Evidence map›Paper›PMID 42143636›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Markerless 3D ultrasound reconstruction for augmented reality assisted minimally invasive spine surgery.

Ruixuan Li, Yuyu Cai, Ayoob Davoodi, Gianni Borghesan, Carlos Rodriguez-Guerrero, Emmanuel Vander Poorten

Abstract read
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Article in International journal of computer assisted radiology and surgery, 2026. 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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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ruixuan LiDepartment of Mechanical Engineering, Robot-Assisted Surgery Group, KU Leuven, Leuven, Belgium. ruixuan.li@kuleuven.be.ORCID http://orcid.org/0000-0003-2882-8398
Yuyu CaiDepartment of Mechanical Engineering, Robot-Assisted Surgery Group, KU Leuven, Leuven, Belgium.
Ayoob DavoodiDepartment of Mechanical Engineering, Robot-Assisted Surgery Group, KU Leuven, Leuven, Belgium.
Gianni BorghesanDepartment of Mechanical Engineering, Robot-Assisted Surgery Group, KU Leuven, Leuven, Belgium.
Carlos Rodriguez-GuerreroDepartment of Mechanical Engineering, Robot-Assisted Surgery Group, KU Leuven, Leuven, Belgium.
Emmanuel Vander PoortenDepartment of Mechanical Engineering, Robot-Assisted Surgery Group, KU Leuven, Leuven, Belgium.

Funding

Fonds Wetenschappelijk Onderzoek 12A8N26N
6 · The paper itself

Abstract

purposeMinimally invasive spine surgery (MISS) requires 3D navigation of bony anatomy, yet conventional navigation is limited by radiation or line-of-sight-dependent external trackers. This study developed a markerless augmented reality ultrasound (AR-US) system to provide radiation-free 3D visualization while eliminating tracker occlusion.

methodsAn integrated pipeline was developed to track a handheld US probe using a head-mounted display (HMD). The probe pose was estimated using FoundationPose during scanning. To mitigate inherent noise in the raw estimates, an Extended Kalman Filter was implemented for refinement. Spatially tracked 2D US images were subsequently used to reconstruct a 3D volume, which was rendered on the HMD.

resultsThe proposed system reconstructed 3D volumes with an average representation error of

conclusionThis work demonstrates the feasibility of a markerless AR-US system for coarse navigation in MISS. The presented approach offers a streamlined and intuitive solution, providing clinicians with 3D anatomy for navigating instrumentation..

Indexed as

Augmented RealityImaging, Three-DimensionalMinimally Invasive Surgical ProceduresSpineSurgery, Computer-AssistedHumansPhantoms, ImagingUltrasonography3D reconstructionAugmented realityMinimally invasive surgeryUltrasound navigation

Identifiers

PMID42143636

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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.