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Ezhov I, Scibilia K, Giannoni L, Kofler F, Iliash I, Hsieh F, Shit S, Caredda C, Lange F, Montcel B, Tachtsidis I, Rueckert D. Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:093509. [PMID: 39318967 PMCID: PMC11421663 DOI: 10.1117/1.jbo.29.9.093509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 09/04/2024] [Accepted: 09/05/2024] [Indexed: 09/26/2024]
Abstract
Significance 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 living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra deems the spectroscopy techniques as a unique diagnostic tool. Aim No established method exists to streamline the inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. We analyze a machine learning technique for inference of changes in the molecular composition of brain tissue. Approach We propose modifications to the existing learnable methodology based on the Beer-Lambert law. We evaluate the method's applicability to linear and nonlinear formulations of this physical law. The approach is tested on data obtained from the bNIRS- and HSI-based monitoring of brain tissue. Results The results demonstrate that the proposed method enables real-time molecular composition inference while maintaining the accuracy of traditional methods. Preliminary findings show that Beer-Lambert law-based spectral unmixing allows contrasting brain anatomy semantics such as the vessel tree and tumor area. Conclusion We present a data-driven technique for inferring molecular composition change from diffuse spectroscopy of brain tissue, potentially enabling intra-operative monitoring.
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Affiliation(s)
- Ivan Ezhov
- Technical University of Munich, Department of Computer Science, Munich, Germany
| | - Kevin Scibilia
- Technical University of Munich, Department of Computer Science, Munich, Germany
| | - Luca Giannoni
- University of Florence, Department of Physics and Astronomy, Florence, Italy
- European Laboratory for Non-Linear Spectroscopy, Florence, Italy
| | | | - Ivan Iliash
- Technical University of Munich, Department of Computer Science, Munich, Germany
| | - Felix Hsieh
- Technical University of Munich, Department of Computer Science, Munich, Germany
| | - Suprosanna Shit
- Technical University of Munich, Department of Computer Science, Munich, Germany
| | - Charly Caredda
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR, Lyon, France
| | - Frédéric Lange
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Bruno Montcel
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR, Lyon, France
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Daniel Rueckert
- Technical University of Munich, Department of Computer Science, Munich, Germany
- Imperial College London, Department of Computing, London, United Kingdom
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Giannoni L, Marradi M, Scibilia K, Ezhov I, Bonaudo C, Artemiou A, Toaha A, Lange F, Caredda C, Montcel B, Puppa AD, Tachtsidis I, Rückert D, Pavone FS. Transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:093508. [PMID: 39258259 PMCID: PMC11384341 DOI: 10.1117/1.jbo.29.9.093508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 08/20/2024] [Accepted: 08/21/2024] [Indexed: 09/12/2024]
Abstract
Significance Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long time required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. Aim We propose here a transportable, high-density, spectral scanning-based hyperspectral imaging (HSI) 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 for fixing, staining nor compromising the integrity of the tissue properties. Approach HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tunable 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 a maximum of 79) and to reconstruct 3D hypercubes composed of high-resolution (4 to 5 μ m ), widefield images ( 0.9 × 0.9 mm 2 ) of the surgical samples, where each pixel is associated with a complete spectrum. Results The HyperProbe1 setup is here presented and characterized. The system is applied to 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumor classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct the mapping of major biomarkers, such as oxy-(HbO 2 ) and deoxyhemoglobin (HHb), as well as cytochrome-c-oxidase (CCO). We also provided a preliminary attempt to infer tumor classification based on differences in composition in the samples, suggesting the possibility of using lipid content and differential CCO concentrations to distinguish between lower and higher-grade gliomas. Conclusions 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.
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Affiliation(s)
- Luca Giannoni
- University of Florence, Department of Physics and Astronomy, Florence, Italy
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Marta Marradi
- University of Florence, Department of Physics and Astronomy, Florence, Italy
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Kevin Scibilia
- Technical University of Munich, TranslaTUM - Center for Translational Cancer Research, Munich, Germany
| | - Ivan Ezhov
- Technical University of Munich, TranslaTUM - Center for Translational Cancer Research, Munich, Germany
| | - Camilla Bonaudo
- Azienda Ospedaliero-Universitaria Careggi, University of Florence, Neurosurgery, Department of Neuroscience, Psychology, Pharmacology and Child Health, Florence, Italy
| | - Angelos Artemiou
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Anam Toaha
- University of Florence, Department of Physics and Astronomy, Florence, Italy
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Frédéric Lange
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Charly Caredda
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon, France
| | - Bruno Montcel
- Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon, France
| | - Alessandro Della Puppa
- Azienda Ospedaliero-Universitaria Careggi, University of Florence, Neurosurgery, Department of Neuroscience, Psychology, Pharmacology and Child Health, Florence, Italy
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Daniel Rückert
- Technical University of Munich, TranslaTUM - Center for Translational Cancer Research, Munich, Germany
- Imperial College London, Department of Computing, London, United Kingdom
| | - Francesco Saverio Pavone
- University of Florence, Department of Physics and Astronomy, Florence, Italy
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
- National Research Council, National Institute of Optics, Sesto Fiorentino, Italy
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Caredda C, Van Reeth E, Mahieu-Williame L, Sablong R, Sdika M, Schneider FC, Picart T, Guyotat J, Montcel B. Intraoperative identification of functional brain areas with RGB imaging using statistical parametric mapping: Simulation and clinical studies. Neuroimage 2023; 278:120286. [PMID: 37487945 DOI: 10.1016/j.neuroimage.2023.120286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 07/06/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023] Open
Abstract
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.
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Affiliation(s)
- Charly Caredda
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F69100, Lyon, France.
| | - Eric Van Reeth
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F69100, Lyon, France
| | - Laurent Mahieu-Williame
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F69100, Lyon, France
| | - Raphaël Sablong
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F69100, Lyon, France
| | - Michaël Sdika
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F69100, Lyon, France
| | - Fabien C Schneider
- Service de Radiologie, Centre Hospitalier Universitaire de Saint Etienne, TAPE EA7423, Université de Lyon, UJM Saint Etienne, F42023, France
| | - Thiébaud Picart
- Service de Neurochirurgie D, Hospices Civils de Lyon, Bron, France
| | - Jacques Guyotat
- Service de Neurochirurgie D, Hospices Civils de Lyon, Bron, France
| | - Bruno Montcel
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F69100, Lyon, France.
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Wu Y, Xu Z, Yang W, Ning Z, Dong H. Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery. Front Bioeng Biotechnol 2022; 10:906728. [PMID: 35711634 PMCID: PMC9196632 DOI: 10.3389/fbioe.2022.906728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.
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Affiliation(s)
- Yue Wu
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Zhongyuan Xu
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Wenjian Yang
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Zhiqiang Ning
- Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (CAS), Hefei, China.,Science Island Branch, Graduate School of USTC, Hefei, China
| | - Hao Dong
- Research Center for Sensing Materials and Devices, Zhejiang Lab, Hangzhou, China
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Giannoni L, Lange F. A hyperspectral imaging system for mapping haemoglobin and cytochrome-c-oxidase concentration changes in the exposed cerebral cortex. IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS : A PUBLICATION OF THE IEEE LASERS AND ELECTRO-OPTICS SOCIETY 2021; 27:7400411. [PMID: 33716586 PMCID: PMC7116887 DOI: 10.1109/jstqe.2021.3053634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
We present a novel hyperspectral imaging (HSI) system using visible and near-infrared (NIR) light on the exposed cerebral cortex of animals, to monitor and quantify in vivo changes in the oxygenation of haemoglobin and in cellular metabolism via measurement of the redox states of cytochrome-c-oxidase (CCO). The system, named hNIR, is based on spectral scanning illumination at 11 bands (600, 630, 665, 784, 800, 818, 835, 851, 868, 881 and 894 nm), using a supercontinuum laser coupled with a rotating Pellin-Broca prism. Image reconstruction is performed with the aid of a Monte Carlo framework for photon pathlength estimation and post-processing correction of partial volume effects. The system is validated on liquid optical phantoms mimicking brain tissue haemodynamics and metabolism, and finally applied in vivo on the exposed cortex of mice undergoing alternating oxygenation challenges. The results of the study demonstrate the capacity of hNIR to map and quantify the haemodynamic and metabolic states of the exposed cortex at microvascular levels. This represents (to the best of our knowledge) the first example of simultaneous mapping and quantification of cerebral haemoglobin and CCO in vivo using visible and NIR HSI, which can potentially become a powerful tool for better understanding brain physiology.
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Affiliation(s)
- Luca Giannoni
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Frédéric Lange
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
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Yuan Y, Yan S, Fang Q. Light transport modeling in highly complex tissues using the implicit mesh-based Monte Carlo algorithm. BIOMEDICAL OPTICS EXPRESS 2021; 12:147-161. [PMID: 33520382 PMCID: PMC7818958 DOI: 10.1364/boe.411898] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/22/2020] [Accepted: 11/25/2020] [Indexed: 05/16/2023]
Abstract
The mesh-based Monte Carlo (MMC) technique has grown tremendously since its initial publication nearly a decade ago. It is now recognized as one of the most accurate Monte Carlo (MC) methods, providing accurate reference solutions for the development of novel biophotonics techniques. In this work, we aim to further advance MMC to address a major challenge in biophotonics modeling, i.e. light transport within highly complex tissues, such as dense microvascular networks, porous media and multi-scale tissue structures. Although the current MMC framework is capable of simulating light propagation in such media given its generality, the run-time and memory usage grow rapidly with increasing media complexity and size. This greatly limits our capability to explore complex and multi-scale tissue structures. Here, we propose a highly efficient implicit mesh-based Monte Carlo (iMMC) method that incorporates both mesh- and shape-based tissue representations to create highly complex yet memory-efficient light transport simulations. We demonstrate that iMMC is capable of providing accurate solutions for dense vessel networks and porous tissues while reducing memory usage by greater than a hundred- or even thousand-fold. In a sample network of microvasculature, the reduced shape complexity results in nearly 3x speed acceleration. The proposed algorithm is now available in our open-source MMC software at http://mcx.space/#mmc.
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Affiliation(s)
- Yaoshen Yuan
- Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA
| | - Shijie Yan
- Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA
| | - Qianqian Fang
- Department of Bioengineering, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA
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Optimal Spectral Combination of a Hyperspectral Camera for Intraoperative Hemodynamic and Metabolic Brain Mapping. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155158] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas are assessed by monitoring the oxygenated (HbO2) and deoxygenated hemoglobin (Hb) concentration changes occurring in the brain. Sometimes, the functional status of the brain is assessed using metabolic biomarkers: the oxidative state of cytochrome-c-oxidase (oxCCO). A setup composed of a white light source and a hyperspectral or a standard RGB camera could be used to identify the functional areas. The choice of the best spectral configuration is still based on an empirical approach. We propose in this study a method to define the optimal spectral combinations of a commercial hyperspectral camera for the computation of hemodynamic and metabolic brain maps. The method is based on a Monte Carlo framework that simulates the acquisition of the intrinsic optical signal following a neuronal activation. The results indicate that the optimal spectral combination of a hyperspectral camera aims to accurately quantify the HbO2 (0.5% error), Hb (4.4% error), and oxCCO (15% error) responses in the brain following neuronal activation. We also show that RGB imaging is a low cost and accurate solution to compute Hb maps (4% error), but not accurate to compute HbO2 (48% error) or oxCCO (1036% error) maps.
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