomas and infiltrative component of glioblastomas.

5-ALA-induced protoporphyrin IX (PpIX) fluorescence enables to guiding in intra-operative surgical glioma resection. However at present, it has yet to be shown that this method is able to identify infiltrative component of glioma. In extracted tumor tissues we measured a two-peaked emission in low grade gliomas and in the infiltrative component of glioblastomas due to multiple photochemical states of PpIX. The second emission peak appearing at 620 nm (shifted by 14 nm from the main peak at 634 nm) limits the sensibility of current methods to measured PpIX concentration. We propose new measured parameters, by taking into consideration the two-peaked emission, to overcome these limitations in sensitivity. These parameters clearly distinguish the solid component of glioblastomas from low grade gliomas and infiltrative component of glioblastomas.