Evidence map›Paper›PMID 39544341›Full record

ReviewJournal of biomedical optics2025

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

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

Abstract readReview
In one paragraph

Review in Journal of biomedical optics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Alankar KotwalUniversity of Texas Medical Branch, Department of Neurosurgery, Galveston, Texas, United States.ORCID 0000-0003-1866-9346
Vishwanath SaragadamUniversity of California Riverside, Department of Electrical and Computer Engineering, Riverside, California, United States.ORCID 0000-0001-8028-7520
Joshua D BernstockBrigham and Women's Hospital, Harvard Medical School, Department of Neurosurgery, Boston, Massachusetts, United States.ORCID 0000-0002-7814-3867
Alfredo SandovalUniversity of Texas Medical Branch, Department of Neurosurgery, Galveston, Texas, United States.
Ashok VeeraraghavanRice University, Department of Electrical and Computer Engineering, Houston, Texas, United States.
Pablo A ValdésUniversity of Texas Medical Branch, Department of Neurosurgery, Galveston, Texas, United States.ORCID 0000-0002-6788-8003

Funding

White matter tract-specific near-infrared fluorescence probes for in vivo fluorescence guided white matter tractographyR21EB034033 · NIBIB · UNIVERSITY OF TEXAS MED BR GALVESTON · PI VALDES QUEVEDO, PABLO ANDRES · 2022 to 2024
$657k
NIBIB NIH HHS R21 EB034033
6 · The paper itself

Abstract

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.

Indexed as

Hyperspectral ImagingNeurosurgical ProceduresBrainBrain NeoplasmsHumansImage Processing, Computer-AssistedSurgery, Computer-Assistedbrain tumorsfluorescence-guided surgeryhyperspectral imagingneurosurgery

Identifiers

PMID39544341
PMCPMC11559659

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.