Evidence map›Paper›PMID 41254277›Full record

ArticleScientific reports2025

VR-prep improves visualization of medical data in augmented reality on smartphones.

A M C Böhner, A M Jacob, M Bahaaeldin, L Oelmeier, A Isaak, C C Pieper, J A Luetkens, D Kuetting

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

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

8 authors.

A M C Böhner *Clinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany. Alexander.Boehner@ukbonn.de.
A M Jacob *Clinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
M BahaaeldinClinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
L OelmeierClinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
A IsaakClinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
C C PieperClinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
J A LuetkensClinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
D KuettingClinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Relaying complex anatomical information between clinicians and to patients is challenging in medical routine and might be improved via direct 3D visualization of image series in augmented reality (AR) in portable devices. We devised a workflow for displaying medical imaging data in augmented reality on smartphones, relying solely on open-source products: 'Medical Imaging XR' (MIXR) application, '3D Slicer' software, and 'Fiji'. We designed a pipeline for image transformation and data harmonization (VR-prep) to expedite and optimize the 3D rendering of DICOM series in MIXR. VR-prep significantly reduced the size of image files to be uploaded to MIXR, without sacrificing quality and readability of images. Due to reduced file size, upload and download times onto the mobile device were significantly lower. Our workflow is suitable for CT, MRI, US and PET-CT, whereas MIXR alone only permits CT data. VR-prep is user-friendly and allows for broader integration of medical imaging data sets in AR, to facilitate clinician to clinician and clinician to patient communication. VR-prep relies exclusively in open-source software making it viable to adopt in a broad range of applications from clinical decision-making, patient communication, and medical education.

Indexed as

Augmented RealityImaging, Three-DimensionalSmartphoneHumansMagnetic Resonance ImagingMobile ApplicationsSoftwareWorkflowAugmented realityCTMedical imagingMRISmartphones

Identifiers

PMID41254277
PMCPMC12627710

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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.