Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

Wu, Yue; Xu, Zhongyuan; Yang, Wenjian; Dong, Hao

doi:10.3389/fbioe.2022.906728

Cited by 10 publications

(8 citation statements)

References 120 publications

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“…We focus here on the applications to brain surgery. 48 A first HSI application to this field is to infer the brain tissue metabolic and hemodynamic signals, such as oxyhemoglobin, deoxyhemoglobin, and cytochrome c-oxidase, to study the functionality of the brain, diagnosing diseases, and for surgical assistance. [49][50][51][52][53] Our main focus here is however the use of HSI for tumor tissue identification.…”

Section: Hsi In Brain Cancer Surgerymentioning

confidence: 99%

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

Giannantonio¹,

Alperovich

Piercosimo

et al. 2023

Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis

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Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion. Currently, no real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. While marker-based methods exist for the high-grade glioma case, there is no convenient solution available for the low-grade case; thus, marker-free optical techniques represent an attractive option. Although RGB imaging is a standard tool in surgical microscopes, it does not contain sufficient information for tissue differentiation. We leverage the richer information from hyperspectral imaging (HSI), acquired with a snapscan camera in the 468 − 787 nm range, coupled to a surgical microscope, to build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance. However, the main limitation of the HSI snapscan camera is the image acquisition time, limiting its widespread deployment in the operation theater. Here, we investigate the effect of HSI channel reduction and pre-selection to scope the design space for the development of cheaper and faster sensors. Neural networks are used to identify the most important spectral channels for tumor tissue differentiation, optimizing the trade-off between the number of channels and precision to enable real-time intra-surgical application. We evaluate the performance of our method on a clinical dataset that was acquired during surgery on five patients. By demonstrating the possibility to efficiently detect low-grade glioma, these results can lead to better cancer resection demarcations, potentially improving treatment effectiveness and patient outcome.

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Section: Hsi In Brain Cancer Surgerymentioning

confidence: 99%

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

Giannantonio¹,

Alperovich

Piercosimo

et al. 2023

Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis

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“…This modality extends the advantages of spectroscopic analysis, by (i) looking at the optical signature of a tissue to extract quantitative information about its chemical composition with high spectral resolution and (ii) providing 2D optical image reconstruction of the interrogated brain tissue. HSI has the capacity to allow users to target and identify specific brain tissue biomarkers in neurosurgery, providing monitoring and assessment of the physiology and pathophysiology of the brain, as well as of the functional and structural status of cerebral tissue 7,8 .…”

Section: The Hyperprobe Solutionmentioning

confidence: 99%

HyperProbe consortium: innovate tumour neurosurgery with innovative photonic solutions

Giannoni,

Marradi,

Marchetti

et al. 2023

Diffuse Optical Spectroscopy and Imaging IX

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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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“…The additional spectral information provided can then be used to assign a ‘tissue/molecular label’ for each pixel by comparing the measured spectrum to already known spectra of different tissues/molecules. Thus, biomolecules, such as HbO 2 , Hb, water, and cytochrome c-oxidase (CCO) [ 93 ], can be resolved with a resolution of 100 μm. Hyperspectral cameras are usually placed at a distance of 25–30 cm above the skin region to be examined, leaving the skin surface and perfusion undisturbed due to their contactless character [ 94 ].…”

Section: Skin Microangiopathy and Foot Ulcersmentioning

confidence: 99%

Clinical and Translational Imaging and Sensing of Diabetic Microangiopathy: A Narrative Review

Fasoula,

Xie,

Katsouli

et al. 2023

JCDD

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Microvascular changes in diabetes affect the function of several critical organs, such as the kidneys, heart, brain, eye, and skin, among others. The possibility of detecting such changes early enough in order to take appropriate actions renders the development of appropriate tools and techniques an imperative need. To this end, several sensing and imaging techniques have been developed or employed in the assessment of microangiopathy in patients with diabetes. Herein, we present such techniques; we provide insights into their principles of operation while discussing the characteristics that make them appropriate for such use. Finally, apart from already established techniques, we present novel ones with great translational potential, such as optoacoustic technologies, which are expected to enter clinical practice in the foreseeable future.

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Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

Cited by 10 publications

References 120 publications

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

HyperProbe consortium: innovate tumour neurosurgery with innovative photonic solutions

Clinical and Translational Imaging and Sensing of Diabetic Microangiopathy: A Narrative Review

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