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Caredda C, Cohen JE, Mahieu-Williame L, Sablong R, Sdika M, Schneider FC, Picart T, Guyotat J, Montcel B. A priori free spectral unmixing with periodic absorbance changes: application for auto-calibrated intraoperative functional brain mapping. BIOMEDICAL OPTICS EXPRESS 2024; 15:387-412. [PMID: 38223192 PMCID: PMC10783910 DOI: 10.1364/boe.491292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 07/05/2023] [Accepted: 07/05/2023] [Indexed: 01/16/2024]
Abstract
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.
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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
| | - Jérémy E. Cohen
- 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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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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Puustinen S, Vrzáková H, Hyttinen J, Rauramaa T, Fält P, Hauta-Kasari M, Bednarik R, Koivisto T, Rantala S, von Und Zu Fraunberg M, Jääskeläinen JE, Elomaa AP. Hyperspectral Imaging in Brain Tumor Surgery-Evidence of Machine Learning-Based Performance. World Neurosurg 2023; 175:e614-e635. [PMID: 37030483 DOI: 10.1016/j.wneu.2023.03.149] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 04/10/2023]
Abstract
BACKGROUND Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared. METHODS We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods. RESULTS The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery. CONCLUSIONS In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems.
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Affiliation(s)
- Sami Puustinen
- University of Eastern Finland, Faculty of Health Sciences, School of Medicine, Kuopio, Finland; Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland.
| | - Hana Vrzáková
- Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland; University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Joni Hyttinen
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Tuomas Rauramaa
- Kuopio University Hospital, Department of Clinical Pathology, Kuopio, Finland
| | - Pauli Fält
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Markku Hauta-Kasari
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Roman Bednarik
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Timo Koivisto
- Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
| | - Susanna Rantala
- Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
| | - Mikael von Und Zu Fraunberg
- Oulu University Hospital, Department of Neurosurgery, Oulu, Finland; University of Oulu, Faculty of Medicine, Research Unit of Clinical Medicine, Oulu, Finland
| | | | - Antti-Pekka Elomaa
- University of Eastern Finland, Faculty of Health Sciences, School of Medicine, Kuopio, Finland; Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland; Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
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Laurence A, Toffa DH, Peng K, Robert M, Bouthillier A, Nguyen DK, Leblond F. Multispectral intraoperative imaging for the detection of the hemodynamic response to interictal epileptiform discharges. BIOMEDICAL OPTICS EXPRESS 2022; 13:6245-6257. [PMID: 36589558 PMCID: PMC9774841 DOI: 10.1364/boe.465699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/03/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
Interictal epileptiform discharges (IEDs) are brief neuronal discharges occurring between seizures in patients with epilepsy. The characterization of the hemodynamic response function (HRF) specific to IEDs could increase the accuracy of other functional imaging techniques to localize epileptiform activity, including functional near-infrared spectroscopy and functional magnetic resonance imaging. This study evaluated the possibility of using an intraoperative multispectral imaging system combined with electrocorticography (ECoG) to measure the average HRF associated with IEDs in eight patients. Inter-patient variability of the HRF is illustrated in terms of oxygenated hemoglobin peak latency, oxygenated hemoglobin increase/decrease following IEDs, and signal-to-noise ratio. A sub-region was identified using an unsupervised clustering algorithm in three patients that corresponded to the most active area identified by ECoG.
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Affiliation(s)
- Audrey Laurence
- Polytechnique Montreal, Department of Engineering Physics, Montréal, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
| | - Dènahin H. Toffa
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
- Centre Hospitalier de l’Université de Montréal, Division of Neurology, Montréal, Canada
| | - Ke Peng
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
| | - Manon Robert
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
| | - Alain Bouthillier
- Centre Hospitalier de l’Université de Montréal, Division of Neurosurgery, Montréal, Canada
| | - Dang K. Nguyen
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
- Centre Hospitalier de l’Université de Montréal, Division of Neurology, Montréal, Canada
| | - Frederic Leblond
- Polytechnique Montreal, Department of Engineering Physics, Montréal, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
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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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Advances in Hyperspectral and Multispectral Optical Spectroscopy and Imaging of Tissue. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Optical imaging and characterization of tissue has become a huge applied field due to the advantages of the optical analysis methods, which include non-invasiveness, portability, high sensitivity, and high spectral specificity [...]
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Intraoperative Resting-State Functional Connectivity Based on RGB Imaging. Diagnostics (Basel) 2021; 11:diagnostics11112067. [PMID: 34829414 PMCID: PMC8625493 DOI: 10.3390/diagnostics11112067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/27/2021] [Accepted: 11/04/2021] [Indexed: 11/26/2022] Open
Abstract
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.
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Lee S, Namgoong JM, Kim Y, Cha J, Kim JK. Multimodal imaging of laser speckle contrast imaging combined with mosaic filter-based hyperspectral imaging for precise surgical guidance. IEEE Trans Biomed Eng 2021; 69:443-452. [PMID: 34260344 DOI: 10.1109/tbme.2021.3097122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To enable a real-time surgical guidance system that simultaneously monitors blood vessel perfusion, oxygen saturation, thrombosis, and tissue recovery by combining multiple optical imaging techniques into a single system: visible imaging, mosaic filter-based snapshot hyperspectral imaging (HSI), and laser speckle contrast imaging (LSCI). METHODS The multimodal optical imaging system was demonstrated by clamping blood vessels in the small intestines of rats to create areas of restricted blood flow. Subsequent tissue damage and regeneration were monitored during procedures. Using LSCI, vessel perfusion was measured, revealing the biological activity and survival of organ tissues. Blood oxygen saturation was monitored using HSI in the near-infrared region. Principal component analysis was used over the spectral dimension to identify an HSI wavelength combination optimized for hemodynamic biomarker visualization. HSI and LSCI were complimentary, identifying thrombus generation and tissue recovery, which was not possible in either modality alone. RESULTS AND CONCLUSION By analyzing multimodal tissue information from visible imaging, LSCI perfusion imaging, and HSI, a recovery prognosis could be determined based on the blood supply to the organ. The unique combination of the complementary imaging techniques into a single surgical microscope holds promise for improving the real-time determination of blood supply and tissue prognosis during surgery. SIGNIFICANCE Precise real-time monitoring for vascular anomalies promises to reduce the risk of organ damage in precise surgical operations such as tissue resection and transplantation. In addition, the convergence of label-free imaging technologies removes delays associated with the injection and diffusion of vascular monitoring dyes.
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