Evidence map›Paper›PMID 40469942›Full record

ArticleBrain & spine2025

Fusion of 3D photorealistic lateral-to-medial brain white matter dissection and diffusion tensor imaging for dynamic visualization of key fiber tracts.

Daniele Armocida, Toma Spiriev, Milko Milev, Francesco Carbone, Michael Wolf-Vollenbröker, Michael Sabel, Marion Rapp, Jan Frederick Cornelius

Abstract read
In one paragraph

Article in Brain & spine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Daniele ArmocidaUniversità degli studi di Torino, Neuroscience Department "Rita Levi Montalcini", Neurosurgery Unit, Via Cherasco 15, 10126, Turin, TO, Italy.
Toma SpirievDepartment of Neurosurgery, Acibadem City Clinic University Hospital Tokuda, Sofia, Bulgaria.
Milko MilevDepartment of Neurosurgery, Acibadem City Clinic University Hospital Tokuda, Sofia, Bulgaria.
Francesco CarboneDepartment of Neurosurgery, Städtisches Klinikum Karlsruhe - Karlsruher Neurozentrum, Moltkestraße 90, 76133, Karlsruhe, DE, Germany.
Michael Wolf-VollenbrökerInstitute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany.
Michael SabelDepartment of Neurosurgery, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany.
Marion RappDepartment of Neurosurgery, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany.
Jan Frederick CorneliusDepartment of Neurosurgery, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Knowledge of fiber tract organization is vital for understanding brain connectivity and function. Integrating traditional anatomical dissection with advanced imaging methods like diffusion tensor imaging (DTI) and 3D-photographic surface scanning (photogrammetry) provides detailed visualization of white matter (WM) pathways. However, both modalities are typically presented as separate 3D-models. This study presents photorealistic 3D photogrammetric models of lateral-to-medial dissections of clinically relevant fiber tracts combined with 3D DTI fiber tract data. Research question: Can the integration of photorealistic 3D photogrammetric models and DTI data enhance the understanding of WM fiber tract anatomy and improve its educational and neurosurgical applications? Material and methods: One brain fixed using Klingler's method was dissected. Stepwise identification of the arcuate, superior longitudinal, inferior fronto-occipital fasciculi, uncinate fascicle, anterior commissure, Mayer's loop, and the internal capsule was documented with detailed 3D-photogrammetric models generated at each dissection step. Relevant fiber tracts were also generated from population-averaged DTI data using open-access databases and software. Results: Seven photorealistic models and five integrated DTI-photogrammetry fusion 3D models were produced. Data fusion in 3D software enabled integrated models showing spatial relationships and boundaries between fiber tracts, adjustable by viewing angle and opacity changes. Combining photogrammetry with DTI-segmentation added educational value for understanding 3D-localization and fiber tract trajectories. Discussion and conclusion: The photogrammetric models and DTI data presented enhance the comprehension of critical WM fiber tracts, benefiting education and surgical planning. Customizable views allow straightforward comparisons between Klingler dissection models and fully explorable DTI models, providing a comprehensive understanding of WM anatomy.

Indexed as

NeuroanatomyNeurosurgeryPhotogrammetry. DTIWhite matter fibers

Identifiers

PMID40469942
PMCPMC12136766

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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.