Volume 30 Issue 2 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 30 · NO. 2 | February 2025

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Special Section on Hyperspectral Imaging, Part 2 (3)

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Special Section on Hyperspectral Imaging, Part 2

Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications

Alankar Kotwal, Vishwanath Saragadam, Joshua Bernstock, Alfredo Sandoval, Ashok Veeraraghavan, Pablo Valdés

Journal of Biomedical Optics, Vol. 30, Issue 02, 023512, (November 2024) https://doi.org/10.1117/1.JBO.30.2.023512

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Significance

Accurate identification between pathologic (e.g., tumors) and healthy brain tissue is a critical need in neurosurgery. However, conventional surgical adjuncts have significant limitations toward achieving this goal (e.g., image guidance based on pre-operative imaging becomes inaccurate up to 3 cm as surgery proceeds). Hyperspectral imaging (HSI) has emerged as a potential powerful surgical adjunct to enable surgeons to accurately distinguish pathologic from normal tissues.

Aim

We review HSI techniques in neurosurgery; categorize, explain, and summarize their technical and clinical details; and present some promising directions for future work.

Approach

We performed a literature search on HSI methods in neurosurgery focusing on their hardware and implementation details; classification, estimation, and band selection methods; publicly available labeled and unlabeled data; image processing and augmented reality visualization systems; and clinical study conclusions.

Results

We present a detailed review of HSI results in neurosurgery with a discussion of over 25 imaging systems, 45 clinical studies, and 60 computational methods. We first provide a short overview of HSI and the main branches of neurosurgery. Then, we describe in detail the imaging systems, computational methods, and clinical results for HSI using reflectance or fluorescence. Clinical implementations of HSI yield promising results in estimating perfusion and mapping brain function, classifying tumors and healthy tissues (e.g., in fluorescence-guided tumor surgery, detecting infiltrating margins not visible with conventional systems), and detecting epileptogenic regions. Finally, we discuss the advantages and disadvantages of HSI approaches and interesting research directions as a means to encourage future development.

Conclusions

We describe a number of HSI applications across every major branch of neurosurgery. We believe these results demonstrate the potential of HSI as a powerful neurosurgical adjunct as more work continues to enable rapid acquisition with smaller footprints, greater spectral and spatial resolutions, and improved detection.

Digital instrument simulator to optimize the development of hyperspectral systems: application for intraoperative functional brain mapping

Charly Caredda, Frédéric Lange, Luca Giannoni, Ivan Ezhov, Thiébaud Picart, Jacques Guyotat, Ilias Tachtsidis, Bruno Montcel

Journal of Biomedical Optics, Vol. 30, Issue 02, 023513, (December 2024) https://doi.org/10.1117/1.JBO.30.2.023513

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Significance

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas can be assessed by monitoring cerebral hemodynamics and metabolism. Robust quantification of these biomarkers is complicated to perform during neurosurgery due to the critical context of the operating room. In actual devices, the inhomogeneities of the optical properties of the exposed brain cortex are poorly taken into consideration, which introduce quantification errors of biomarkers of brain functionality. Moreover, the best choice of spectral configuration is still based on an empirical approach.

Aim

We propose a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This simulator can provide realistic modeling of the cerebral cortex and the identification of the optimal wavelengths to monitor cerebral hemodynamics (oxygenated HbO2 and deoxygenated hemoglobin Hb) and metabolism (oxidized state of cytochromes b and c and cytochrome-c-oxidase oxCytb, oxCytc, and oxCCO).

Approach

The digital instrument simulator is computed with white Monte Carlo simulations of a volume created from a real image of exposed cortex. We developed an optimization procedure based on a genetic algorithm to identify the best wavelength combinations in the visible and near-infrared range to quantify concentration changes in HbO2, Hb, oxCCO, and the oxidized state of cytochrome b and c (oxCytb and oxCytc).

Results

The digital instrument allows the modeling of intensity maps collected by a camera sensor as well as images of path length to take into account the inhomogeneities of the optical properties. The optimization procedure helps to identify the best wavelength combination of 18 wavelengths that reduces the quantification errors in HbO2, Hb, and oxCCO by 47%, 57%, and 57%, respectively, compared with the gold standard of 121 wavelengths between 780 and 900 nm. The optimization procedure does not help to resolve changes in cytochrome b and c in a significant way but helps to better resolve oxCCO changes.

Conclusions

We proposed a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This digital instrument simulator and this optimization framework could be used to optimize the design of hyperspectral imaging devices.

Personal identification using a cross-sectional hyperspectral image of a hand

Takashi Suzuki

Journal of Biomedical Optics, Vol. 30, Issue 02, 023514, (December 2024) https://doi.org/10.1117/1.JBO.30.2.023514

TOPICS: Hyperspectral imaging, Image processing, Feature extraction, Biometrics, Principal component analysis, Light sources, Cameras, Veins, Skin, Optical coherence tomography

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