Evidence map›Paper›PMID 40483355›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Multi-volume rendering using depth buffers for surgical planning in virtual reality.

Balázs Faludi, Marek Żelechowski, Maria Licci, Norbert Zentai, Attill Saemann, Daniel Studer, Georg Rauter, Raphael Guzman, Carol Hasler, Gregory F Jost and 1 more

Abstract read
In one paragraph

Article in International journal of computer assisted radiology and surgery, 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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0citing papers 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

11 authors.

Balázs FaludiDepartment of Biomedical Engineering, University of Basel, Basel, Switzerland. balazs.faludi@unibas.ch.ORCID http://orcid.org/0009-0006-3780-8355
Marek ŻelechowskiDepartment of Biomedical Engineering, University of Basel, Basel, Switzerland.
Maria LicciNeurosurgery, University Children's Hospital of Basel, Basel, Switzerland.
Norbert ZentaiDepartment of Biomedical Engineering, University of Basel, Basel, Switzerland.
Attill SaemannNeurosurgery, University Hospital of Basel, Basel, Switzerland.
Daniel StuderNeurosurgery, University Children's Hospital of Basel, Basel, Switzerland.
Georg RauterDepartment of Biomedical Engineering, University of Basel, Basel, Switzerland.
Raphael GuzmanNeurosurgery, University Children's Hospital of Basel, Basel, Switzerland.
Carol HaslerNeurosurgery, University Children's Hospital of Basel, Basel, Switzerland.
Gregory F JostSpinale Chirurgie, Hospital Biel, Biel, Switzerland.
Philippe C CattinDepartment of Biomedical Engineering, University of Basel, Basel, Switzerland.

Funding

Werner Siemens-Stiftung MIRACLE project
6 · The paper itself

Abstract

purposePlanning highly complex surgeries in virtual reality (VR) provides a user-friendly and natural way to navigate volumetric medical data and can improve the sense of depth and scale. Using ray marching-based volume rendering to display the data has several benefits over traditional mesh-based rendering, such as offering a more accurate and detailed visualization without the need for prior segmentation and meshing. However, volume rendering can be difficult to extend to support multiple intersecting volumes in a scene while maintaining a high enough update rate for a comfortable user experience in VR.

methodsUpon loading a volume, a rough ad hoc segmentation is performed using a motion-tracked controller. The segmentation is not used to extract a surface mesh and does not need to precisely define the exact surfaces to be rendered, as it only serves to separate the volume into individual sub-volumes, which are rendered in multiple, consecutive volume rendering passes. For each pass, the ray lengths are written into the camera depth buffer at early ray termination and read in subsequent passes to ensure correct occlusion between individual volumes.

resultsWe evaluate the performance of the multi-volume renderer using three different use cases and corresponding datasets. We show that the presented approach can avoid dropped frames at the typical update rate of 90 frames per second of a desktop-based VR system and, therefore, provide a comfortable user experience even in the presence of more than twenty individual volumes.

conclusionOur proof-of-concept implementation shows the feasibility of VR-based surgical planning systems, which require dynamic and direct manipulation of the original volumetric data without sacrificing rendering performance and user experience.

Indexed as

Imaging, Three-DimensionalSurgery, Computer-AssistedVirtual RealityHumansUser-Computer InterfaceMulti-volume renderingSurgical planningVirtual reality

Identifiers

PMID40483355
PMCPMC12575470

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LicenceCC BY
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Registered trials

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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.